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Shi J, Chen Y, Heidari AA, Cai Z, Chen H, Chen Y, Liang G. Environment random interaction of rime optimization with Nelder-Mead simplex for parameter estimation of photovoltaic models. Sci Rep 2024; 14:15701. [PMID: 38977743 PMCID: PMC11231246 DOI: 10.1038/s41598-024-65292-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 06/18/2024] [Indexed: 07/10/2024] Open
Abstract
As countries attach importance to environmental protection, clean energy has become a hot topic. Among them, solar energy, as one of the efficient and easily accessible clean energy sources, has received widespread attention. An essential component in converting solar energy into electricity are solar cells. However, a major optimization difficulty remains in precisely and effectively calculating the parameters of photovoltaic (PV) models. In this regard, this study introduces an improved rime optimization algorithm (RIME), namely ERINMRIME, which integrates the Nelder-Mead simplex (NMs) with the environment random interaction (ERI) strategy. In the later phases of ERINMRIME, the ERI strategy serves as a complementary mechanism for augmenting the solution space exploration ability of the agent. By facilitating external interactions, this method improves the algorithm's efficacy in conducting a global search by keeping it from becoming stuck in local optima. Moreover, by incorporating NMs, ERINMRIME enhances its ability to do local searches, leading to improved space exploration. To evaluate ERINMRIME's optimization performance on PV models, this study conducted experiments on four different models: the single diode model (SDM), the double diode model (DDM), the three-diode model (TDM), and the photovoltaic (PV) module model. The experimental results show that ERINMRIME reduces root mean square error for SDM, DDM, TDM, and PV module models by 46.23%, 59.32%, 61.49%, and 23.95%, respectively, compared with the original RIME. Furthermore, this study compared ERINMRIME with nine improved classical algorithms. The results show that ERINMRIME is a remarkable competitor. Ultimately, this study evaluated the performance of ERINMRIME across three distinct commercial PV models, while considering varying irradiation and temperature conditions. The performance of ERINMRIME is superior to existing similar algorithms in different irradiation and temperature conditions. Therefore, ERINMRIME is an algorithm with great potential in identifying and recognizing unknown parameters of PV models.
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Affiliation(s)
- Jinge Shi
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou, 325035, China
| | - Yi Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou, 325035, China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Zhennao Cai
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou, 325035, China
| | - Huiling Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou, 325035, China.
| | - Yipeng Chen
- Center of AI Technology Application R&D, Wenzhou Polytechnic, Wenzhou, 325035, China
| | - Guoxi Liang
- Department of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, 325035, China.
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2
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Fisher A, Tan X, Billah M, Lingras P, Huang J, Mago V. PAAD: Panelization algorithm for architectural designs. PLoS One 2024; 19:e0303646. [PMID: 38861492 PMCID: PMC11166312 DOI: 10.1371/journal.pone.0303646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 04/29/2024] [Indexed: 06/13/2024] Open
Abstract
Due to the competitive nature of the construction industry, the efficiency of requirement analysis is important in enhancing client satisfaction and a company's reputation. For example, determining the optimal configuration of panels (generally called panelization) that form the structure of a building is one aspect of cost estimation. However, existing methods typically rely on rule-based approaches that may lead to suboptimal material usage, particularly in complex designs featuring angled walls and openings. Such inefficiency can increase costs and environmental impact due to unnecessary material waste. To address these challenges, this research proposes a Panelization Algorithm for Architectural Designs, referred to as PAAD, which utilizes a genetic evolutionary strategy built on the 2D bin packing problem. This method is designed to balance between strict adherence to manufacturing constraints and the objective of optimizing material usage. PAAD starts with multiple potential solutions within the predefined problem space, facilitating dynamic exploration of panel configurations. It approaches structural rules as flexible constraints, making necessary corrections in post-processing, and through iterative developments, the algorithm refines panel sets to minimize material use. The methodology is validated through an analysis against an industry implementation and expert-derived solutions, highlighting PAAD's ability to surpass existing results and reduce the need for manual corrections. Additionally, to motivate future research, a synthetic data generator, the architectural drawing encodings used, and a preliminary interface are also introduced. This not only highlights the algorithm's practical applicability but also encourages its use in real-world scenarios.
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Affiliation(s)
- Andrew Fisher
- Department of Mathematics and Computing Science, Saint Mary’s University, Halifax, Nova Scotia, Canada
| | - Xing Tan
- Department of Computer Science, Lakehead University, Thunder Bay, Ontario, Canada
| | - Muntasir Billah
- Department of Civil Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Pawan Lingras
- Department of Mathematics and Computing Science, Saint Mary’s University, Halifax, Nova Scotia, Canada
| | - Jimmy Huang
- School of Information Technology, York University, Toronto, Ontario, Canada
| | - Vijay Mago
- School of Health Policy and Management, York University, Toronto, Ontario, Canada
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3
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Grelier G, Casal MA, Torrente-Patiño A, Romero J. Image sequence sorting algorithm for commercial tasks. Front Artif Intell 2024; 7:1382566. [PMID: 38742122 PMCID: PMC11089148 DOI: 10.3389/frai.2024.1382566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 04/08/2024] [Indexed: 05/16/2024] Open
Abstract
Introduction The sorting of sequences of images is crucial for augmenting user engagement in various virtual commercial platforms, particularly within the real estate sector. A coherent sequence of images respecting room type categorization significantly enhances the intuitiveness and seamless navigation of potential customers through listings. Methods This study methodically formalizes the challenge of image sequence sorting and expands its applicability by framing it as an ordering problem. The complexity lies in devising a universally applicable solution due to computational demands and impracticality of exhaustive searches for optimal sequencing. To tackle this, our proposed algorithm employs a shortest path methodology grounded in semantic similarity between images. Tailored specifically for the real estate sector, it evaluates diverse similarity metrics to efficiently arrange images. Additionally, we introduce a genetic algorithm to optimize the selection of semantic features considered by the algorithm, further enhancing its effectiveness. Results Empirical evidence from our dataset demonstrates the efficacy of the proposed methodology. It successfully organizes images in an optimal sequence across 85% of the listings, showcasing its effectiveness in enhancing user experience in virtual commercial platforms, particularly in real estate. Conclusion This study presents a novel approach to sorting sequences of images in virtual commercial platforms, particularly beneficial for the real estate sector. The proposed algorithm effectively enhances user engagement by providing more intuitive and visually coherent image arrangements.
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Affiliation(s)
| | - Miguel A. Casal
- RNASA Lab-IMEDIR, Department of Computer Science and Information Technologies, Faculty of Communication Science, University of A Coruña, A Coruña, Spain
| | - Alvaro Torrente-Patiño
- PhotoILike, Bergondo, Spain
- RNASA Lab-IMEDIR, Department of Computer Science and Information Technologies, Faculty of Communication Science, University of A Coruña, A Coruña, Spain
| | - Juan Romero
- PhotoILike, Bergondo, Spain
- RNASA Lab-IMEDIR, Department of Computer Science and Information Technologies, Faculty of Communication Science, University of A Coruña, A Coruña, Spain
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Scalzitti N, Miralavy I, Korenchan DE, Farrar CT, Gilad AA, Banzhaf W. Computational peptide discovery with a genetic programming approach. J Comput Aided Mol Des 2024; 38:17. [PMID: 38570405 DOI: 10.1007/s10822-024-00558-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/07/2024] [Indexed: 04/05/2024]
Abstract
The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search spaces that need to be considered. In silico methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and can facilitate the discovery of new peptides. This study presents the development and use of a new variant of the genetic-programming-based POET algorithm, called POETRegex , where individuals are represented by a list of regular expressions. This algorithm was trained on a small curated dataset and employed to generate new peptides improving the sensitivity of peptides in magnetic resonance imaging with chemical exchange saturation transfer (CEST). The resulting model achieves a performance gain of 20% over the initial POET models and is able to predict a candidate peptide with a 58% performance increase compared to the gold-standard peptide. By combining the power of genetic programming with the flexibility of regular expressions, new peptide targets were identified that improve the sensitivity of detection by CEST. This approach provides a promising research direction for the efficient identification of peptides with therapeutic or diagnostic potential.
