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Han Y, Lin M, Li N, Qi Q, Li J, Liu Q. DCWPSO: particle swarm optimization with dynamic inertia weight updating and enhanced learning strategies. PeerJ Comput Sci 2024; 10:e2253. [PMID: 39314689 PMCID: PMC11419644 DOI: 10.7717/peerj-cs.2253] [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: 03/08/2024] [Accepted: 07/20/2024] [Indexed: 09/25/2024]
Abstract
Particle swarm optimization (PSO) stands as a prominent and robust meta-heuristic algorithm within swarm intelligence (SI). It originated in 1995 by simulating the foraging behavior of bird flocks. In recent years, numerous PSO variants have been proposed to address various optimization applications. However, the overall performance of these variants has not been deemed satisfactory. This article introduces a novel PSO variant, presenting three key contributions: First, a novel dynamic oscillation inertia weight is introduced to strike a balance between exploration and exploitation; Second, the utilization of cosine similarity and dynamic neighborhood strategy enhances both the quality of solution and the diversity of particle populations; Third, a unique worst-best example learning strategy is proposed to enhance the quality of the least favorable solution and consequently improving the overall population. The algorithm's validation is conducted using a test suite comprised of benchmarks from the CEC2014 and CEC2022 test suites on real-parameter single-objective optimization. The experimental results demonstrate the competitiveness of our algorithm against recently proposed state-of-the-art PSO variants and well-known algorithms.
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Affiliation(s)
- Yibo Han
- School of Computer Science and Technology, Hainan University, Haikou, China
| | - Meiting Lin
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Ni Li
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
- Key Laboratory of Data Science and Smart Education of Ministry of Education, Hainan Normal University, Haikou, China
| | - Qi Qi
- School of Computer Science and Technology, Hainan University, Haikou, China
| | - Jinqing Li
- School of Computer Science and Technology, Hainan University, Haikou, China
| | - Qingxin Liu
- School of Computer Science and Technology, Hainan University, Haikou, China
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2
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Bernardo RMC, Torres DFM, Herdeiro CAR, Soares Dos Santos MP. Universe-inspired algorithms for control engineering: A review. Heliyon 2024; 10:e31771. [PMID: 38882329 PMCID: PMC11176799 DOI: 10.1016/j.heliyon.2024.e31771] [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: 12/01/2023] [Revised: 05/08/2024] [Accepted: 05/21/2024] [Indexed: 06/18/2024] Open
Abstract
Control algorithms have been proposed based on knowledge related to nature-inspired mechanisms, including those based on the behavior of living beings. This paper presents a review focused on major breakthroughs carried out in the scope of applied control inspired by the gravitational attraction between bodies. A control approach focused on Artificial Potential Fields was identified, as well as four optimization metaheuristics: Gravitational Search Algorithm, Black-Hole algorithm, Multi-Verse Optimizer, and Galactic Swarm Optimization. A thorough analysis of ninety-one relevant papers was carried out to highlight their performance and to identify the gravitational and attraction foundations, as well as the universe laws supporting them. Included are their standard formulations, as well as their improved, modified, hybrid, cascade, fuzzy, chaotic and adaptive versions. Moreover, this review also deeply delves into the impact of universe-inspired algorithms on control problems of dynamic systems, providing an extensive list of control-related applications, and their inherent advantages and limitations. Strong evidence suggests that gravitation-inspired and black-hole dynamic-driven algorithms can outperform other well-known algorithms in control engineering, even though they have not been designed according to realistic astrophysical phenomena and formulated according to astrophysics laws. Even so, they support future research directions towards the development of high-sophisticated control laws inspired by Newtonian/Einsteinian physics, such that effective control-astrophysics bridges can be established and applied in a wide range of applications.
