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Li M, Luo Q, Zhou Y. BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection. Biomimetics (Basel) 2024; 9:187. [PMID: 38534872 DOI: 10.3390/biomimetics9030187] [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: 02/01/2024] [Revised: 03/09/2024] [Accepted: 03/11/2024] [Indexed: 03/28/2024] Open
Abstract
Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection.
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Affiliation(s)
- Mengjun Li
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
| | - Qifang Luo
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
- Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China
| | - Yongquan Zhou
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
- Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
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2
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Manoharan P, Chandrasekaran K, Chandran R, Ravichandran S, Mohammad S, Jangir P. An effective strategy for unit commitment of microgrid power systems integrated with renewable energy sources including effects of battery degradation and uncertainties. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:11037-11080. [PMID: 38217814 DOI: 10.1007/s11356-023-31608-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 12/14/2023] [Indexed: 01/15/2024]
Abstract
The large use of renewable sources and plug-in electric vehicles (PEVs) would play a critical part in achieving a low-carbon energy source and reducing greenhouse gas emissions, which are the primary cause of global warming. On the other hand, predicting the instability and intermittent nature of wind and solar power output poses significant challenges. To reduce the unpredictable and random nature of renewable microgrids (MGs) and additional unreliable energy sources, a battery energy storage system (BESS) is connected to an MG system. The uncoordinated charging of PEVs offers further hurdles to the unit commitment (UC) required in contemporary MG management. The UC problem is an exceptionally difficult optimization problem due to the mixed-integer structure, large scale, and nonlinearity. It is further complicated by the multiple uncertainties associated with renewable sources, PEV charging and discharging, and electricity market pricing, in addition to the BESS degradation factor. Therefore, in this study, a new variant of mixed-integer particle swarm optimizer is introduced as a reliable optimization framework to handle the UC problem. This study considers six various case studies of UC problems, including uncertainties and battery degradation to validate the reliability and robustness of the proposed algorithm. Out of which, two case studies defined as a multiobjective problem, and it has been transformed into a single-objective model using different weight factors. The simulation findings demonstrate that the proposed approach and improved methodology for the UC problem are effective than its peers. Based on the average results, the economic consequences of numerous scenarios are thoroughly examined and contrasted, and some significant conclusions are presented.
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Affiliation(s)
- Premkumar Manoharan
- Department of Electrical & Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, 560078, India.
- Department of Electrical and Electronics Engineering, Institute of Power Engineering (IPE), College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Putrajaya, Kajang, Selangor, Malaysia.
| | - Kumar Chandrasekaran
- Department of Electrical & Electronics Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, 641032, India
| | - Ramakrishnan Chandran
- Department of Electrical & Electronics Engineering, SNS College of Technology, Coimbatore, Tamil Nadu, 641035, India
| | - Sowmya Ravichandran
- Department of Electrical & Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Udupi, Karnataka, 576104, India
| | - Soni Mohammad
- Department of Electrical & Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, 560078, India
| | - Pradeep Jangir
- Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, 602105, Chennai, India
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3
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Abdelrazek M, Abd Elaziz M, El-Baz AH. CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection. Sci Rep 2024; 14:701. [PMID: 38184680 PMCID: PMC10771514 DOI: 10.1038/s41598-023-50959-8] [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: 08/26/2023] [Accepted: 12/28/2023] [Indexed: 01/08/2024] Open
Abstract
In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9-100%), sensitivity (77.6-100%), precision (91.8-96.08%), specificity (91.6-100%) and F-Score (90-100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC'2022 benchmarks functions.
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Affiliation(s)
- Mohammed Abdelrazek
- Department of Mathematics, Faculty of Science, Damietta University, New Damietta, 34517, Egypt
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, UAE
- MEU Research Unit, Middle East University, Amman, 11831, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon
| | - A H El-Baz
- Department of Computer Science, Faculty of Computers and Artificial Intelligence, Damietta University, New Damietta, 34517, Egypt.