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Affiliation(s)
- Nicolas Scalzitti
- BEACON Center of Evolution in Action, Michigan State University, East Lansing, MI, USA
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Iliya Miralavy
- BEACON Center of Evolution in Action, Michigan State University, East Lansing, MI, USA
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - David E Korenchan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Christian T Farrar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Assaf A Gilad
- BEACON Center of Evolution in Action, Michigan State University, East Lansing, MI, USA.
- Department of Chemical Engineering, Michigan State University, East Lansing, MI, USA.
- Department of Radiology, Michigan State University, East Lansing, MI, USA.
| | - Wolfgang Banzhaf
- BEACON Center of Evolution in Action, Michigan State University, East Lansing, MI, USA.
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA.
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Das S, Merz KM. Molecular Gas-Phase Conformational Ensembles. J Chem Inf Model 2024; 64:749-760. [PMID: 38206321 DOI: 10.1021/acs.jcim.3c01309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.
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Affiliation(s)
- Susanta Das
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
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Suhail K, Brindha D. Microscopic urinary particle detection by different YOLOv5 models with evolutionary genetic algorithm based hyperparameter optimization. Comput Biol Med 2024; 169:107895. [PMID: 38183704 DOI: 10.1016/j.compbiomed.2023.107895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 12/07/2023] [Accepted: 12/22/2023] [Indexed: 01/08/2024]
Abstract
The diagnosis of kidney disease often involves analysing urine sediment particles. Traditionally, urinalysis was performed manually by collecting urine samples and using a centrifuge, which was prone to manual errors and relied on labour-intensive processes. Automated urine sediment microscopy, based on machine learning models, requires segmentation and feature extraction, which can hinder model performance due to intrinsic characteristics of microscopic images. Deep learning models based on convolutional neural networks (CNNs) often rely on a large number of manually annotated data, making the system computationally complex. This study propose an advanced deep learning model based on YOLOv5, which offers faster performance and requires comparatively less data. The proposed model used five variants of the YOLOv5 model (YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) to detect six categories of urine particles (erythrocyte, leukocyte, crystals, cast, mycete, epithelial cells) from microscopic urine sediment images. The dataset involved 5376 images of urine sediments with 6 particles. There are 30 sets of hyperparamreteres are employed in the YOLOv5 model. To optimize the hyperparameters and fine-tune with the urine sediment dataset and for training each model, the system employed a genetic algorithm (GA) based on evolutionary principles named as Evolutionary Genetic Algorithm (EGA). Among the six categories of detected particles mycete achieved maximum performance with a mAP of 97.6 % and crystals achieved minimum performance with a mAP of 81.7 % with YOLOv5x model compared to other particles. To optimize the hyperparameters for training each model, the system employed a genetic algorithm (GA) based on evolutionary principles named as Evolutionary Genetic Algorithm (EGA). Among all the models, YOLOv5l and YOLOv5x performed the best. YOLOv5l achieved a mean average precision (mAP) of 85.8 % while YOLOv5x achieved a mAP of 85.4 % at an IoU threshold of 0.5. The detection speed per image was 23.4 ms for YOLOv5l and 28.4 ms for YOLOv5x. The proposed method developed a faster and better automated microscopic model using advanced deep learning techniques to detect urinary particles from microscopic urine sediment images for kidney disease identification. The method demonstrated strong performance in urinalysis.
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Affiliation(s)
- K Suhail
- Department of Biomedical Engineering, PSG College of Technology, Coimbatore, 641004, India.
| | - D Brindha
- Department of Biomedical Engineering, PSG College of Technology, Coimbatore, 641004, India.
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Wu Z, Tao W, Lv N, Zhao H. Optimization of parameters for a fringe projection measurement system by use of an improved differential evolution method. OPTICS EXPRESS 2024; 32:3632-3646. [PMID: 38297580 DOI: 10.1364/oe.507602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 12/04/2023] [Indexed: 02/02/2024]
Abstract
Fringe projection 3D measurement is widely used for object surface reconstruction. While improving measurement accuracy is a crucial task. Measurement accuracy is profoundly affected by various optical structural parameters. However, the current practice of system construction lacks theoretical guidelines and often relies on the experience of the operator, inevitably leading to unpredictable error. This paper investigates a theoretical optimization model and proposes an automatic optimization method for qualitatively determining the multiple optimal optical structural parameters in fringe projection measurement system. The aim is to enhance measurement accuracy conducting a rational comprehensive optimal structural parameters design prior to the system construction. Firstly, the mathematical model of the measurement system is established based on the principle of optical triangulation, and the phase sensitivity criterion is defined as the optimization norm. Within the full measurement range, the optimization merit function is formulated by combing three positions: the center position, the left and right boundary of the CCD. The imaging effectiveness criteria and sensor geometric dimensions are taken into account as the constraint boundaries. Subsequently, a combined improved differential evolution and Levy flight optimization algorithm is applied to search for the optimal parameters. The optimal structural parameters of the system were designed based on the optimization process. Experimental results validated the improvement in measurement accuracy achieved by the optimized structural parameters.
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Zhang Q, Hu J, Liu Z, Duan J. Multi-objective optimization of dual resource integrated scheduling problem of production equipment and RGVs considering conflict-free routing. PLoS One 2024; 19:e0297139. [PMID: 38277415 PMCID: PMC10834064 DOI: 10.1371/journal.pone.0297139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 12/28/2023] [Indexed: 01/28/2024] Open
Abstract
In flexible job shop scheduling problem (FJSP), the collision of bidirectional rail guided vehicles (RGVs) directly affects RGVs scheduling, and it is closely coupled with the allocation of production equipment, which directly affects the production efficiency. In this problem, taking minimizing the maximum completion time of RGVs and minimizing the maximum completion time of products as multi-objectives a dual-resource integrated scheduling model of production equipment and RGVs considering conflict-free routing problem (CFRP) is proposed. To solve the model, a multi-objective improved discrete grey wolf optimizer (MOID-GWO) is designed. Further, the performance of popular multi-objective evolutionary algorithms (MOEAs) such as NSGA-Ⅱ, SPEA2 and MOPSO are selected for comparative test. The results show that, among 42 instances of different scales designed, 37, 34 and 28 instances in MOID-GWO are superior to the comparison algorithms in metrics of generational distance (GD), inverted GD (IGD) and Spread, respectively. Moreover, in metric of Convergence and Diversity (CD), the Pareto frontier (PF) obtained by MOID-GWO is closer to the optimal solution. Finally, taking the production process of a construction machinery equipment component as an example, the validity and feasibility of the model and algorithm are verified.
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Affiliation(s)
- Qinglei Zhang
- China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai, China
- Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, China
| | - Jing Hu
- Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, China
| | - Zhen Liu
- Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, China
| | - Jianguo Duan
- China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai, China
- Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, China
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P E, S K, Sagayam KM, J A. An automated cervical cancer diagnosis using genetic algorithm and CANFIS approaches. Technol Health Care 2024; 32:2193-2209. [PMID: 38251073 DOI: 10.3233/thc-230926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
BACKGROUND Cervical malignancy is considered among the most perilous cancers affecting women in numerous East African and South Asian nations, both in terms of its prevalence and fatality rates. OBJECTIVE This research aims to propose an efficient automated system for the segmentation of cancerous regions in cervical images. METHODS The proposed techniques encompass preprocessing, feature extraction with an optimized feature set, classification, and segmentation. The original cervical image undergoes smoothing using the Gaussian Filter technique, followed by the extraction of Local Binary Pattern (LBP) and Grey Level Co-occurrence Matrix (GLCM) features from the enhanced cervical images. LBP features capture pixel relationships within a mask window, while GLCM features quantify energy metrics across all pixels in the images. These features serve to distinguish normal cervical images from abnormal ones. The extracted features are optimized using Genetic Algorithm (GA) as an optimization method, and the optimized sets of features are classified using the Co-Active Adaptive Neuro-Fuzzy Inference System (CANFIS) classification method. Subsequently, a morphological segmentation technique is employed to categorize irregular cervical images, identifying and segmenting malignant regions within them. RESULTS The proposed approach achieved a sensitivity of 99.09%, specificity of 99.39%, and accuracy of 99.36%. CONCLUSION The proposed approach demonstrated superior performance compared to state-of-the-art techniques, and the results have been validated by expert radiologists.
