Multiple elite individual guided piecewise search-based differential evolution

The differential evolution (DE) algorithm relies mainly on mutation strategy and control parameters' selection. To take full advantage of top elite individuals in terms of fitness and success rates, a new mutation operator is proposed. The control parameters such as scale factor and crossover r...

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Main Authors: Gupta, Shubham, Singh, Shitu, Su, Rong, Gao, Shangce, Bansal, Jagdish Chand
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/166997
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1669972023-05-12T15:40:42Z Multiple elite individual guided piecewise search-based differential evolution Gupta, Shubham Singh, Shitu Su, Rong Gao, Shangce Bansal, Jagdish Chand School of Electrical and Electronic Engineering Science::Mathematics::Applied mathematics::Optimization Control Parameters Differential Evolution The differential evolution (DE) algorithm relies mainly on mutation strategy and control parameters' selection. To take full advantage of top elite individuals in terms of fitness and success rates, a new mutation operator is proposed. The control parameters such as scale factor and crossover rate are tuned based on their success rates recorded over past evolutionary stages. The proposed DE variant, MIDE, performs the evolution in a piecewise manner, i.e., after every predefined evolutionary stages, MIDE adjusts its settings to enrich its diversity skills. The performance of the MIDE is validated on two different sets of benchmarks: CEC 2014 and CEC 2017 (special sessions & competitions on real-parameter single objective optimization) using different performance measures. In the end, MIDE is also applied to solve constrained engineering problems. The efficiency and effectiveness of the MIDE are further confirmed by a set of experiments. Agency for Science, Technology and Research (A*STAR) Submitted/Accepted version This work was supported by the A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund - Pre-Positioning (IAF-PP) (Award A19D6a0053) 2023-05-10T05:40:15Z 2023-05-10T05:40:15Z 2023 Journal Article Gupta, S., Singh, S., Su, R., Gao, S. & Bansal, J. C. (2023). Multiple elite individual guided piecewise search-based differential evolution. IEEE/CAA Journal of Automatica Sinica, 10(1), 135-158. https://dx.doi.org/10.1109/JAS.2023.123018 2329-9266 https://hdl.handle.net/10356/166997 10.1109/JAS.2023.123018 2-s2.0-85147249494 1 10 135 158 en A19D6a0053 IEEE/CAA Journal of Automatica Sinica © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JAS.2023.123018. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Mathematics::Applied mathematics::Optimization
Control Parameters
Differential Evolution
spellingShingle Science::Mathematics::Applied mathematics::Optimization
Control Parameters
Differential Evolution
Gupta, Shubham
Singh, Shitu
Su, Rong
Gao, Shangce
Bansal, Jagdish Chand
Multiple elite individual guided piecewise search-based differential evolution
description The differential evolution (DE) algorithm relies mainly on mutation strategy and control parameters' selection. To take full advantage of top elite individuals in terms of fitness and success rates, a new mutation operator is proposed. The control parameters such as scale factor and crossover rate are tuned based on their success rates recorded over past evolutionary stages. The proposed DE variant, MIDE, performs the evolution in a piecewise manner, i.e., after every predefined evolutionary stages, MIDE adjusts its settings to enrich its diversity skills. The performance of the MIDE is validated on two different sets of benchmarks: CEC 2014 and CEC 2017 (special sessions & competitions on real-parameter single objective optimization) using different performance measures. In the end, MIDE is also applied to solve constrained engineering problems. The efficiency and effectiveness of the MIDE are further confirmed by a set of experiments.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Gupta, Shubham
Singh, Shitu
Su, Rong
Gao, Shangce
Bansal, Jagdish Chand
format Article
author Gupta, Shubham
Singh, Shitu
Su, Rong
Gao, Shangce
Bansal, Jagdish Chand
author_sort Gupta, Shubham
title Multiple elite individual guided piecewise search-based differential evolution
title_short Multiple elite individual guided piecewise search-based differential evolution
title_full Multiple elite individual guided piecewise search-based differential evolution
title_fullStr Multiple elite individual guided piecewise search-based differential evolution
title_full_unstemmed Multiple elite individual guided piecewise search-based differential evolution
title_sort multiple elite individual guided piecewise search-based differential evolution
publishDate 2023
url https://hdl.handle.net/10356/166997
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