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      An electronic transition-based bare bones particle swarm optimization algorithm for high dimensional optimization problems

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          Abstract

          An electronic transition-based bare bones particle swarm optimization (ETBBPSO) algorithm is proposed in this paper. The ETBBPSO is designed to present high precision results for high dimensional single-objective optimization problems. Particles in the ETBBPSO are divided into different orbits. A transition operator is proposed to enhance the global search ability of ETBBPSO. The transition behavior of particles gives the swarm more chance to escape from local minimums. In addition, an orbit merge operator is proposed in this paper. An orbit with low search ability will be merged by an orbit with high search ability. Extensive experiments with CEC2014 and CEC2020 are evaluated with ETBBPSO. Four famous population-based algorithms are also selected in the control group. Experimental results prove that ETBBPSO can present high precision results for high dimensional single-objective optimization problems.

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          Most cited references32

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          Differential Evolution: A Survey of the State-of-the-Art

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            Particle swarm optimization for feature selection in classification: a multi-objective approach.

            Classification problems often have a large number of features in the data sets, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the performance. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main conflicting objectives of maximizing the classification performance and minimizing the number of features. However, most existing feature selection algorithms treat the task as a single objective problem. This paper presents the first study on multi-objective particle swarm optimization (PSO) for feature selection. The task is to generate a Pareto front of nondominated solutions (feature subsets). We investigate two PSO-based multi-objective feature selection algorithms. The first algorithm introduces the idea of nondominated sorting into PSO to address feature selection problems. The second algorithm applies the ideas of crowding, mutation, and dominance to PSO to search for the Pareto front solutions. The two multi-objective algorithms are compared with two conventional feature selection methods, a single objective feature selection method, a two-stage feature selection algorithm, and three well-known evolutionary multi-objective algorithms on 12 benchmark data sets. The experimental results show that the two PSO-based multi-objective algorithms can automatically evolve a set of nondominated solutions. The first algorithm outperforms the two conventional methods, the single objective method, and the two-stage algorithm. It achieves comparable results with the existing three well-known multi-objective algorithms in most cases. The second algorithm achieves better results than the first algorithm and all other methods mentioned previously.
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              Genetic algorithms: a survey

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                Author and article information

                Contributors
                Role: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Software
                Role: InvestigationRole: Project administrationRole: Validation
                Role: Funding acquisitionRole: MethodologyRole: Supervision
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2022
                25 July 2022
                : 17
                : 7
                : e0271925
                Affiliations
                [1 ] School of Information and Communication Engineering, Hubei University of Economics, Wuhan, China
                [2 ] Smart Business Department of China Construction Third Engineering Bureau Installation Engineering Co., Ltd., Wuhan, China
                [3 ] Faculty of Computer and Information Sciences, Hosei University, Tokyo, Japan
                Torrens University Australia, AUSTRALIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-5042-4045
                https://orcid.org/0000-0001-8387-7710
                Article
                PONE-D-22-07662
                10.1371/journal.pone.0271925
                9312387
                35877651
                adab4ff2-c0f4-4d00-9cb3-1f947b43659f
                © 2022 Tian et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 18 March 2022
                : 10 July 2022
                Page count
                Figures: 31, Tables: 5, Pages: 23
                Funding
                Funded by: Hubei University of Economics
                Award ID: PYZD202001
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001691, Japan Society for the Promotion of Science;
                Award ID: KAKENHI Grant Number, JP19K12162
                Award Recipient :
                Hao Tian is supported by the Hubei University of Economics Research and Cultivation Key Projects PYZD202001; Yuji Sato is supported by the JSPS KAKENHI Grant Numbers JP19K12162; The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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