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Affiliation(s)
- Elayaraja P
- Department of Electronics and Communication Engineering, Kongunadu College of Engineering and Technology, Trichy, India
| | - Kumarganesh S
- Department of Electronics and Communication Engineering, Knowledge Institute of Technology, Salem, India
| | - K Martin Sagayam
- Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
| | - Andrew J
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
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Angelo JS, Guedes IA, Barbosa HJC, Dardenne LE. Multi-and many-objective optimization: present and future in de novo drug design. Front Chem 2023; 11:1288626. [PMID: 38192501 PMCID: PMC10773868 DOI: 10.3389/fchem.2023.1288626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 11/27/2023] [Indexed: 01/10/2024] Open
Abstract
de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting objectives. Since several desired properties can be considered in the optimization process, dnDD is naturally categorized as a many-objective optimization problem (ManyOOP), where more than three objectives must be simultaneously optimized. However, a large number of objectives typically pose several challenges that affect the choice and the design of optimization methodologies. Herein, we cover the application of multi- and many-objective optimization methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyze how molecular properties used in the optimization process are applied as either objectives or constraints to the problem. Finally, we discuss future research in many-objective optimization for dnDD, highlighting two important possible impacts: i) its integration with the development of multi-target approaches to accelerate the discovery of innovative and more efficacious drug therapies and ii) its role as a catalyst for new developments in more fundamental and general methodological frameworks in the field.
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Affiliation(s)
| | | | | | - Laurent E. Dardenne
- Coordenação de Modelagem Computacional, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
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11
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de Oliveira Neto GC, de Araujo SA, Gomes RA, Alliprandini DH, Flausino FR, Amorim M. Simulation of Electronic Waste Reverse Chains for the Sao Paulo Circular Economy: An Artificial Intelligence-Based Approach for Economic and Environmental Optimizations. SENSORS (BASEL, SWITZERLAND) 2023; 23:9046. [PMID: 38005434 PMCID: PMC10674985 DOI: 10.3390/s23229046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/18/2023] [Accepted: 08/22/2023] [Indexed: 11/26/2023]
Abstract
The objective of this study was to apply simulation and genetic algorithms for the economic and environmental optimization of the reverse network (manufacturers, waste managers, and recyclers in Sao Paulo, Brazil) of waste from electrical and electronic equipment (WEEE) to promote the circular economy. For the economic evaluation, the reduction in fuel, drivers, insurance, depreciation, maintenance, and charges was considered. For the environmental evaluation, the impact of abiotic, biotic, water, land, air, and greenhouse gases was measured. It was concluded that the optimized structure of the WEEE reverse chains for Sao Paulo, Brazil provided a reduction in the number of collections, thus making the most of cubage. It also generated economic and environmental gains, contributing to the strategic actions of the circular economy. Therefore, the proposed approach is replicable in organizational practice, which is mainly required to meet the 2030 agenda of reducing the carbon footprint generated by transport in large cities. Thus, this study can guide companies in structuring the reverse WEEE chains in Sao Paulo, Brazil, and other states and countries for economic and environmental optimization, which is an aspect of great relevance considering the exponential generation of WEEE.
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Affiliation(s)
- Geraldo Cardoso de Oliveira Neto
- Business Administration and Industrial Engineering Post-Graduation Program, FEI University, Sao Paulo 01525-000, Brazil; (G.C.d.O.N.); (D.H.A.); (F.R.F.)
| | - Sidnei Alves de Araujo
- Informatics and Knowledge Management Post-Graduation Program, Universidade Nove de Julho—UNINOVE, Sao Paulo 01525-000, Brazil; (S.A.d.A.); (R.A.G.)
| | - Robson Aparecido Gomes
- Informatics and Knowledge Management Post-Graduation Program, Universidade Nove de Julho—UNINOVE, Sao Paulo 01525-000, Brazil; (S.A.d.A.); (R.A.G.)
| | - Dario Henrique Alliprandini
- Business Administration and Industrial Engineering Post-Graduation Program, FEI University, Sao Paulo 01525-000, Brazil; (G.C.d.O.N.); (D.H.A.); (F.R.F.)
| | - Fabio Richard Flausino
- Business Administration and Industrial Engineering Post-Graduation Program, FEI University, Sao Paulo 01525-000, Brazil; (G.C.d.O.N.); (D.H.A.); (F.R.F.)
| | - Marlene Amorim
- Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT) and GOVCOPP, University of Aveiro, 3810-193 Aveiro, Portugal
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Brückerhoff-Plückelmann F, Bente I, Becker M, Vollmar N, Farmakidis N, Lomonte E, Lenzini F, Wright CD, Bhaskaran H, Salinga M, Risse B, Pernice WHP. Event-driven adaptive optical neural network. SCIENCE ADVANCES 2023; 9:eadi9127. [PMID: 37862413 PMCID: PMC10588940 DOI: 10.1126/sciadv.adi9127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 09/19/2023] [Indexed: 10/22/2023]
Abstract
We present an adaptive optical neural network based on a large-scale event-driven architecture. In addition to changing the synaptic weights (synaptic plasticity), the optical neural network's structure can also be reconfigured enabling various functionalities (structural plasticity). Key building blocks are wavelength-addressable artificial neurons with embedded phase-change materials that implement nonlinear activation functions and nonvolatile memory. Using multimode focusing, the activation function features both excitatory and inhibitory responses and shows a reversible switching contrast of 3.2 decibels. We train the neural network to distinguish between English and German text samples via an evolutionary algorithm. We investigate both the synaptic and structural plasticity during the training process. On the basis of this concept, we realize a large-scale network consisting of 736 subnetworks with 16 phase-change material neurons each. Overall, 8398 neurons are functional, highlighting the scalability of the photonic architecture.
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Affiliation(s)
| | - Ivonne Bente
- Physical Institute, University of Münster, Heisenbergstraße 11, 48149 Münster, Germany
| | - Marlon Becker
- Institute for Geoinformatics, University of Münster, Heisenbergstraße 2, 48149 Münster, Germany
| | - Niklas Vollmar
- Institute of Materials Physics, University of Münster, Wilhelm-Klemm-Straße 10, 48149 Münster, Germany
| | - Nikolaos Farmakidis
- Department of Material, University of Oxford, Parks Road, Oxford OX1 3PH, UK
| | - Emma Lomonte
- Physical Institute, University of Münster, Heisenbergstraße 11, 48149 Münster, Germany
| | - Francesco Lenzini
- Physical Institute, University of Münster, Heisenbergstraße 11, 48149 Münster, Germany
| | - C. David Wright
- Department of Engineering, University of Exeter, North Park Road, Exeter EX4 4QF, UK
| | - Harish Bhaskaran
- Department of Material, University of Oxford, Parks Road, Oxford OX1 3PH, UK
| | - Martin Salinga
- Institute of Materials Physics, University of Münster, Wilhelm-Klemm-Straße 10, 48149 Münster, Germany
| | - Benjamin Risse
- Institute for Geoinformatics, University of Münster, Heisenbergstraße 2, 48149 Münster, Germany
| | - Wolfram H. P. Pernice
- Physical Institute, University of Münster, Heisenbergstraße 11, 48149 Münster, Germany
- Kirchhoff-Institute for Physics, University of Heidelberg, Im Neuenheimer Feld 227, 69120 Heidelberg, Germany
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Zameer A, Naz S, Raja MAZ, Hafeez J, Ali N. Neuro-Evolutionary Framework for Design Optimization of Two-Phase Transducer with Genetic Algorithms. MICROMACHINES 2023; 14:1677. [PMID: 37763840 PMCID: PMC10535456 DOI: 10.3390/mi14091677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/21/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023]
Abstract
Multilayer piezocomposite transducers are widely used in many applications where broad bandwidth is required for tracking and detection purposes. However, it is difficult to operate these multilayer transducers efficiently under frequencies of 100 kHz. Therefore, this work presents the modeling and optimization of a five-layer piezocomposite transducer with ten variables of nonuniform layer thicknesses and different volume fractions by exploiting the strength of the genetic algorithm (GA) with a one-dimensional model (ODM). The ODM executes matrix manipulation by resolving wave equations and produces mechanical output in the form of pressure and electrical impedance. The product of gain and bandwidth is the required function to be maximized in this multi-objective and multivariate optimization problem, which is a challenging task having ten variables. Converting it into the minimization problem, the reciprocal of the gain-bandwidth product is considered. The total thickness is adjusted to keep the central frequency at approximately 50-60 kHz. Piezocomposite transducers with three active materials, PZT5h, PZT4d, PMN-PT, and CY1301 polymer, as passive materials were designed, simulated, and statistically evaluated. The results show significant improvement in gain bandwidth compared to previous existing techniques.
