Particle Swarm Optimization with Hybrid Ring Topology for Multimodal Optimization Problems

Zong-Gan CHEN; Zhi-Hui ZHAN; Dong LIU; Sam KWONG; Jun ZHANG

doi:10.1109/SMC42975.2020.9282962

Particle Swarm Optimization with Hybrid Ring Topology for Multimodal Optimization Problems

Zong-Gan CHEN, Zhi-Hui ZHAN, Dong LIU, Sam KWONG, Jun ZHANG

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

9 Citations (Scopus)

Abstract

Multimodal optimization problems (MMOPs) require the algorithm to locate multiple global optima and also achieve a certain accuracy on the found optima. When applying particle swarm optimization (PSO) to solve MMOPs, a fixed population communication topology may not be sufficient to handle these two requirements simultaneously. In this paper, a novel PSO with hybrid ring topology, termed HRTPSO, is proposed for MMOPs. In the early evolutionary process of HRTPSO, a sparse topology is constructed to enhance the population diversity to help locate multiple optima, while in the later evolutionary process of HRTPSO, the population communication topology is switched to a relatively dense topology for improving the convergence efficiency on the found optima. The switch of topology is controlled by a threshold and its effect is also analyzed in this paper. Experimental results on the 20 multimodal functions in CEC'2013 benchmark set show that HRTPSO has better performance than the other six multimodal optimization algorithms.

Original language	English
Title of host publication	2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Publisher	IEEE
Pages	2044-2049
Number of pages	6
ISBN (Electronic)	9781728185262
ISBN (Print)	9781728185279
DOIs	https://doi.org/10.1109/SMC42975.2020.9282962
Publication status	Published - Oct 2020
Externally published	Yes
Event	2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada Duration: 11 Oct 2020 → 14 Oct 2020

Conference

Conference	2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
Country/Territory	Canada
City	Toronto
Period	11/10/20 → 14/10/20

Bibliographical note

This work was supported in part by the Key-Area Research and Development of Guangdong Province under Grant 2020B010166002, the National Key Research and Development Program of China under Grant 2019YFB2102102, the Outstanding Youth Science Foundation under Grant 61822602, the National Natural Science Foundations of China under Grant 61772207 and Grant 61873097, the Guangdong Natural Science Foundation Research Team under Grant 2018B030312003 and 2018B050502006, the Ministry of Science and ICT through the National Research Foundation of Korea (NRF-2019H1D3A2A01101977), and the Hong Kong GRF-RGC General Research Fund (9042489) under Grant CityU 11206317.

Keywords

hybrid ring topology
multimodal optimization problems
Particle swarm optimization

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10.1109/SMC42975.2020.9282962

Cite this

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title = "Particle Swarm Optimization with Hybrid Ring Topology for Multimodal Optimization Problems",

abstract = "Multimodal optimization problems (MMOPs) require the algorithm to locate multiple global optima and also achieve a certain accuracy on the found optima. When applying particle swarm optimization (PSO) to solve MMOPs, a fixed population communication topology may not be sufficient to handle these two requirements simultaneously. In this paper, a novel PSO with hybrid ring topology, termed HRTPSO, is proposed for MMOPs. In the early evolutionary process of HRTPSO, a sparse topology is constructed to enhance the population diversity to help locate multiple optima, while in the later evolutionary process of HRTPSO, the population communication topology is switched to a relatively dense topology for improving the convergence efficiency on the found optima. The switch of topology is controlled by a threshold and its effect is also analyzed in this paper. Experimental results on the 20 multimodal functions in CEC'2013 benchmark set show that HRTPSO has better performance than the other six multimodal optimization algorithms.",

keywords = "hybrid ring topology, multimodal optimization problems, Particle swarm optimization",

author = "Zong-Gan CHEN and Zhi-Hui ZHAN and Dong LIU and Sam KWONG and Jun ZHANG",

note = "This work was supported in part by the Key-Area Research and Development of Guangdong Province under Grant 2020B010166002, the National Key Research and Development Program of China under Grant 2019YFB2102102, the Outstanding Youth Science Foundation under Grant 61822602, the National Natural Science Foundations of China under Grant 61772207 and Grant 61873097, the Guangdong Natural Science Foundation Research Team under Grant 2018B030312003 and 2018B050502006, the Ministry of Science and ICT through the National Research Foundation of Korea (NRF-2019H1D3A2A01101977), and the Hong Kong GRF-RGC General Research Fund (9042489) under Grant CityU 11206317.; 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 ; Conference date: 11-10-2020 Through 14-10-2020",