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Affiliation(s)
- Rodrigo M C Bernardo
- Center for Mechanical Technology & Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Delfim F M Torres
- Center for Research and Development in Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Carlos A R Herdeiro
- Center for Research and Development in Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Marco P Soares Dos Santos
- Center for Mechanical Technology & Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal
- Intelligent Systems Associate Laboratory (LASI), Portugal
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Liu Y, Zeng Y, Li R, Zhu X, Zhang Y, Li W, Li T, Zhu D, Hu G. A Random Particle Swarm Optimization Based on Cosine Similarity for Global Optimization and Classification Problems. Biomimetics (Basel) 2024; 9:204. [PMID: 38667215 PMCID: PMC11048164 DOI: 10.3390/biomimetics9040204] [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: 02/26/2024] [Revised: 03/23/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024] Open
Abstract
In today's fast-paced and ever-changing environment, the need for algorithms with enhanced global optimization capability has become increasingly crucial due to the emergence of a wide range of optimization problems. To tackle this issue, we present a new algorithm called Random Particle Swarm Optimization (RPSO) based on cosine similarity. RPSO is evaluated using both the IEEE Congress on Evolutionary Computation (CEC) 2022 test dataset and Convolutional Neural Network (CNN) classification experiments. The RPSO algorithm builds upon the traditional PSO algorithm by incorporating several key enhancements. Firstly, the parameter selection is adapted and a mechanism called Random Contrastive Interaction (RCI) is introduced. This mechanism fosters information exchange among particles, thereby improving the ability of the algorithm to explore the search space more effectively. Secondly, quadratic interpolation (QI) is incorporated to boost the local search efficiency of the algorithm. RPSO utilizes cosine similarity for the selection of both QI and RCI, dynamically updating population information to steer the algorithm towards optimal solutions. In the evaluation using the CEC 2022 test dataset, RPSO is compared with recent variations of Particle Swarm Optimization (PSO) and top algorithms in the CEC community. The results highlight the strong competitiveness and advantages of RPSO, validating its effectiveness in tackling global optimization tasks. Additionally, in the classification experiments with optimizing CNNs for medical images, RPSO demonstrated stability and accuracy comparable to other algorithms and variants. This further confirms the value and utility of RPSO in improving the performance of CNN classification tasks.
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Affiliation(s)
- Yujia Liu
- School of Intelligent Manufacturing Engineering, Jiangxi College of Application Science and Technology, Nanchang 330000, China
| | - Yuan Zeng
- School of Intelligent Manufacturing Engineering, Jiangxi College of Application Science and Technology, Nanchang 330000, China
| | - Rui Li
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
| | - Xingyun Zhu
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
| | - Yuemai Zhang
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
| | - Weijie Li
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
| | - Taiyong Li
- School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, China;
| | - Donglin Zhu
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
| | - Gangqiang Hu
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
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Khan TA, Ling SH, Rizvi AA. Optimisation of electrical Impedance tomography image reconstruction error using heuristic algorithms. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10527-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Turgut OE, Turgut MS, Kırtepe E. Q-learning-based metaheuristic algorithm for thermoeconomic optimization of a shell-and-tube evaporator working with refrigerant mixtures. Soft comput 2023. [DOI: 10.1007/s00500-023-08016-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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A chaotic adaptive butterfly optimization algorithm. EVOLUTIONARY INTELLIGENCE 2023. [DOI: 10.1007/s12065-023-00832-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
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7
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Levy flight incorporated hybrid learning model for gravitational search algorithm. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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8
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PSO-ELPM: PSO with Elite Learning, enhanced Parameter updating, and exponential Mutation operator. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.01.103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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Khan TA, Ling SH, Rizvi AA. Electrical Impedance Tomography – Image Reconstruction using Population-based Optimisation Algorithms.. [DOI: 10.21203/rs.3.rs-2343798/v1] [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
Abstract