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Affiliation(s)
- Aneela Zameer
- Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan
| | - Sidra Naz
- Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan
| | - Muhammad Asif Zahoor Raja
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
| | - Jehanzaib Hafeez
- Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan
| | - Nasir Ali
- Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan
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14
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Qiao Y, Lv N, Jia B. Multiview intelligent networking based on the genetic evolution algorithm for precise 3D measurements. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:14260-14280. [PMID: 37679135 DOI: 10.3934/mbe.2023638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
The use of multi-visual network 3D measurements is increasing; however, finding ways to apply low-cost industrial cameras to achieve intelligent networking and efficient measurement is a key problem that has not been fully solved. In this paper, the multivision stereo vision 3D measurement principle and multivision networking process constraints are analyzed in depth, and an intelligent networking method based on the genetic evolution algorithm (GEA) is proposed. The genetic operation is improved, and the fitness function is dynamically calibrated. Based on the visual sphere model, the best observation distance is assigned as the radius of the visual sphere, and the required constraints are fused to establish an intelligent networking design of the centering multivision. A simulation and experiment show that the proposed algorithm is widely feasible, and its measurement accuracy meets the requirements of the industrial field. Our multiview intelligent networking algorithms and methods provide solid theoretical and technical support for low-cost and efficient on-site 3D measurements of industrial structures.
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Affiliation(s)
- Yujing Qiao
- School of Mechanical Engineering, Yangzhou Polytechnic College, Yangzhou 225009, China
| | - Ning Lv
- School of Mechanical Engineering, Yangzhou Polytechnic College, Yangzhou 225009, China
| | - Baoming Jia
- Intelligent Machine Institute, Harbin University of Science and Technology, Harbin 150080, China
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15
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Almutairi SA. A multimodal AI-based non-invasive COVID-19 grading framework powered by deep learning, manta ray, and fuzzy inference system from multimedia vital signs. Heliyon 2023; 9:e16552. [PMID: 37251492 PMCID: PMC10210825 DOI: 10.1016/j.heliyon.2023.e16552] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 05/19/2023] [Accepted: 05/19/2023] [Indexed: 05/31/2023] Open
Abstract
The COVID-19 pandemic has presented unprecedented challenges to healthcare systems worldwide. One of the key challenges in controlling and managing the pandemic is accurate and rapid diagnosis of COVID-19 cases. Traditional diagnostic methods such as RT-PCR tests are time-consuming and require specialized equipment and trained personnel. Computer-aided diagnosis systems and artificial intelligence (AI) have emerged as promising tools for developing cost-effective and accurate diagnostic approaches. Most studies in this area have focused on diagnosing COVID-19 based on a single modality, such as chest X-rays or cough sounds. However, relying on a single modality may not accurately detect the virus, especially in its early stages. In this research, we propose a non-invasive diagnostic framework consisting of four cascaded layers that work together to accurately detect COVID-19 in patients. The first layer of the framework performs basic diagnostics such as patient temperature, blood oxygen level, and breathing profile, providing initial insights into the patient's condition. The second layer analyzes the coughing profile, while the third layer evaluates chest imaging data such as X-ray and CT scans. Finally, the fourth layer utilizes a fuzzy logic inference system based on the previous three layers to generate a reliable and accurate diagnosis. To evaluate the effectiveness of the proposed framework, we used two datasets: the Cough Dataset and the COVID-19 Radiography Database. The experimental results demonstrate that the proposed framework is effective and trustworthy in terms of accuracy, precision, sensitivity, specificity, F1-score, and balanced accuracy. The audio-based classification achieved an accuracy of 96.55%, while the CXR-based classification achieved an accuracy of 98.55%. The proposed framework has the potential to significantly improve the accuracy and speed of COVID-19 diagnosis, allowing for more effective control and management of the pandemic. Furthermore, the framework's non-invasive nature makes it a more attractive option for patients, reducing the risk of infection and discomfort associated with traditional diagnostic methods.
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Affiliation(s)
- Saleh Ateeq Almutairi
- Taibah University, Applied College, Computer Science and Information department, Medinah, 41461, Saudi Arabia
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16
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Dawson R, O’Dwyer C, Irwin E, Mrozowski MS, Hunter D, Ingleby S, Riis E, Griffin PF. Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer. SENSORS (BASEL, SWITZERLAND) 2023; 23:4007. [PMID: 37112348 PMCID: PMC10142828 DOI: 10.3390/s23084007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/11/2023] [Accepted: 04/13/2023] [Indexed: 06/19/2023]
Abstract
Machine learning (ML) is an effective tool to interrogate complex systems to find optimal parameters more efficiently than through manual methods. This efficiency is particularly important for systems with complex dynamics between multiple parameters and a subsequent high number of parameter configurations, where an exhaustive optimisation search would be impractical. Here we present a number of automated machine learning strategies utilised for optimisation of a single-beam caesium (Cs) spin exchange relaxation free (SERF) optically pumped magnetometer (OPM). The sensitivity of the OPM (T/Hz), is optimised through direct measurement of the noise floor, and indirectly through measurement of the on-resonance demodulated gradient (mV/nT) of the zero-field resonance. Both methods provide a viable strategy for the optimisation of sensitivity through effective control of the OPM's operational parameters. Ultimately, this machine learning approach increased the optimal sensitivity from 500 fT/Hz to <109fT/Hz. The flexibility and efficiency of the ML approaches can be utilised to benchmark SERF OPM sensor hardware improvements, such as cell geometry, alkali species and sensor topologies.
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17
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Zhang X, Liu Q, Qu Y. An adaptive differential evolution algorithm with population size reduction strategy for unconstrained optimization problem. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
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18
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Naderi E, Mirzaei L, Pourakbari-Kasmaei M, Cerna FV, Lehtonen M. Optimization of active power dispatch considering unified power flow controller: application of evolutionary algorithms in a fuzzy framework. EVOLUTIONARY INTELLIGENCE 2023. [DOI: 10.1007/s12065-023-00826-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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19
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Ashraf MT, Hamid I, Nawaz Q, Ali H. Hybrid Approach using Extreme Gradient Boosting (XGBoost) and Evolutionary Algorithm for Cancer Classification. 2023 INTERNATIONAL MULTI-DISCIPLINARY CONFERENCE IN EMERGING RESEARCH TRENDS (IMCERT) 2023. [DOI: 10.1109/imcert57083.2023.10075236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
| | - Isma Hamid
- National Textie University,Department of Computer Science,Faisalabad,Pakistan
| | - Qamar Nawaz
- University of Agriculture,Department of Computer Science,Faisalabad,Pakistan
| | - Hamid Ali
- National Textile University,Department of Computer Science,Faisalabad,Pakistan
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20
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Comparison of recent metaheuristic optimization algorithms to solve the SHE optimization problem in MLI. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07980-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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21
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Soong CJ, Rahman RA, Ramli R, Manaf MSA, Ting CC. An Evolutionary Algorithm: An Enhancement of Binary Tournament Selection for Fish Feed Formulation. COMPLEXITY 2022; 2022:1-15. [DOI: 10.1155/2022/7796633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Binary tournament (BT) selection is known as an established selection operator that has been employed in various problems. However, in the development of evolutionary algorithms (EA), this selection operator has a drawback in providing an efficient implementation of the union procedure, which cannot guarantee a parsimonious knowledge base with reduced number of rules. Therefore, this paper introduces binary-standard deviation (SD) tournament selection into EA as an enhancement of BT that can lead to focus on more exploration in terms of searching for the best solutions. The proposed selection operator has been experimented within fish feed formulation in grouper fish farming as a case study on finding the minimum cost and fulfilling constraints. This approach is better than experimental design in terms of costs and time. The motivation for doing so is to search for alternative ingredients for the grouper fish, as the price of trash fish is too luxurious. It is because grouper fish are carnivorous and need many trash fish for better growth. The novelty of the proposed SD tournament selection is compared with BT selection in terms of searching for an efficient but not myopic algorithm. Hence, based on the comparative study, the findings of the enhanced selection operator towards the EA have been convinced and accepted in terms of better cost and fulfilling constraints requirements.