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4
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Ding H, Liu Y, Wang Z, Jin G, Hu P, Dhiman G. Adaptive Guided Equilibrium Optimizer with Spiral Search Mechanism to Solve Global Optimization Problems. Biomimetics (Basel) 2023; 8:383. [PMID: 37754134 PMCID: PMC10526928 DOI: 10.3390/biomimetics8050383] [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: 07/17/2023] [Revised: 08/13/2023] [Accepted: 08/16/2023] [Indexed: 09/28/2023] Open
Abstract
The equilibrium optimizer (EO) is a recently developed physics-based optimization technique for complex optimization problems. Although the algorithm shows excellent exploitation capability, it still has some drawbacks, such as the tendency to fall into local optima and poor population diversity. To address these shortcomings, an enhanced EO algorithm is proposed in this paper. First, a spiral search mechanism is introduced to guide the particles to more promising search regions. Then, a new inertia weight factor is employed to mitigate the oscillation phenomena of particles. To evaluate the effectiveness of the proposed algorithm, it has been tested on the CEC2017 test suite and the mobile robot path planning (MRPP) problem and compared with some advanced metaheuristic techniques. The experimental results demonstrate that our improved EO algorithm outperforms the comparison methods in solving both numerical optimization problems and practical problems. Overall, the developed EO variant has good robustness and stability and can be considered as a promising optimization tool.
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Affiliation(s)
- Hongwei Ding
- School of Information Science and Engineering, Yunnan University, Kunming 650106, China; (H.D.); (Y.L.)
| | - Yuting Liu
- School of Information Science and Engineering, Yunnan University, Kunming 650106, China; (H.D.); (Y.L.)
| | - Zongshan Wang
- School of Information Science and Engineering, Yunnan University, Kunming 650106, China; (H.D.); (Y.L.)
| | - Gushen Jin
- Glasgow College, University of Electronic Science and Technology of China, Chengdu 611731, China;
| | - Peng Hu
- Research and Development Department, Youbei Technology Co., Ltd., Kunming 650011, China;
| | - Gaurav Dhiman
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon;
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5
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Chen Z, Xuan P, Heidari AA, Liu L, Wu C, Chen H, Escorcia-Gutierrez J, Mansour RF. An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection. iScience 2023; 26:106679. [PMID: 37216098 PMCID: PMC10193239 DOI: 10.1016/j.isci.2023.106679] [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: 01/17/2023] [Revised: 03/01/2023] [Accepted: 04/12/2023] [Indexed: 05/24/2023] Open
Abstract
The domains of contemporary medicine and biology have generated substantial high-dimensional genetic data. Identifying representative genes and decreasing the dimensionality of the data can be challenging. The goal of gene selection is to minimize computing costs and enhance classification precision. Therefore, this article designs a new wrapper gene selection algorithm named artificial bee bare-bone hunger games search (ABHGS), which is the hunger games search (HGS) integrated with an artificial bee strategy and a Gaussian bare-bone structure to address this issue. To evaluate and validate the performance of our proposed method, ABHGS is compared to HGS and a single strategy embedded in HGS, six classic algorithms, and ten advanced algorithms on the CEC 2017 functions. The experimental results demonstrate that the bABHGS outperforms the original HGS. Compared to peers, it increases classification accuracy and decreases the number of selected features, indicating its actual engineering utility in spatial search and feature selection.
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Affiliation(s)
- Zhiqing Chen
- School of Intelligent Manufacturing, Wenzhou Polytechnic, Wenzhou 325035, China
| | - Ping Xuan
- Department of Computer Science, School of Engineering, Shantou University, Shantou 515063, China
| | - Ali Asghar Heidari
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan 610065, China
| | - Chengwen Wu
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Huiling Chen
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - José Escorcia-Gutierrez
- Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla 080002, Colombia
| | - Romany F. Mansour
- Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt
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6
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Rai R, Dhal KG. Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:1-54. [PMID: 37359743 PMCID: PMC10096115 DOI: 10.1007/s11831-023-09923-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 03/26/2023] [Indexed: 06/28/2023]
Abstract
There have been many algorithms created and introduced in the literature inspired by various events observable in nature, such as evolutionary phenomena, the actions of social creatures or agents, broad principles based on physical processes, the nature of chemical reactions, human behavior, superiority, and intelligence, intelligent behavior of plants, numerical techniques and mathematics programming procedure and its orientation. Nature-inspired metaheuristic algorithms have dominated the scientific literature and have become a widely used computing paradigm over the past two decades. Equilibrium Optimizer, popularly known as EO, is a population-based, nature-inspired meta-heuristics that belongs to the class of Physics based optimization algorithms, enthused by dynamic source and sink models with a physics foundation that are used to make educated guesses about equilibrium states. EO has achieved massive recognition, and there are quite a few changes made to existing EOs. This article gives a thorough review of EO and its variations. We started with 175 research articles published by several major publishers. Additionally, we discuss the strengths and weaknesses of the algorithms to help researchers find the variant that best suits their needs. The core optimization problems from numerous application areas using EO are also covered in the study, including image classification, scheduling problems, and many others. Lastly, this work recommends a few potential areas for EO research in the future.
