Multi-population Modified L-SHADE for Single Objective Bound Constrained optimization

Yann Chern Jou; Shuo Ying Wang; Jia Fong Yeh; Tsung Che Chiang

doi:10.1109/CEC48606.2020.9185735

Multi-population Modified L-SHADE for Single Objective Bound Constrained optimization

Yann Chern Jou, Shuo Ying Wang, Jia Fong Yeh, Tsung Che Chiang

資訊工程學系

研究成果: 書貢獻/報告類型 › 會議論文篇章

11 引文斯高帕斯（Scopus）

摘要

In this paper, we extend a previous algorithm mL-SHADE by running the evolutionary process through multiple populations and adding dynamic control of mutation intensity and hyper-parameters. The whole population is partitioned into subpopulations by a random clustering method. Mutation intensity and hyper-parameters are adjusted based on the consumption of fitness function evaluations. Performance of the proposed algorithm is verified by ten benchmark functions in the CEC2020 Competition on Single Objective Bound Constrained optimization. The results show the competitiveness of the proposed algorithm.

原文	英語
主出版物標題	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
發行者	Institute of Electrical and Electronics Engineers Inc.
ISBN（電子）	9781728169293
DOIs	https://doi.org/10.1109/CEC48606.2020.9185735
出版狀態	已發佈 - 2020 7月
事件	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Virtual, Glasgow, 英国持續時間: 2020 7月 19 → 2020 7月 24

出版系列

名字	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

會議

會議	2020 IEEE Congress on Evolutionary Computation, CEC 2020
國家/地區	英国
城市	Virtual, Glasgow
期間	2020/07/19 → 2020/07/24

ASJC Scopus subject areas

控制和優化
決策科學（雜項）
人工智慧
電腦視覺和模式識別
硬體和架構

存取文件

10.1109/CEC48606.2020.9185735

其它文件與鏈接

引用此

Jou, Y. C., Wang, S. Y., Yeh, J. F., & Chiang, T. C. (2020). Multi-population Modified L-SHADE for Single Objective Bound Constrained optimization. 於 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings 文章 9185735 (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC48606.2020.9185735

Multi-population Modified L-SHADE for Single Objective Bound Constrained optimization. / Jou, Yann Chern; Wang, Shuo Ying; Yeh, Jia Fong 等.
2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9185735 (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings).

研究成果: 書貢獻/報告類型 › 會議論文篇章

Jou, YC, Wang, SY, Yeh, JF & Chiang, TC 2020, Multi-population Modified L-SHADE for Single Objective Bound Constrained optimization. 於 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings., 9185735, 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE Congress on Evolutionary Computation, CEC 2020, Virtual, Glasgow, 英国, 2020/07/19. https://doi.org/10.1109/CEC48606.2020.9185735

Jou YC, Wang SY, Yeh JF, Chiang TC. Multi-population Modified L-SHADE for Single Objective Bound Constrained optimization. 於 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9185735. (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings). doi: 10.1109/CEC48606.2020.9185735

Jou, Yann Chern ; Wang, Shuo Ying ; Yeh, Jia Fong 等. / Multi-population Modified L-SHADE for Single Objective Bound Constrained optimization. 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings).

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abstract = "In this paper, we extend a previous algorithm mL-SHADE by running the evolutionary process through multiple populations and adding dynamic control of mutation intensity and hyper-parameters. The whole population is partitioned into subpopulations by a random clustering method. Mutation intensity and hyper-parameters are adjusted based on the consumption of fitness function evaluations. Performance of the proposed algorithm is verified by ten benchmark functions in the CEC2020 Competition on Single Objective Bound Constrained optimization. The results show the competitiveness of the proposed algorithm.",

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AB - In this paper, we extend a previous algorithm mL-SHADE by running the evolutionary process through multiple populations and adding dynamic control of mutation intensity and hyper-parameters. The whole population is partitioned into subpopulations by a random clustering method. Mutation intensity and hyper-parameters are adjusted based on the consumption of fitness function evaluations. Performance of the proposed algorithm is verified by ten benchmark functions in the CEC2020 Competition on Single Objective Bound Constrained optimization. The results show the competitiveness of the proposed algorithm.

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Multi-population Modified L-SHADE for Single Objective Bound Constrained optimization

摘要

出版系列

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ASJC Scopus subject areas

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