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Affiliation(s)
- Cai-Juan Soong
- Faculty of Data Science & Information Technology, INTI International University, Nilai, Malaysia
| | - Rosshairy Abd Rahman
- Institute of Strategic Industrial Decision Modelling, Universiti Utara Malaysia, Changlun, Malaysia
| | - Razamin Ramli
- School of Quantitative Sciences, Universiti Utara Malaysia, Changlun, Malaysia
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22
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Karkinli AE. Detection of object boundary from point cloud by using multi-population based differential evolution algorithm. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07969-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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23
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Agushaka JO, Ezugwu AE, Abualigah L. Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07854-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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24
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Comprehensive Planning of Laboratory Equipment Based on Genetic Algorithms. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5242251. [PMID: 36131900 PMCID: PMC9484927 DOI: 10.1155/2022/5242251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/17/2022] [Accepted: 08/27/2022] [Indexed: 11/28/2022]
Abstract
Laboratory equipment planning is a very important task in modern enterprise management. Laboratory equipment planning by computer algorithm is a very complex NP-hard combinatorial optimization problem, so it is impossible to find an accurate algorithm in polynomial time. In this study, an improved genetic algorithm is used to solve and analyze the comprehensive planning of laboratory equipment. After analyzing the traditional heuristic algorithm and genetic algorithm to solve the simple laboratory equipment planning problem, the simple laboratory equipment planning is designed and implemented according to the principle of the heuristic algorithm. Finally, the algorithm is implemented in Python. A general equipment planning based on genetic algorithm with two selection operators is realized. Two constraints of test start and completion time are given. In the scenario of using multiple test equipment for a test project, the possible solutions of laboratory equipment planning under given constraints are analyzed. The efficiency coefficient is not necessarily a constant, it is related to the output characteristics of energy equipment. Three independent planning algorithms are used to solve the actual test requirements. One is the planning method based on manual test scheduling in the test cycle of experimental instruments, the other is the genetic algorithm for gene location crossover operator, and the third is the genetic algorithm for experimental part crossover operator. The planning solutions obtained by the three algorithms are compared from three aspects: the shortest time to complete the test, the calculation time of the algorithm, and the utilization of the test equipment.
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25
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Panwar K, Deep K. Discrete Salp Swarm Algorithm for Euclidean Travelling Salesman Problem. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03976-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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26
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A deterministic and nature-inspired algorithm for the fuzzy multi-objective path optimization problem. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00825-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
AbstractIncreasing evaluation indexes have been involved in the network modeling, and some parameters cannot be described precisely. Fuzzy set theory becomes a promising mathematical method to characterize such uncertain parameters. This study investigates the fuzzy multi-objective path optimization problem (FMOPOP), in which each arc has multiple crisp and fuzzy weights simultaneously. Fuzzy weights are characterized by triangular fuzzy numbers or trapezoidal fuzzy numbers. We adopt two fuzzy number ranking methods based on their fuzzy graded mean values and distances from the fuzzy minimum number. Motivated by the ripple spreading patterns on the natural water surface, we propose a novel ripple-spreading algorithm (RSA) to solve the FMOPOP. Theoretical analyses prove that the RSA can find all Pareto optimal paths from the source node to all other nodes within a single run. Numerical examples and comparative experiments demonstrate the efficiency and robustness of the newly proposed RSA. Moreover, in the first numerical example, the processes of the RSA are illustrated using metaphor-based language and ripple spreading phenomena to be more comprehensible. To the best of our knowledge, the RSA is the first algorithm for the FMOPOP that can adopt various fuzzy numbers and ranking methods while maintaining optimality.
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27
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A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering. ENERGIES 2022. [DOI: 10.3390/en15145247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Numerical models can be used for many purposes in oil and gas engineering, such as production optimization and forecasting, uncertainty analysis, history matching, and risk assessment. However, subsurface problems are complex and non-linear, and making reliable decisions in reservoir management requires substantial computational effort. Proxy models have gained much attention in recent years. They are advanced non-linear interpolation tables that can approximate complex models and alleviate computational effort. Proxy models are constructed by running high-fidelity models to gather the necessary data to create the proxy model. Once constructed, they can be a great choice for different tasks such as uncertainty analysis, optimization, forecasting, etc. The application of proxy modeling in oil and gas has had an increasing trend in recent years, and there is no consensus rule on the correct choice of proxy model. As a result, it is crucial to better understand the advantages and disadvantages of various proxy models. The existing work in the literature does not comprehensively cover all proxy model types, and there is a considerable requirement for fulfilling the existing gaps in summarizing the classification techniques with their applications. We propose a novel categorization method covering all proxy model types. This review paper provides a more comprehensive guideline on comparing and developing a proxy model compared to the existing literature. Furthermore, we point out the advantages of smart proxy models (SPM) compared to traditional proxy models (TPM) and suggest how we may further improve SPM accuracy where the literature is limited. This review paper first introduces proxy models and shows how they are classified in the literature. Then, it explains that the current classifications cannot cover all types of proxy models and proposes a novel categorization based on various development strategies. This new categorization includes four groups multi-fidelity models (MFM), reduced-order models (ROM), TPM, and SPM. MFMs are constructed based on simplifying physics assumptions (e.g., coarser discretization), and ROMs are based on dimensional reduction (i.e., neglecting irrelevant parameters). Developing these two models requires an in-depth knowledge of the problem. In contrast, TPMs and novel SPMs require less effort. In other words, they do not solve the complex underlying mathematical equations of the problem; instead, they decouple the mathematical equations into a numeric dataset and train statistical/AI-driven models on the dataset. Nevertheless, SPMs implement feature engineering techniques (i.e., generating new parameters) for its development and can capture the complexities within the reservoir, such as the constraints and characteristics of the grids. The newly introduced parameters can help find the hidden patterns within the parameters, which eventually increase the accuracy of SPMs compared to the TPMs. This review highlights the superiority of SPM over traditional statistical/AI-based proxy models. Finally, the application of various proxy models in the oil and gas industry, especially in subsurface modeling with a set of real examples, is presented. The introduced guideline in this review aids the researchers in obtaining valuable information on the current state of PM problems in the oil and gas industry.