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doi = "10.1109/SMC42975.2020.9282962",

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booktitle = "2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)",

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CHEN, Z-G, ZHAN, Z-H, LIU, D, KWONG, S & ZHANG, J 2020, Particle Swarm Optimization with Hybrid Ring Topology for Multimodal Optimization Problems. in 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp. 2044-2049, 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020, Toronto, Canada, 11/10/20. https://doi.org/10.1109/SMC42975.2020.9282962

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T1 - Particle Swarm Optimization with Hybrid Ring Topology for Multimodal Optimization Problems

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AU - ZHAN, Zhi-Hui

AU - LIU, Dong

AU - KWONG, Sam

AU - ZHANG, Jun

N1 - This work was supported in part by the Key-Area Research and Development of Guangdong Province under Grant 2020B010166002, the National Key Research and Development Program of China under Grant 2019YFB2102102, the Outstanding Youth Science Foundation under Grant 61822602, the National Natural Science Foundations of China under Grant 61772207 and Grant 61873097, the Guangdong Natural Science Foundation Research Team under Grant 2018B030312003 and 2018B050502006, the Ministry of Science and ICT through the National Research Foundation of Korea (NRF-2019H1D3A2A01101977), and the Hong Kong GRF-RGC General Research Fund (9042489) under Grant CityU 11206317.

PY - 2020/10

Y1 - 2020/10

N2 - Multimodal optimization problems (MMOPs) require the algorithm to locate multiple global optima and also achieve a certain accuracy on the found optima. When applying particle swarm optimization (PSO) to solve MMOPs, a fixed population communication topology may not be sufficient to handle these two requirements simultaneously. In this paper, a novel PSO with hybrid ring topology, termed HRTPSO, is proposed for MMOPs. In the early evolutionary process of HRTPSO, a sparse topology is constructed to enhance the population diversity to help locate multiple optima, while in the later evolutionary process of HRTPSO, the population communication topology is switched to a relatively dense topology for improving the convergence efficiency on the found optima. The switch of topology is controlled by a threshold and its effect is also analyzed in this paper. Experimental results on the 20 multimodal functions in CEC'2013 benchmark set show that HRTPSO has better performance than the other six multimodal optimization algorithms.

AB - Multimodal optimization problems (MMOPs) require the algorithm to locate multiple global optima and also achieve a certain accuracy on the found optima. When applying particle swarm optimization (PSO) to solve MMOPs, a fixed population communication topology may not be sufficient to handle these two requirements simultaneously. In this paper, a novel PSO with hybrid ring topology, termed HRTPSO, is proposed for MMOPs. In the early evolutionary process of HRTPSO, a sparse topology is constructed to enhance the population diversity to help locate multiple optima, while in the later evolutionary process of HRTPSO, the population communication topology is switched to a relatively dense topology for improving the convergence efficiency on the found optima. The switch of topology is controlled by a threshold and its effect is also analyzed in this paper. Experimental results on the 20 multimodal functions in CEC'2013 benchmark set show that HRTPSO has better performance than the other six multimodal optimization algorithms.

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DO - 10.1109/SMC42975.2020.9282962

M3 - Conference paper (refereed)

SN - 9781728185279

SP - 2044

EP - 2049

BT - 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

PB - IEEE

T2 - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020

Y2 - 11 October 2020 through 14 October 2020

ER -

Particle Swarm Optimization with Hybrid Ring Topology for Multimodal Optimization Problems

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Keywords

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