Preventing living tissues' direct exposure to ionising radiation has resulted in tremendous growth in medical imaging and e-health, enhancing intensive care of perilous patients and helping to improve quality of life. Moreover, the practice of image-reconstruction instruments that utilise ionising radiation significantly impacts the patient's health. Prolonged or frequent exposure to ionising radiation is linked to several illnesses like cancer. These factors urged the advancement of non-invasive approaches, for instance, Electrical Impedance Tomography (EIT), a portable, non-invasive, low-cost, and safe imaging method. EIT image reconstruction still demands more exploitation, as it is an inverse and ill-conditioned problem. Numerous numerical techniques are used to answer this problem without producing anatomically unpredictable outcomes. Evolutionary Computational techniques can be used as substitutes for the conventional methods that usually create low-resolution blurry images. EIT reconstruction techniques optimise the relative error of reconstruction using population-based optimisation methods presented in this work. Three advanced optimisation methods have been developed to facilitate the iterative procedure for avoiding anatomically erratic solutions. Three different optimising techniques, namely, a) Advanced Particle Swarm Optimisation Algorithm (APSO), b) Advanced Gravitational Search Algorithm (AGSA), and c) Hybrid Gravitational Search Particle Swarm Optimization Algorithm (HGSPSO), are used. By utilising the advantages of these proposed techniques, the performance in terms of convergence and solution stability is improved. EIT images were obtained from the EIDORS library database for two case studies. The image reconstruction was optimised using the three proposed algorithms. EIDORS library was used for generating and solving forward and reverse problems. Two case studies were undertaken, i.e. circular tank simulation and gastric emptying. The results thus obtained are analysed and presented as a real-world application of population-based optimisation methods. Results obtained from the proposed methods are quantitatively assessed with ground truth images using the relative mean squared error, confirming that a low error value is reached in the results. HGSPSO algorithm has superior performance compared to the other proposed methods in terms of solution quality and stability.
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All particles driving particle swarm optimization: Superior particles pulling plus inferior particles pushing. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Improved Particle Swarm Optimization Algorithms for Optimal Designs with Various Decision Criteria. MATHEMATICS 2022. [DOI: 10.3390/math10132310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Particle swarm optimization (PSO) is an attractive, easily implemented method which is successfully used across a wide range of applications. In this paper, utilizing the core ideology of genetic algorithm and dynamic parameters, an improved particle swarm optimization algorithm is proposed. Then, based on the improved algorithm, combining the PSO algorithm with decision making, nested PSO algorithms with two useful decision making criteria (optimistic coefficient criterion and minimax regret criterion) are proposed . The improved PSO algorithm is implemented on two unimodal functions and two multimodal functions, and the results are much better than that of the traditional PSO algorithm. The nested algorithms are applied on the Michaelis–Menten model and two parameter logistic regression model as examples. For the Michaelis–Menten model, the particles converge to the best solution after 50 iterations. For the two parameter logistic regression model, the optimality of algorithms are verified by the equivalence theorem. More results for other models applying our algorithms are available upon request.
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Joshi SK. Chaos embedded opposition based learning for gravitational search algorithm. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03786-9] [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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Mutual learning differential particle swarm optimization. EGYPTIAN INFORMATICS JOURNAL 2022. [DOI: 10.1016/j.eij.2022.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Aerodynamic-Aeroacoustic Optimization of a Baseline Wing and Flap Configuration. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031063] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Optimization design was widely used in the high-lift device design process, and the aeroacoustic reduction characteristic is an important objective of the optimization. The aerodynamic and aeroacoustic study on the baseline wing and flap configuration was performed numerically. In the current study, the three-dimensional Large Eddy Simulation (LES) equations coupled with dynamic Smagorinsky subgrid model and Ffowcs–William and Hawkings (FW–H) equation are employed to simulate the flow fields and carry out acoustic analogy. The numerical results show reasonable agreement with the experimental data. Further, the particle swarm optimization algorithm coupled with the Kriging surrogate model was employed to determine optimum location of the flap deposition. The Latin hypercube method is used for the generation of initial samples for optimization. In addition, the relationship between the design variables and the objective functions are obtained using the optimization sample points. The optimized maximum overall sound pressure level (OASPL) of far-field noise decreases by 3.99 dB with a loss of lift-drag ratio (L/D) of less than 1%. Meanwhile, the optimized performances are in good and reasonable agreement with the numerical predictions. The findings provide suggestions for the low-noise and high-lift configuration design and application in high-lift devices.
