Multi-objective particle swarm optimization algorithm using Cauchy mutation and improved crowding distance

PurposeMulti-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is prop...

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Published in:	International journal of intelligent computing and cybernetics Vol. 16; no. 2; pp. 250 - 276
Main Authors:	Li, Qingxia, Zeng, Xiaohua, Wei, Wenhong
Format:	Journal Article
Language:	English
Published:	Bingley Emerald Publishing Limited 15.05.2023 Emerald Group Publishing Limited
Subjects:	Adaptive algorithms Archives & records Convergence Crowding Decomposition Efficiency Local optimization Multiple objective analysis Objectives Optimization algorithms Pareto optimization Pareto optimum Particle swarm optimization Swarm intelligence Pareto Cauchy variation Crowding distance Multi-objective Particle swarm optimization
ISSN:	1756-378X, 1756-3798
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Abstract	PurposeMulti-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence. Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm. Therefore, this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.Design/methodology/approachIn this paper, the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.FindingsIn order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm, this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization. Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.Originality/valueIn order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently, this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.
AbstractList	PurposeMulti-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence. Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm. Therefore, this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.Design/methodology/approachIn this paper, the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.FindingsIn order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm, this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization. Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.Originality/valueIn order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently, this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.
Author	Zeng, Xiaohua Wei, Wenhong Li, Qingxia
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References	(key2023071410503704400_ref023) 2021; 14 (key2023071410503704400_ref024) 2021; 40 (key2023071410503704400_ref001) 2020; 36 (key2023071410503704400_ref004) 2019; 23 (key2023071410503704400_ref016) 2020; 14 (key2023071410503704400_ref030) 2018; 454-455 (key2023071410503704400_ref015) 2022; 19 (key2023071410503704400_ref025) 2020; 86 (key2023071410503704400_ref032) 2017; 47 (key2023071410503704400_ref007) 2014; 18 (key2023071410503704400_ref020) 2019; 46 (key2023071410503704400_ref013) 2021; 7 (key2023071410503704400_ref006) 2015; 148 (key2023071410503704400_ref017) 2015 (key2023071410503704400_ref009) 2022; 61 (key2023071410503704400_ref003) 2013; 13 (key2023071410503704400_ref026) 2005 (key2023071410503704400_ref027) 2014 (key2023071410503704400_ref014) 2014 (key2023071410503704400_ref010) 2021; 733 (key2023071410503704400_ref008) 2002; 6 (key2023071410503704400_ref018) 2018; 6 (key2023071410503704400_ref005) 2020; 25 (key2023071410503704400_ref002) 2014; 22 (key2023071410503704400_ref019) 2019; 7 (key2023071410503704400_ref031) 2011; 1 (key2023071410503704400_ref012) 2016; 93 (key2023071410503704400_ref029) 2018 (key2023071410503704400_ref022) 2019; 50 (key2023071410503704400_ref028) 2018; 48 (key2023071410503704400_ref011) 2015; 8 (key2023071410503704400_ref021) 2021; 36
References_xml	– volume: 36 start-page: 2085 issue: 9 year: 2021 ident: key2023071410503704400_ref021 article-title: R2 indicator and objective space partition based many-objective particle swarm optimizer publication-title: Kongzhi yu Juece/Control and Decision – volume: 6 start-page: 182 issue: 2 year: 2002 ident: key2023071410503704400_ref008 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.996017 – volume: 93 start-page: 48 year: 2016 ident: key2023071410503704400_ref012 article-title: A novel artificial bee colony algorithm for shortest path problems with fuzzy arc weights publication-title: Journal of the International Measurement Confederation doi: 10.1016/j.measurement.2016.06.050 – volume: 86 year: 2020 ident: key2023071410503704400_ref025 article-title: A self-organized speciation based multi-objective particle swarm optimizer for multimodal multi-objective problems publication-title: Applied Soft Computing – volume: 7 start-page: 1 issue: 4 year: 2021 ident: key2023071410503704400_ref013 article-title: Modified artificial bee colony algorithm for solving mixed interval-valued fuzzy shortest path problem publication-title: Complex and Intelligent Systems – start-page: 257 year: 2005 ident: key2023071410503704400_ref026 article-title: An effective use of crowding distance in multiobjective particle swarm optimization – volume: 48 start-page: 3762 year: 2018 ident: key2023071410503704400_ref028 article-title: A novel multi-objective optimization algorithm based on artificial algae for multi-objective engineering design problems publication-title: Applied Intelligence doi: 10.1007/s10489-018-1170-x – volume: 7 start-page: 168091 year: 2019 ident: key2023071410503704400_ref019 article-title: A multi-objective particle swarm optimization algorithm based on enhanced selection publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2954542 – volume: 733 start-page: 61 year: 2021 ident: key2023071410503704400_ref010 article-title: An interactive framework to compare multi-criteria optimization algorithms: preliminary results on NSGA-II and MOPSO. 