Comprehensive Learning Particle Swarm Optimization Algorithm With Local Search for Multimodal Functions

被引：308

作者：

Cao, Yulian ^{[1
]}

Zhang, Han ^{[2
]}

Li, Wenfeng ^{[1
]}

Zhou, Mengchu ^{[3
]}

Zhang, Yu ^{[1
]}

Chaovalitwongse, Wanpracha Art ^{[4
]}

机构：

[1] Wuhan Univ Technol, Sch Logist Engn, Wuhan 430063, Hubei, Peoples R China

[2] Karlsruhe Inst Technol, Inst Nucl & Energy Technol, D-76344 Eggenstein Leopoldshafen, Germany

[3] New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA

[4] Univ Arkansas, Dept Ind Engn, Fayetteville, AR 72701 USA

来源：

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION | 2019年 / 23卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Adaptive strategy; evolutionary algorithm; local search (LS); multimodal function; particle swarm optimization (PSO); GLOBAL OPTIMIZATION; DIVERSITY; NETWORK;

D O I：

10.1109/TEVC.2018.2885075

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A comprehensive learning particle swarm optimizer (CLPSO) embedded with local search (LS) is proposed to pursue higher optimization performance by taking the advantages of CLPSO's strong global search capability and LS's fast convergence ability. This paper proposes an adaptive LS starting strategy by utilizing our proposed quasi-entropy index to address its key issue, i.e., when to start LS. The changes of the index as the optimization proceeds are analyzed in theory and via numerical tests. The proposed algorithm is tested on multimodal benchmark functions. Parameter sensitivity analysis is performed to demonstrate its robustness. The comparison results reveal overall higher convergence rate and accuracy than those of CLPSO, state-of-the-art particle swarm optimization variants.

引用

页码：718 / 731

页数：14

共 51 条

[11] A Supervised Learning and Control Method to Improve Particle Swarm Optimization Algorithms
Dong, Wenyong
Zhou, MengChu
[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2017, 47 (07): : 1135 - 1148
[12] Fei Han, 2015, Advances in Swarm and Computational Intelligence - 6th International Conference, ICSI 2015, held in conjunction with the Second BRICS Congress, CCI 2015. Proceedings: LNCS 9140, P460, DOI 10.1007/978-3-319-20466-6_48
[13] Incorporation of Solvent Effect into Multi-Objective Evolutionary Algorithm for Improved Protein Structure Prediction
Gao, Shangce
Song, Shuangbao
Cheng, Jiujun
Todo, Yuki
Zhou, Mengchu
[J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2018, 15 (04) : 1365 - 1378
[14] Haibin Duan, 2015, IEEE/CAA Journal of Automatica Sinica, V2, P11, DOI 10.1109/JAS.2015.7032901
[15] Enhanced leader PSO (ELPSO): A new PSO variant for solving global optimisation problems
Jordehi, A. Rezaee
[J]. APPLIED SOFT COMPUTING, 2015, 26 : 401 - 417
[16] A Collaborative Resource Allocation Strategy for Decomposition-Based Multiobjective Evolutionary Algorithms
Kang, Qi
Song, Xinyao
Zhou, MengChu
Li, Li
[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2019, 49 (12): : 2416 - 2423
[17] Kennedy J, 1995, 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6, P1942, DOI 10.1109/icnn.1995.488968
[18] Composite Particle Swarm Optimizer With Historical Memory for Function Optimization
Li, Jie
Zhang, Junqi
Jiang, ChangJun
Zhou, MengChu
[J]. IEEE TRANSACTIONS ON CYBERNETICS, 2015, 45 (10) : 2350 - 2363
[19] Li X., 2013, Tech. Rep
[20] Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
Liang, J. J.
Qin, A. K.
Suganthan, Ponnuthurai Nagaratnam
Baskar, S.
[J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2006, 10 (03) : 281 - 295

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