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Lai V, Huang YF, Koo CH, Ahmed AN, El-Shafie A. A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 29:3435-3457. [PMID: 35250256 PMCID: PMC8877748 DOI: 10.1007/s11831-021-09701-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 12/07/2021] [Indexed: 05/15/2023]
Abstract
Reservoir operation optimisation secures benefits, such as optimising energy production while minimising the possibility of flooding, operating costs, and water scarcity, at the lowest possible cost. This paper carries reviews of research on reservoir optimisation models and the consequential challenges of optimally operating reservoir operations. An introductory section is given to the background of reservoir operations and the current concerns on the optimal reservoir operations, for the decision-makers and stakeholders. Next, the review covered the recent ten years (between 2011 and 2021), on the recent research developments in innovation and techniques of reservoir operation optimisation. Further reviews on the conventional techniques that are the traditional methods, linear programming, nonlinear programming, and dynamic programming are discussed. Enhancements to the techniques in improving the drawbacks of the traditional techniques in optimisation of reservoir policies are next explained and evaluated. Recent advances in applying metaheuristics optimisation algorithms beneficial to the reservoir operations are explained, including the advantages and hinderances. A comprehensive tabulated and categorised review according to the classification of reservoir models, evaluation methods, and reservoir systems is given.
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Affiliation(s)
- Vivien Lai
- Department of Civil Engineering, Universiti Tunku Abdul Rahman, Selangor, Malaysia
| | - Yuk Feng Huang
- Department of Civil Engineering, Universiti Tunku Abdul Rahman, Selangor, Malaysia
| | - Chai Hoon Koo
- Department of Civil Engineering, Universiti Tunku Abdul Rahman, Selangor, Malaysia
| | - Ali Najah Ahmed
- Institute of Engineering Infrastructures (IEI), Universiti Tenaga Nasional (UNITEN), 43000 Selangor, Malaysia
| | - Ahmed El-Shafie
- Department of Civil Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
- National Water and Energy Center, United Arab Emirates University, P.O Box 1551, Al Ain, United Arab Emirates
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Lin S, Jia H, Abualigah L, Altalhi M. Enhanced Slime Mould Algorithm for Multilevel Thresholding Image Segmentation Using Entropy Measures. ENTROPY 2021; 23:e23121700. [PMID: 34946006 PMCID: PMC8700578 DOI: 10.3390/e23121700] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/17/2021] [Accepted: 12/17/2021] [Indexed: 01/10/2023]
Abstract
Image segmentation is a fundamental but essential step in image processing because it dramatically influences posterior image analysis. Multilevel thresholding image segmentation is one of the most popular image segmentation techniques, and many researchers have used meta-heuristic optimization algorithms (MAs) to determine the threshold values. However, MAs have some defects; for example, they are prone to stagnate in local optimal and slow convergence speed. This paper proposes an enhanced slime mould algorithm for global optimization and multilevel thresholding image segmentation, namely ESMA. First, the Levy flight method is used to improve the exploration ability of SMA. Second, quasi opposition-based learning is introduced to enhance the exploitation ability and balance the exploration and exploitation. Then, the superiority of the proposed work ESMA is confirmed concerning the 23 benchmark functions. Afterward, the ESMA is applied in multilevel thresholding image segmentation using minimum cross-entropy as the fitness function. We select eight greyscale images as the benchmark images for testing and compare them with the other classical and state-of-the-art algorithms. Meanwhile, the experimental metrics include the average fitness (mean), standard deviation (Std), peak signal to noise ratio (PSNR), structure similarity index (SSIM), feature similarity index (FSIM), and Wilcoxon rank-sum test, which is utilized to evaluate the quality of segmentation. Experimental results demonstrated that ESMA is superior to other algorithms and can provide higher segmentation accuracy.
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Affiliation(s)
- Shanying Lin
- College of Marine Engineering, Dalian Maritime University, Dalian 116026, China
- Correspondence: (S.L.); (H.J.)
| | - Heming Jia
- School of Information Engineering, Sanming University, Sanming 365004, China
- Correspondence: (S.L.); (H.J.)
| | - Laith Abualigah
- Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan; or
- School of Computer Science, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
| | - Maryam Altalhi
- Department of Management Information System, College of Business Administration, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
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