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Affiliation(s)
- Rebika Rai
- Department of Computer Applications, Sikkim University, Sikkim, India
| | - Krishna Gopal Dhal
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, Midnapore, West Bengal India
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7
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Turgut OE, Turgut MS, Kırtepe E. A systematic review of the emerging metaheuristic algorithms on solving complex optimization problems. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08481-5] [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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8
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Marjit S, Bhattacharyya T, Chatterjee B, Sarkar R. Simulated annealing aided genetic algorithm for gene selection from microarray data. Comput Biol Med 2023; 158:106854. [PMID: 37023541 DOI: 10.1016/j.compbiomed.2023.106854] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/26/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
In recent times, microarray gene expression datasets have gained significant popularity due to their usefulness to identify different types of cancer directly through bio-markers. These datasets possess a high gene-to-sample ratio and high dimensionality, with only a few genes functioning as bio-markers. Consequently, a significant amount of data is redundant, and it is essential to filter out important genes carefully. In this paper, we propose the Simulated Annealing aided Genetic Algorithm (SAGA), a meta-heuristic approach to identify informative genes from high-dimensional datasets. SAGA utilizes a two-way mutation-based Simulated Annealing (SA) as well as Genetic Algorithm (GA) to ensure a good trade-off between exploitation and exploration of the search space, respectively. The naive version of GA often gets stuck in a local optimum and depends on the initial population, leading to premature convergence. To address this, we have blended a clustering-based population generation with SA to distribute the initial population of GA over the entire feature space. To further enhance the performance, we reduce the initial search space by a score-based filter approach called the Mutually Informed Correlation Coefficient (MICC). The proposed method is evaluated on 6 microarray and 6 omics datasets. Comparison of SAGA with contemporary algorithms has shown that SAGA performs much better than its peers. Our code is available at https://github.com/shyammarjit/SAGA.
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9
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Devi RM, Premkumar M, Kiruthiga G, Sowmya R. IGJO: An Improved Golden Jackel Optimization Algorithm Using Local Escaping Operator for Feature Selection Problems. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11146-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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10
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Zhong C, Li G, Meng Z, Li H, He W. A self-adaptive quantum equilibrium optimizer with artificial bee colony for feature selection. Comput Biol Med 2023; 153:106520. [PMID: 36608463 DOI: 10.1016/j.compbiomed.2022.106520] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/28/2022] [Accepted: 12/31/2022] [Indexed: 01/03/2023]
Abstract
Feature selection (FS) is a popular data pre-processing technique in machine learning to extract the optimal features to maintain or increase the classification accuracy of the dataset, which is a combinatorial optimization problem, requiring a powerful optimizer to obtain the optimum subset. The equilibrium optimizer (EO) is a recent physical-based metaheuristic algorithm with good performance for various optimization problems, but it may encounter premature or the local convergence in feature selection. This work presents a self-adaptive quantum EO with artificial bee colony for feature selection, named SQEOABC. In the proposed algorithm, the quantum theory and the self-adaptive mechanism are employed into the updating rule of EO to enhance convergence, and the updating mechanism from the artificial bee colony is also incorporated into EO to achieve appropriate FS solutions. In the experiments, 25 benchmark datasets from the UCI repository are investigated to verify SQEOABC, which is compared with several state-of-the-art metaheuristic algorithms and the variants of EO. The statistical results of fitness values and accuracy demonstrate that SQEOABC has better performance than the compared algorithms and the variants of EO. Finally, a real-world FS problem from COVID-19 illustrates the effectiveness and superiority of SQEOABC.
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Affiliation(s)
- Changting Zhong
- Department of Engineering Mechanics, State Key Laboratory of Structural Analyses for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China; School of Civil Engineering and Architecture, Hainan University, Haikou 570228, China.
| | - Gang Li
- Department of Engineering Mechanics, State Key Laboratory of Structural Analyses for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China; Ningbo Institute of Dalian University of Technology, Ningbo, 315000, China.
| | - Zeng Meng
- School of Civil Engineering, Hefei University of Technology, Hefei, 230009, China.
| | - Haijiang Li
- BIM for Smart Engineering Centre, Cardiff School of Engineering, Cardiff University, Queen's Buildings, Cardiff, CF24 3AA, Whales, UK.
| | - Wanxin He
- Department of Engineering Mechanics, State Key Laboratory of Structural Analyses for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China.
