Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism
The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a nov...
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| Vydané v: | Computational intelligence and neuroscience Ročník 2020; číslo 2020; s. 1 - 26 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Cairo, Egypt
Hindawi Publishing Corporation
2020
Hindawi John Wiley & Sons, Inc |
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| ISSN: | 1687-5265, 1687-5273, 1687-5273 |
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| Abstract | The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed θ-dominance is adopted to further enhance the performance of the algorithm. The proposed algorithm is evaluated on the standard benchmark problems DTLZ, WFG, and UF1-9 and compared with the four recently proposed multiobjective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary optimization algorithms. The experimental results indicate that the proposed algorithm has better convergence and diversity, and its performance is superior to other comparative algorithms on most test instances. |
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| AbstractList | The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed θ-dominance is adopted to further enhance the performance of the algorithm. The proposed algorithm is evaluated on the standard benchmark problems DTLZ, WFG, and UF1-9 and compared with the four recently proposed multiobjective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary optimization algorithms. The experimental results indicate that the proposed algorithm has better convergence and diversity, and its performance is superior to other comparative algorithms on most test instances. The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed -dominance is adopted to further enhance the performance of the algorithm. The proposed algorithm is evaluated on the standard benchmark problems DTLZ, WFG, and UF1-9 and compared with the four recently proposed multiobjective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary optimization algorithms. The experimental results indicate that the proposed algorithm has better convergence and diversity, and its performance is superior to other comparative algorithms on most test instances. The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed θ-dominance is adopted to further enhance the performance of the algorithm. The proposed algorithm is evaluated on the standard benchmark problems DTLZ, WFG, and UF1-9 and compared with the four recently proposed multiobjective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary optimization algorithms. The experimental results indicate that the proposed algorithm has better convergence and diversity, and its performance is superior to other comparative algorithms on most test instances.The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed θ-dominance is adopted to further enhance the performance of the algorithm. The proposed algorithm is evaluated on the standard benchmark problems DTLZ, WFG, and UF1-9 and compared with the four recently proposed multiobjective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary optimization algorithms. The experimental results indicate that the proposed algorithm has better convergence and diversity, and its performance is superior to other comparative algorithms on most test instances. The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed θ -dominance is adopted to further enhance the performance of the algorithm. The proposed algorithm is evaluated on the standard benchmark problems DTLZ, WFG, and UF1-9 and compared with the four recently proposed multiobjective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary optimization algorithms. The experimental results indicate that the proposed algorithm has better convergence and diversity, and its performance is superior to other comparative algorithms on most test instances. |
| Audience | Academic |
| Author | Wang, Yi Zhang, Maosheng Yang, Wusi Chen, Li |
| AuthorAffiliation | 2 Key Laboratory for Geo-Hazards in Loess Area, MNR, Xi'an Center of Geological Survey, China Geological Survey, Xi'an 710054, China 1 School of Information Technology and Software, Northwest University, Xi'an 710127, China |
| AuthorAffiliation_xml | – name: 2 Key Laboratory for Geo-Hazards in Loess Area, MNR, Xi'an Center of Geological Survey, China Geological Survey, Xi'an 710054, China – name: 1 School of Information Technology and Software, Northwest University, Xi'an 710127, China |
| Author_xml | – sequence: 1 fullname: Zhang, Maosheng – sequence: 2 fullname: Wang, Yi – sequence: 3 fullname: Chen, Li – sequence: 4 fullname: Yang, Wusi |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32190037$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1007_s00500_023_09050_7 crossref_primary_10_1007_s10489_022_03465_9 crossref_primary_10_1016_j_asoc_2020_106655 crossref_primary_10_3390_app11219787 crossref_primary_10_3390_math9161959 crossref_primary_10_1007_s40747_020_00263_z crossref_primary_10_3390_app13063589 crossref_primary_10_7717_peerj_cs_2872 crossref_primary_10_3390_sym14122619 crossref_primary_10_1007_s11831_022_09778_9 crossref_primary_10_1109_ACCESS_2021_3086559 |
| Cites_doi | 10.1016/j.cie.2019.01.055 10.1109/TEVC.2013.2258025 10.1016/j.asoc.2019.01.033 10.1007/978-3-540-31880-4_35 10.1109/TEVC.2013.2281535 10.1007/s00500-016-2098-x 10.1155/2016/1898527 10.1016/j.swevo.2018.12.007 10.1109/TEVC.2004.826067 10.1016/j.ins.2016.09.026 10.1016/j.amc.2014.12.006 10.1007/s10489-018-1170-x 10.1109/TEVC.2007.892759 10.1109/TEVC.2012.2185847 10.1016/j.asoc.2016.11.009 10.1109/TEVC.2016.2631279 10.1109/TEVC.2015.2420112 10.1109/TEVC.2005.861417 10.1109/TCYB.2014.2322602 10.1162/EVCO_a_00009 10.1007/s00500-014-1570-8 10.1109/TEVC.2016.2519378 10.1016/j.ejor.2015.06.071 10.1109/TEVC.2016.2549267 10.1109/TEVC.2018.2791283 10.1162/EVCO_a_00104 10.1007/s00500-017-2687-3 10.1016/j.ins.2011.04.003 10.1016/j.ins.2017.10.037 10.1016/j.ins.2015.07.018 10.1109/TCYB.2017.2710133 10.1109/mci.2017.2742868 10.1109/tevc.2008.925798 10.1371/journal.pone.0172033 10.1109/4235.996017 |
| ContentType | Journal Article |
| Copyright | Copyright © 2020 Wusi Yang et al. COPYRIGHT 2020 John Wiley & Sons, Inc. Copyright © 2020 Wusi Yang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0 Copyright © 2020 Wusi Yang et al. 2020 |
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| SubjectTerms | Algorithms Comparative analysis Competition Computer Simulation Convergence Evolutionary algorithms Mathematical optimization Multiple objective analysis Optimization algorithms Particle swarm optimization |
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| Title | Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism |
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