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28
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Eissa NS, Khairuddin U, Yusof R. A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation. BMC Bioinformatics 2022; 23:273. [PMID: 35818034 PMCID: PMC9275179 DOI: 10.1186/s12859-022-04815-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 06/21/2022] [Indexed: 11/10/2022] Open
Abstract
Background DNA Methylation is one of the most important epigenetic processes that are crucial to regulating the functioning of the human genome without altering the DNA sequence. DNA Methylation data for cancer patients are becoming more accessible than ever, which is attributed to newer DNA sequencing technologies, notably, the relatively low-cost DNA microarray technology by Illumina Infinium. This technology makes it possible to study DNA methylation at hundreds of thousands of different loci. Currently, most of the research found in the literature focuses on the discovery of DNA methylation markers for specific cancer types. A relatively small number of studies have attempted to find unified DNA methylation biomarkers that can diagnose different types of cancer (pan-cancer classification). Results In this study, the aim is to conduct a pan-classification of cancer disease. We retrieved individual data for different types of cancer patients from The Cancer Genome Atlas (TCGA) portal. We selected data for many cancer types: Breast Cancer (BRCA), Ovary Cancer (OV), Stomach Cancer (STOMACH), Colon Cancer (COAD), Kidney Cancer (KIRC), Liver Cancer (LIHC), Lung Cancer (LUSC), Prostate Cancer (PRAD) and Thyroid cancer (THCA). The data was pre-processed and later used to build the required dataset. The system that we developed consists of two main stages. The purpose of the first stage is to perform feature selection and, therefore, decrease the dimensionality of the DNA methylation loci (features). This is accomplished using an unsupervised metaheuristic technique. As for the second stage, we used supervised machine learning and developed deep neural network (DNN) models to help classify the samples’ malignancy status and cancer type. Experimental results showed that compared to recently published methods, our proposed system achieved better classification results in terms of recall, and similar and higher results in terms of precision and accuracy. The proposed system also achieved an excellent receiver operating characteristic area under the curve (ROC AUC) values varying from 0.85 to 0.89. Conclusions This research presented an effective new approach to classify different cancer types based on DNA methylation data retrieved from TCGA. The performance of the proposed system was compared to recently published works, using different performance metrics. It provided better results, confirming the effectiveness of the proposed method for classifying different cancer types based on DNA methylation data. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04815-7.
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Affiliation(s)
- Noureldin S Eissa
- Department of Computer Engineering, Arab Academy for Science, Technology and Maritime Transport, Cairo, Egypt. .,Centre for Artificial Intelligence and Robotics, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia.
| | - Uswah Khairuddin
- Centre for Artificial Intelligence and Robotics, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia.,Department of Mechanical Precision Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
| | - Rubiyah Yusof
- Centre for Artificial Intelligence and Robotics, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
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29
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Cicala G, Demarchi S, Menapace M, Annunziata L, Tacchella A. A comparison of declarative AI techniques for computer automated design of elevator systems. INTELLIGENZA ARTIFICIALE 2022. [DOI: 10.3233/ia-210132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Like other custom-built machinery, elevators are charecterized by a design process which includes selection, sizing and placement of components to fit a given configuration, satisfy users’ requirements and adhere to stringent normative regulations. Unlike mass-produced items, the design process needs to be repeated almost from scratch each time a new configuration is considered. Since elevators are still designed mostly manually, project engineers must engage in time-consuming and error-prone activities over and over again, leaving little to be reused from one design to the next. Computer automated design can provide a cost-effective solution as it relieves the project engineer from such burdens. However, it introduces new challenges both in terms of efficiency — the search space for solutions grows exponentially in the number of component choices — and effectiveness — the perceived quality of the final design may not be as good as in the manual process. In this paper we compare three mainstream AI techniques that can provide problem-solving capabilities inside our tool LiftCreate for automated elevator design, namely Genetic Algorithms (GAs), Constraint Programming (CP) and Satisfiability Modulo Theories (SMT). A special-purpose heuristic search technique embedded in LiftCreate provides us with a yardstick to evaluate the solutions obtained with GAs, CP and SMT and to assess their feasibility for practical applications.
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Affiliation(s)
- G. Cicala
- DIBRIS, Università degli Studi di Genova, Via Opera Pia, Genoa, Italy
| | - S. Demarchi
- DIBRIS, Università degli Studi di Genova, Via Opera Pia, Genoa, Italy
| | - M. Menapace
- DIBRIS, Università degli Studi di Genova, Via Opera Pia, Genoa, Italy
| | - L. Annunziata
- DIBRIS, Università degli Studi di Genova, Via Opera Pia, Genoa, Italy
| | - A. Tacchella
- DIBRIS, Università degli Studi di Genova, Via Opera Pia, Genoa, Italy
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30
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Yuan D, Zhang D, Yang Y, Yang S. Automatic construction of filter tree by genetic programming for ultrasound guidance image segmentation. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103641] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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31
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Wang BC, Liu ZZ, Song W. Solving constrained optimization problems via multifactorial evolution. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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32
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Structural Damage Identification Based on Variable-Length Elements and an Improved Genetic Algorithm for Railway Bridges. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
A new damage identification method is proposed to solve the problem of no correspondence between the element division form of the finite element model and the actual damage location. The three basic operators in the traditional genetic algorithm are improved, and the catastrophe and neighborhood search processes are introduced to enhance the local optimization ability of the algorithm. The train–rail–bridge coupling time-varying equation is established. Based on the dynamic response of the bridge under trainload, the damage index is constructed, and the corresponding objective function is given. Through a numerical example, the stability and convergence rate of the algorithm are statistically analyzed. The effects of noise, the number of measuring points, and train speed on the recognition results are discussed. The research results indicate that, even if the damage location is different from the element division form of the finite element model, this method can accurately locate the damage location, but it will affect the quantitative results to a certain extent. In addition, the convergence speed of this method is fast, and the computing efficiency is about 6.7 times that of the conventional one-time recognition method.
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33
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Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments. MATHEMATICS 2022. [DOI: 10.3390/math10111797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
We consider the problem of evolutionary self-organization of control strategies using the example of speculative trading in a non-stationary immersion market environment. The main issue that obstructs obtaining real profit is the extremely high instability of the system component of observation series which implement stochastic chaos. In these conditions, traditional techniques for increasing the stability of control strategies are ineffective. In particular, the use of adaptive computational schemes is difficult due to the high volatility and non-stationarity of observation series. That leads to significant statistical errors of both kinds in the generated control decisions. An alternative approach based on the use of dynamic robustification technologies significantly reduces the effectiveness of the decisions. In the current work, we propose a method based on evolutionary modeling, which supplies structural and parametric self-organization of the control model.
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34
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Radzki G, Bocewicz G, Wikarek J, Nielsen P, Banaszak Z. Comparison of exact and approximate approaches to UAVs mission contingency planning in dynamic environments. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:7094-7121. [PMID: 35730298 DOI: 10.3934/mbe.2022335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
This paper presents a novel approach to the joint proactive and reactive planning of deliveries by an unmanned aerial vehicle (UAV) fleet. We develop a receding horizon-based approach to contingency planning for the UAV fleet's mission. We considered the delivery of goods to spatially dispersed customers, over an assumed time horizon. In order to take into account forecasted weather changes that affect the energy consumption of UAVs and limit their range, we propose a set of reaction rules that can be encountered during delivery in a highly dynamic and unpredictable environment. These rules are used in the course of designing the contingency plans related to the need to implement an emergency return of the UAV to the base or handling of ad hoc ordered deliveries. Due to the nonlinearity of the environment's characteristics, both constraint programming and genetic algorithm paradigms have been implemented. Because of the NP-difficult nature of the considered planning problem, conditions have been developed that allow for the acceleration of calculations. The multiple computer experiments carried out allow for comparison representatives of the approximate and exact methods so as to judge which approach is faster for which size of the selected instance of the UAV mission planning problem.