2020 international conference on communication publication-title: Computing and Electronics Systems – volume: 8 start-page: 203 issue: 2 year: 2015 ident: key2023071410503704400_ref011 article-title: Particle swarm optimisation algorithm for solving shortest path problems with mixed fuzzy arc weights publication-title: International Journal of Applied Decision Sciences doi: 10.1504/IJADS.2015.069610 – volume: 1 start-page: 32 issue: 1 year: 2011 ident: key2023071410503704400_ref031 article-title: Multi-objective evolutionary algorithms: a survey of the state-of-the-art publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2011.03.001 – volume: 47 start-page: 2794 issue: 9 year: 2017 ident: key2023071410503704400_ref032 article-title: An external archive-guided multiobjective particle swarm optimization algorithm publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2017.2710133 – volume: 19 start-page: 230 issue: 2 year: 2022 ident: key2023071410503704400_ref015 article-title: An effective fault-tolerance technique in web services: an approach based on hybrid optimization algorithm of PSO and cuckoo search publication-title: The International Arab Journal of Information Technology – volume: 18 start-page: 577 issue: 4 year: 2014 ident: key2023071410503704400_ref007 article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: solving problems with box constraints publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2013.2281535 – start-page: 1324 year: 2014 ident: key2023071410503704400_ref027 article-title: Hybrid many-objective particle swarm optimization set-evolution – start-page: 1 year: 2018 ident: key2023071410503704400_ref029 article-title: A many-objective particle swarm optimization based on virtual pareto front – volume: 6 start-page: 14710 year: 2018 ident: key2023071410503704400_ref018 article-title: A hybrid multiobjective particle swarm optimization algorithm based on R2 indicator publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2812701 – volume: 23 start-page: 9979 issue: 20 year: 2019 ident: key2023071410503704400_ref004 article-title: Software requirement optimization using a fuzzy artificial chemical reaction optimization algorithm publication-title: Soft Computing doi: 10.1007/s00500-018-3553-7 – volume: 454-455 start-page: 59 year: 2018 ident: key2023071410503704400_ref030 article-title: Surrogate-assisted hierarchical particle swarm optimization publication-title: Information Sciences doi: 10.1016/j.ins.2018.04.062 – volume: 25 start-page: 363 issue: 3 year: 2020 ident: key2023071410503704400_ref005 article-title: Parallel multi-objective artificial bee colony algorithm for software requirement optimization publication-title: Requirements Engineering doi: 10.1007/s00766-020-00328-y – volume: 13 start-page: 3903 issue: 9 year: 2013 ident: key2023071410503704400_ref003 article-title: Attributed multi-objective comprehensive learning particle swarm optimization for optimal security of networks publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2013.04.015 – volume: 36 start-page: 659 issue: 2 year: 2020 ident: key2023071410503704400_ref001 article-title: Elite artificial bees' colony algorithm to solve robot's fuzzy constrained routing problem publication-title: Computational Intelligence doi: 10.1111/coin.12258 – volume: 50 start-page: 320 issue: 2 year: 2019 ident: key2023071410503704400_ref022 article-title: Handling many-objective optimisation problems with R2 indicator and decomposition-based particle swarm optimiser publication-title: International Journal of Systems Science doi: 10.1080/00207721.2018.1552765 – start-page: 3148 year: 2015 ident: key2023071410503704400_ref017 article-title: R2-MOPSO: a multi-objective particle swarm optimizer based on R2-indicator and decomposition – volume: 22 start-page: 47 issue: 1 year: 2014 ident: key2023071410503704400_ref002 article-title: D2MOPSO: MOPSO based on decomposition and dominance with archiving using crowding distance in objective and solution spaces publication-title: Evolutionary Computation doi: 10.1162/EVCO_a_00104 – volume: 14 start-page: 369 issue: 4 year: 2020 ident: key2023071410503704400_ref016 article-title: Efficient improved ant colony optimisation algorithm for dynamic software rejuvenation in web services publication-title: IET Software doi: 10.1049/iet-sen.2019.0018 – volume: 14 start-page: 759 issue: 2 year: 2021 ident: key2023071410503704400_ref023 article-title: Non-dominated Sorting Genetic Algorithm (NSGA-III) for effective resource allocation in cloud publication-title: Evolutionary Intelligence doi: 10.1007/s12065-020-00436-2 – start-page: 266 year: 2014 ident: key2023071410503704400_ref014 article-title: MOPSOhv: a new hypervolume-based multi-objective particle swarm optimizer – volume: 40 start-page: 5027 issue: 3 year: 2021 ident: key2023071410503704400_ref024 article-title: A novel approach for the next software release using a binary artificial algae algorithm publication-title: Journal of Intelligent and Fuzzy Systems doi: 10.3233/JIFS-201759 – volume: 46 start-page: 104 year: 2019 ident: key2023071410503704400_ref020 article-title: Comparison between MOEA/D and NSGA-III on a set of many and multi-objective benchmark problems with challenging difficulties publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2019.02.003 – volume: 61 start-page: 3403 issue: 5 year: 2022 ident: key2023071410503704400_ref009 article-title: A novel ant colony algorithm for solving shortest path problems with fuzzy arc weights publication-title: Alexandria Engineering Journal doi: 10.1016/j.aej.2021.08.058 – volume: 148 start-page: 23 year: 2015 ident: key2023071410503704400_ref006 article-title: Improved multi-objective particle swarm optimization with preference strategy for optimal DG integration into the distribution system publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.08.074
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SubjectTerms	Adaptive algorithms Archives & records Convergence Crowding Decomposition Efficiency Local optimization Multiple objective analysis Objectives Optimization algorithms Pareto optimization Pareto optimum Particle swarm optimization Swarm intelligence
Title	Multi-objective particle swarm optimization algorithm using Cauchy mutation and improved crowding distance
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