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11
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Wang Z, Gao S, Zhang Y, Guo L. Symmetric uncertainty-incorporated probabilistic sequence-based ant colony optimization for feature selection in classification. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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12
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Pramanik R, Sarkar S, Sarkar R. An adaptive and altruistic PSO-based deep feature selection method for Pneumonia detection from Chest X-rays. Appl Soft Comput 2022; 128:109464. [PMID: 35966452 PMCID: PMC9364947 DOI: 10.1016/j.asoc.2022.109464] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 07/11/2022] [Accepted: 07/29/2022] [Indexed: 12/23/2022]
Abstract
Pneumonia is one of the major reasons for child mortality especially in income-deprived regions of the world. Although it can be detected and treated with very less sophisticated instruments and medication, Pneumonia detection still remains a major concern in developing countries. Computer-aided based diagnosis (CAD) systems can be used in such countries due to their lower operating costs than professional medical experts. In this paper, we propose a CAD system for Pneumonia detection from Chest X-rays, using the concepts of deep learning and a meta-heuristic algorithm. We first extract deep features from the pre-trained ResNet50, fine-tuned on a target Pneumonia dataset. Then, we propose a feature selection technique based on particle swarm optimization (PSO), which is modified using a memory-based adaptation parameter, and enriched by incorporating an altruistic behavior into the agents. We name our feature selection method as adaptive and altruistic PSO (AAPSO). The proposed method successfully eliminates non-informative features obtained from the ResNet50 model, thereby improving the Pneumonia detection ability of the overall framework. Extensive experimentation and thorough analysis on a publicly available Pneumonia dataset establish the superiority of the proposed method over several other frameworks used for Pneumonia detection. Apart from Pneumonia detection, AAPSO is further evaluated on some standard UCI datasets, gene expression datasets for cancer prediction and a COVID-19 prediction dataset. The overall results are satisfactory, thereby confirming the usefulness of AAPSO in dealing with varied real-life problems. The supporting source codes of this work can be found at https://github.com/rishavpramanik/AAPSO.
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Affiliation(s)
- Rishav Pramanik
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India
| | - Sourodip Sarkar
- Department of Electronics and Communication Engineering, Heritage Institute of Technology, Kolkata, 700107, India
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India
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Balakrishnan K, Dhanalakshmi R, Akila M, Sinha BB. Improved equilibrium optimization based on Levy flight approach for feature selection. EVOLVING SYSTEMS 2022. [DOI: 10.1007/s12530-022-09461-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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14
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Beheshti Z. BMPA-TVSinV: A Binary Marine Predators Algorithm using time-varying sine and V-shaped transfer functions for wrapper-based feature selection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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15
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Ahmed S, Sheikh KH, Mirjalili S, Sarkar R. Binary Simulated Normal Distribution Optimizer for feature selection: Theory and application in COVID-19 datasets. EXPERT SYSTEMS WITH APPLICATIONS 2022; 200:116834. [PMID: 36034050 PMCID: PMC9396289 DOI: 10.1016/j.eswa.2022.116834] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 02/25/2022] [Accepted: 03/03/2022] [Indexed: 05/04/2023]
Abstract
Classification accuracy achieved by a machine learning technique depends on the feature set used in the learning process. However, it is often found that all the features extracted by some means for a particular task do not contribute to the classification process. Feature selection (FS) is an imperative and challenging pre-processing technique that helps to discard the unnecessary and irrelevant features while reducing the computational time and space requirement and increasing the classification accuracy. Generalized Normal Distribution Optimizer (GNDO), a recently proposed meta-heuristic algorithm, can be used to solve any optimization problem. In this paper, a hybrid version of GNDO with Simulated Annealing (SA) called Binary Simulated Normal Distribution Optimizer (BSNDO) is proposed which uses SA as a local search to achieve higher classification accuracy. The proposed method is evaluated on 18 well-known UCI datasets and compared with its predecessor as well as some popular FS methods. Moreover, this method is tested on high dimensional microarray datasets to prove its worth in real-life datasets. On top of that, it is also applied to a COVID-19 dataset for classification purposes. The obtained results prove the usefulness of BSNDO as a FS method. The source code of this work is publicly available at https://github.com/ahmed-shameem/Feature_selection.