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Affiliation(s)
- Grzegorz Radzki
- Faculty of Electronics and Computer Science, Koszalin University of Technology, Poland
| | - Grzegorz Bocewicz
- Faculty of Electronics and Computer Science, Koszalin University of Technology, Poland
| | - Jaroslaw Wikarek
- Department of Information Systems, Kielce University of Technology, Kielce, Poland
| | - Peter Nielsen
- Department of Materials and Production, Aalborg University, Denmark
| | - Zbigniew Banaszak
- Faculty of Electronics and Computer Science, Koszalin University of Technology, Poland
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Predominant Cognitive Learning Particle Swarm Optimization for Global Numerical Optimization. MATHEMATICS 2022. [DOI: 10.3390/math10101620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Particle swarm optimization (PSO) has witnessed giant success in problem optimization. Nevertheless, its optimization performance seriously degrades when coping with optimization problems with a lot of local optima. To alleviate this issue, this paper designs a predominant cognitive learning particle swarm optimization (PCLPSO) method to effectively tackle complicated optimization problems. Specifically, for each particle, a new promising exemplar is constructed by letting its personal best position cognitively learn from a better personal experience randomly selected from those of others based on a novel predominant cognitive learning strategy. As a result, different particles preserve different guiding exemplars. In this way, the learning effectiveness and the learning diversity of particles are expectedly improved. To eliminate the dilemma that PCLPSO is sensitive to the involved parameters, we propose dynamic adjustment strategies, so that different particles preserve different parameter settings, which is further beneficial to promote the learning diversity of particles. With the above techniques, the proposed PCLPSO could expectedly compromise the search intensification and diversification in a good way to search the complex solution space properly to achieve satisfactory performance. Comprehensive experiments are conducted on the commonly adopted CEC 2017 benchmark function set to testify the effectiveness of the devised PCLPSO. Experimental results show that PCLPSO obtains considerably competitive or even much more promising performance than several representative and state-of-the-art peer methods.
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A Hybrid Arithmetic Optimization and Golden Sine Algorithm for Solving Industrial Engineering Design Problems. MATHEMATICS 2022. [DOI: 10.3390/math10091567] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Arithmetic Optimization Algorithm (AOA) is a physically inspired optimization algorithm that mimics arithmetic operators in mathematical calculation. Although the AOA has an acceptable exploration and exploitation ability, it also has some shortcomings such as low population diversity, premature convergence, and easy stagnation into local optimal solutions. The Golden Sine Algorithm (Gold-SA) has strong local searchability and fewer coefficients. To alleviate the above issues and improve the performance of AOA, in this paper, we present a hybrid AOA with Gold-SA called HAGSA for solving industrial engineering design problems. We divide the whole population into two subgroups and optimize them using AOA and Gold-SA during the searching process. By dividing these two subgroups, we can exchange and share profitable information and utilize their advantages to find a satisfactory global optimal solution. Furthermore, we used the Levy flight and proposed a new strategy called Brownian mutation to enhance the searchability of the hybrid algorithm. To evaluate the efficiency of the proposed work, HAGSA, we selected the CEC 2014 competition test suite as a benchmark function and compared HAGSA against other well-known algorithms. Moreover, five industrial engineering design problems were introduced to verify the ability of algorithms to solve real-world problems. The experimental results demonstrate that the proposed work HAGSA is significantly better than original AOA, Gold-SA, and other compared algorithms in terms of optimization accuracy and convergence speed.
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37
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Multiple surrogates and offspring-assisted differential evolution for high-dimensional expensive problems. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.01.052] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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38
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The Influence of Genetic Algorithms on Learning Possibilities of Artificial Neural Networks. COMPUTERS 2022. [DOI: 10.3390/computers11050070] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The presented research study focuses on demonstrating the learning ability of a neural network using a genetic algorithm and finding the most suitable neural network topology for solving a demonstration problem. The network topology is significantly dependent on the level of generalization. More robust topology of a neural network is usually more suitable for particular details in the training set and it loses the ability to abstract general information. Therefore, we often design the network topology by taking into the account the required generalization, rather than the aspect of theoretical calculations. The next part of the article presents research whether a modification of the parameters of the genetic algorithm can achieve optimization and acceleration of the neural network learning process. The function of the neural network and its learning by using the genetic algorithm is demonstrated in a program for solving a computer game. The research focuses mainly on the assessment of the influence of changes in neural networks’ topology and changes in parameters in genetic algorithm on the achieved results and speed of neural network training. The achieved results are statistically presented and compared depending on the network topology and changes in the learning algorithm.
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Acosta-Angulo B, Lara-Ramos J, Diaz-Angulo J, Torres-Palma R, Martínez-Pachon D, Moncayo-Lasso A, Machuca-Martínez F. Analysis of the Applications of Particle Swarm Optimization and Genetic Algorithms on Reaction Kinetics: A Prospective Study for Advanced Oxidation Processes. ChemElectroChem 2022. [DOI: 10.1002/celc.202200229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
| | - Jose Lara-Ramos
- Universidad del Valle Escuela de Ingeniería Química COLOMBIA
| | | | - Ricardo Torres-Palma
- Universidad de Antioquía: Universidad de Antioquia Facultad de Ciencias Exactas y Naturales COLOMBIA
| | - Diana Martínez-Pachon
- Universidad Antonio Nariño: Universidad Antonio Narino Facultad de Ciencias COLOMBIA
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Metaheuristic-Based Practical Tool for Optimal Design of Reinforced Concrete Isolated Footings: Development and Application for Parametric Investigation. BUILDINGS 2022. [DOI: 10.3390/buildings12040471] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
In the process of designing an economical structure, safety along with total cost must be balanced. This can be attained by design optimization, however the complex nature of the algorithms involved hinders its application. Further, there is a severe lack of research on the optimization of reinforced concrete (RC) isolated footings. Therefore, the main objective of this research is to develop a user-friendly tool for the optimization of RC isolated footings using advanced metaheuristic algorithms to make it more practical and convenient to adopt for design optimization. For this purpose, a spreadsheet-based interface is created in which input parameters from the original design can be entered to find the best option for the minimum cost design. The Evolutionary Algorithm (EA) and the Genetic Algorithm (GA) are used as metaheuristic techniques for optimization. The original design of four examples from the literature is compared with the optimized design obtained from the developed tool to demonstrate its efficiency. For the considered case studies, cost-saving of up to 44% has been obtained. Furthermore, a parametric investigation for the minimum cost objective using the GA has been performed through which a detailed analysis of geometric reinforcement and material strength variables is conducted. The results lead to the derivation of useful thumb rules for the economical design and proportioning of isolated footings.
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41
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Santos MS, Abreu PH, Japkowicz N, Fernández A, Soares C, Wilk S, Santos J. On the joint-effect of class imbalance and overlap: a critical review. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10150-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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42
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Elahi I, Ali H, Asif M, Iqbal K, Ghadi Y, Alabdulkreem E. An evolutionary algorithm for multi-objective optimization of freshwater consumption in textile dyeing industry. PeerJ Comput Sci 2022; 8:e932. [PMID: 35494829 PMCID: PMC9044317 DOI: 10.7717/peerj-cs.932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
Optimization is challenging even after numerous multi-objective evolutionary algorithms have been developed. Most of the multi-objective evolutionary algorithms failed to find out the best solutions spread and took more fitness evolution value to find the best solution. This article proposes an extended version of a multi-objective group counseling optimizer called MOGCO-II. The proposed algorithm is compared with MOGCO, MOPSO, MOCLPSO, and NSGA-II using the well-known benchmark problem such as Zitzler Deb Thieler (ZDT) function. The experiments show that the proposed algorithm generates a better solution than the other algorithms. The proposed algorithm also takes less fitness evolution value to find the optimal Pareto front. Moreover, the textile dyeing industry needs a large amount of fresh water for the dyeing process. After the dyeing process, the textile dyeing industry discharges a massive amount of polluted water, which leads to serious environmental problems. Hence, we proposed a MOGCO-II based optimization scheduling model to reduce freshwater consumption in the textile dyeing industry. The results show that the optimization scheduling model reduces freshwater consumption in the textile dyeing industry by up to 35% compared to manual scheduling.
