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Adaptive control of sub-populations in evolutionary dynamic optimization
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Yazdani, D., Cheng, R. and Branke, Jürgen (2022) Adaptive control of sub-populations in evolutionary dynamic optimization. IEEE Transactions on Cybernetics, 52 (7). pp. 6476-6489. doi:10.1109/TCYB.2020.3036100
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WRAP-adaptive-control-sub-populations-evolutionary-dynamic-optimization-Branke-2020.pdf - Accepted Version - Requires a PDF viewer. Download (1043Kb) | Preview |
Official URL: https://doi.org/10.1109/TCYB.2020.3036100
Abstract
Multi-population methods are highly effective in solving dynamic optimization problems. Three factors affect this significantly: the exclusion mechanisms to avoid the convergence to the same peak by multiple sub-populations, the resource allocation mechanism which assigns the computational resources to the sub-populations, and the control mechanisms to adaptively adjust the number of sub-populations by considering the number of optima and available computational resources. In the existing exclusion mechanisms, when the distance (i.e. the distance between their best found positions) between two sub-populations becomes less than a predefined threshold, the inferior one will be removed/reinitialized. However, this leads to incapability of algorithms in covering peaks/optima that are closer than the threshold. Moreover, despite the importance of resource allocation due to the limited available computational resources between environmental changes, it has not been well studied in the literature. Finally, the number of sub-populations should be adapted to the number of optima. However, in most existing adaptive multi-population methods, there is no predefined upper bound for generating sub-populations. Consequently, in problems with large numbers of peaks, they can generate too many subpopulations sharing limited computational resources. In this paper, a multi-population framework is proposed to address the aforementioned issues by using three adaptive approaches: subpopulation generation, double-layer exclusion, and computational resource allocation. The experimental results demonstrate the superiority of the proposed framework over several peer approaches in solving various benchmark problems.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery |
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| Divisions: | Faculty of Social Sciences > Warwick Business School | ||||||||
| Library of Congress Subject Headings (LCSH): | Adaptive control systems, Mathematical optimization -- Data processing, Evolutionary computation , Computational intelligence | ||||||||
| Journal or Publication Title: | IEEE Transactions on Cybernetics | ||||||||
| Publisher: | IEEE Computer Society | ||||||||
| ISSN: | 2168-2267 | ||||||||
| Official Date: | July 2022 | ||||||||
| Dates: |
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| Volume: | 52 | ||||||||
| Number: | 7 | ||||||||
| Page Range: | pp. 6476-6489 | ||||||||
| DOI: | 10.1109/TCYB.2020.3036100 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | © 2020 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. | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
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