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Affiliation(s)
- Shameem Ahmed
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
| | - Khalid Hassan Sheikh
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
| | - Seyedali Mirjalili
- King Abdulaziz University, Jeddah, Saudi Arabia
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, 4006 QLD, Australia
- Yonsei Frontier Lab, Yonsei University, Seoul, Republic of Korea
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
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17
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An enhanced binary Rat Swarm Optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection. Comput Biol Med 2022; 147:105675. [PMID: 35687926 DOI: 10.1016/j.compbiomed.2022.105675] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 05/24/2022] [Accepted: 05/26/2022] [Indexed: 11/22/2022]
Abstract
In this paper, an enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed to deal with Feature Selection (FS) problems. FS is an important data reduction step in data mining which finds the most representative features from the entire data. Many FS-based swarm intelligence algorithms have been used to tackle FS. However, the door is still open for further investigations since no FS method gives cutting-edge results for all cases. In this paper, a recent swarm intelligence metaheuristic method called RSO which is inspired by the social and hunting behavior of a group of rats is enhanced and explored for FS problems. The binary enhanced RSO is built based on three successive modifications: i) an S-shape transfer function is used to develop binary RSO algorithms; ii) the local search paradigm of particle swarm optimization is used with the iterative loop of RSO to boost its local exploitation; iii) three crossover mechanisms are used and controlled by a switch probability to improve the diversity. Based on these enhancements, three versions of RSO are produced, referred to as Binary RSO (BRSO), Binary Enhanced RSO (BERSO), and Binary Enhanced RSO with Crossover operators (BERSOC). To assess the performance of these versions, a benchmark of 24 datasets from various domains is used. The proposed methods are assessed concerning the fitness value, number of selected features, classification accuracy, specificity, sensitivity, and computational time. The best performance is achieved by BERSOC followed by BERSO and then BRSO. These proposed versions are comparatively assessed against 25 well-regarded metaheuristic methods and five filter-based approaches. The obtained results underline their superiority by producing new best results for some datasets.
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18
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Binary Horse herd optimization algorithm with crossover operators for feature selection. Comput Biol Med 2021; 141:105152. [PMID: 34952338 DOI: 10.1016/j.compbiomed.2021.105152] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/11/2021] [Accepted: 12/14/2021] [Indexed: 01/30/2023]
Abstract
This paper proposes a binary version of Horse herd Optimization Algorithm (HOA) to tackle Feature Selection (FS) problems. This algorithm mimics the conduct of a pack of horses when they are trying to survive. To build a Binary version of HOA, or referred to as BHOA, twofold of adjustments were made: i) Three transfer functions, namely S-shape, V-shape and U-shape, are utilized to transform the continues domain into a binary one. Four configurations of each transfer function are also well studied to yield four alternatives. ii) Three crossover operators: one-point, two-point and uniform are also suggested to ensure the efficiency of the proposed method for FS domain. The performance of the proposed fifteen BHOA versions is examined using 24 real-world FS datasets. A set of six metric measures was used to evaluate the outcome of the optimization methods: accuracy, number of features selected, fitness values, sensitivity, specificity and computational time. The best-formed version of the proposed versions is BHOA with S-shape and one-point crossover. The comparative evaluation was also accomplished against 21 state-of-the-art methods. The proposed method is able to find very competitive results where some of them are the best-recorded. Due to the viability of the proposed method, it can be further considered in other areas of machine learning.
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A Novel Learning-Based Binarization Scheme Selector for Swarm Algorithms Solving Combinatorial Problems. MATHEMATICS 2021. [DOI: 10.3390/math9222887] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Currently, industry is undergoing an exponential increase in binary-based combinatorial problems. In this regard, metaheuristics have been a common trend in the field in order to design approaches to successfully solve them. Thus, a well-known strategy includes the employment of continuous swarm-based algorithms transformed to perform in binary environments. In this work, we propose a hybrid approach that contains discrete smartly adapted population-based strategies to efficiently tackle binary-based problems. The proposed approach employs a reinforcement learning technique, known as SARSA (State–Action–Reward–State–Action), in order to utilize knowledge based on the run time. In order to test the viability and competitiveness of our proposal, we compare discrete state-of-the-art algorithms smartly assisted by SARSA. Finally, we illustrate interesting results where the proposed hybrid outperforms other approaches, thus, providing a novel option to tackle these types of problems in industry.
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