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Affiliation(s)
- Ihsan Elahi
- Department of Computer Science, National Textile University, Faisalabad, Punjab, Pakistan
- Department of Computational Sciences, The University of Faisalabad (TUF), Faisalabad, Punjab, Pakistan
| | - Hamid Ali
- Department of Computer Science, National Textile University, Faisalabad, Punjab, Pakistan
| | - Muhammad Asif
- Department of Computer Science, National Textile University, Faisalabad, Punjab, Pakistan
| | - Kashif Iqbal
- Department of Textile Engineering, National Textile University, Faisalabad, Punjab, Pakistan
| | - Yazeed Ghadi
- Department of Software Engineering/Computer Science, Al Ain University, Al Ain, UAE
| | - Eatedal Alabdulkreem
- Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), Riyadh, Saudi Arabia
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43
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Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation. MATHEMATICS 2022. [DOI: 10.3390/math10071014] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Image segmentation is a key stage in image processing because it simplifies the representation of the image and facilitates subsequent analysis. The multi-level thresholding image segmentation technique is considered one of the most popular methods because it is efficient and straightforward. Many relative works use meta-heuristic algorithms (MAs) to determine threshold values, but they have issues such as poor convergence accuracy and stagnation into local optimal solutions. Therefore, to alleviate these shortcomings, in this paper, we present a modified remora optimization algorithm (MROA) for global optimization and image segmentation tasks. We used Brownian motion to promote the exploration ability of ROA and provide a greater opportunity to find the optimal solution. Second, lens opposition-based learning is introduced to enhance the ability of search agents to jump out of the local optimal solution. To substantiate the performance of MROA, we first used 23 benchmark functions to evaluate the performance. We compared it with seven well-known algorithms regarding optimization accuracy, convergence speed, and significant difference. Subsequently, we tested the segmentation quality of MORA on eight grayscale images with cross-entropy as the objective function. The experimental metrics include peak signal-to-noise ratio (PSNR), structure similarity (SSIM), and feature similarity (FSIM). A series of experimental results have proved that the MROA has significant advantages among the compared algorithms. Consequently, the proposed MROA is a promising method for global optimization problems and image segmentation.
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The Automatic Design of Multimode Resonator Topology with Evolutionary Algorithms. SENSORS 2022; 22:s22051961. [PMID: 35271118 PMCID: PMC8915033 DOI: 10.3390/s22051961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 02/22/2022] [Accepted: 02/26/2022] [Indexed: 02/04/2023]
Abstract
Microwave electromagnetic devices have been used for many applications in tropospheric communication, navigation, radar systems, and measurement. The development of the signal preprocessing units including frequency-selective devices (bandpass filters) determines the reliability and usability of such systems. In wireless sensor network nodes, filters with microstrip resonators are widely used to improve the out-of-band suppression and frequency selectivity. Filters based on multimode microstrip resonators have an order that determines their frequency-selective properties, which is a multiple of the number of resonators. That enables us to reduce the size of systems without deteriorating their selective properties. Various microstrip multimode resonator topologies can be used for both filters and microwave sensors, however, the quality criteria for them may differ. The development of every resonator topology is time consuming. We propose a technique for the automatic generation of the resonator topology with required frequency characteristics based on the use of evolutionary algorithms. The topology is encoded into a set of real valued parameters, which are varied to achieve the desired features. The differential evolution algorithm and the genetic algorithm with simulated binary crossover and polynomial mutation are applied to solve the formulated problem using the dynamic penalties method. The experimental results show that our technique enables us to find microstrip resonator topologies with desired amplitude-frequency characteristics automatically, and manufactured devices demonstrate characteristics very close to the results of the algorithm. The proposed algorithmic approach may be used for automatically exploring the new perspective topologies of resonators used in microwave filters, radar antennas or sensors, in accordance with the defined criteria and constraints.
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Ahandani MA, Abbasfam J, Kharrati H. Parameter identification of permanent magnet synchronous motors using quasi-opposition-based particle swarm optimization and hybrid chaotic particle swarm optimization algorithms. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03223-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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46
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Stability Enhancement of Wind Energy Conversion Systems Based on Optimal Superconducting Magnetic Energy Storage Systems Using the Archimedes Optimization Algorithm. Processes (Basel) 2022. [DOI: 10.3390/pr10020366] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Throughout the past several years, the renewable energy contribution and particularly the contribution of wind energy to electrical grid systems increased significantly, along with the problem of keeping the systems stable. This article presents a new optimization technique entitled the Archimedes optimization algorithm (AOA) that enhances the wind energy conversion system’s stability, integrated with a superconducting magnetic energy storage (SMES) system that uses a proportional integral (PI) controller. The AOA is a modern population technique based on Archimedes’ law of physics. The SMES system has a big impact in integrating wind generators with the electrical grid by regulating the output of wind generators and strengthening the power system’s performance. In this study, the AOA was employed to determine the optimum conditions of the PI controller that regulates the charging and discharging of the SMES system. The simulation outcomes of the AOA, the genetic algorithm (GA), and particle swarm optimization (PSO) were compared to ensure the efficacy of the introduced optimization algorithm. The simulation results showed the effectiveness of the optimally controlled SMES system, using the AOA in smoothing the output power variations and increasing the stability of the system under various operating conditions.
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Xu W, Yu X. A novel space contraction based on evolutionary strategy for economic dispatch. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107743] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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48
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Siddique A, Delwar TS, Ryu JY. A Novel Optimized V-VLC Receiver Sensor Design Using μGA in Automotive Applications. SENSORS 2021; 21:s21237861. [PMID: 34883866 PMCID: PMC8659808 DOI: 10.3390/s21237861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 11/20/2021] [Accepted: 11/21/2021] [Indexed: 11/16/2022]
Abstract
Vehicular visible light communication is known as a promising way of inter-vehicle communication. Vehicular VLC can ensure the significant advancement of safety and efficiency in traffic. It has disadvantages, such as unexpected glare on drivers in moving conditions, i.e., non-line-of-sight link at night. While designing a receiver, the most important factor is to ensure the optimal quality of the received signal. Within this context, to achieve an optimal communication quality, it is necessary to find the optimal maximum signal strength. Hereafter, a new receiver design is focused on in this paper at the circuit level, and a novel micro genetic algorithm is proposed to optimize the signal strength. The receiver can calculate the SNR, and it is possible to modify its structural design. The micro GA determines the alignment of the maximum signal strength at the receiver point rather than monitoring the signal strength for each angle. The results showed that the proposed scheme accurately estimates the alignment of the receiver, which gives the optimum signal strength. In comparison with the conventional GA, the micro GA results showed that the maximum received signal strength was improved by −1.7 dBm, −2.6 dBm for user Location 1 and user Location 2, respectively, which proves that the micro GA is more efficient. The execution time of the conventional GA was 7.1 s, while the micro GA showed 0.7 s. Furthermore, at a low SNR, the receiver showed robust communication for automotive applications.
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Abstract
Because of the rapid increase in the depletion rate of conventional energy sources, the energy crisis has become a central problem in the contemporary world. This issue opens the gateway for exploring and developing renewable energy sources to fulfill the exigent energy demand. Solar energy is an abundant source of sustainable energy and hence, nowadays, solar photovoltaic (PV) systems are employed to extract energy from solar irradiation. However, the PV systems need to work at the maximum power point (MPP) to exploit the highest accessible power during varying operating conditions. For this reason, maximum power point tracking (MPPT) algorithms are used to track the optimum power point. Furthermore, the efficient utilization of PV systems is hindered by renowned partial shading conditions (PSC), which generate multiple peaks in the power-voltage characteristic of the PV array. Thus, this article addresses the performance of the newly developed jellyfish search optimization (JSO) strategy in the PV frameworks to follow the global maximum power point (GMPP) under PSC.
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50
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Time-Lag Selection for Time-Series Forecasting Using Neural Network and Heuristic Algorithm. ELECTRONICS 2021. [DOI: 10.3390/electronics10202518] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
The time-series forecasting is a vital area that motivates continuous investigate areas of intrigued for different applications. A critical step for the time-series forecasting is the right determination of the number of past observations (lags). This paper investigates the forecasting accuracy based on the selection of an appropriate time-lag value by applying a comparative study between three methods. These methods include a statistical approach using auto correlation function, a well-known machine learning technique namely Long Short-Term Memory (LSTM) along with a heuristic algorithm to optimize the choosing of time-lag value, and a parallel implementation of LSTM that dynamically choose the best prediction based on the optimal time-lag value. The methods were applied to an experimental data set, which consists of five meteorological parameters and aerosol particle number concentration. The performance metrics were: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and R-squared. The investigation demonstrated that the proposed LSTM model with heuristic algorithm is the superior method in identifying the best time-lag value.
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