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A survey of evolutionary continuous dynamic optimization over two decades – part A
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Yazdani, Danial , Cheng, Ran, Yazdani, Donya, Branke, Jürgen, Jin, Yaochu and Yao, Xin (2021) A survey of evolutionary continuous dynamic optimization over two decades – part A. IEEE Transactions on Evolutionary Computation, 25 (4). pp. 609-629. doi:10.1109/TEVC.2021.3060014
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Official URL: http://dx.doi.org/10.1109/TEVC.2021.3060014
Abstract
Many real-world optimization problems are dynamic. The field of dynamic optimization deals with such problems where the search space changes over time. In this two-part paper, we present a comprehensive survey of the research in evolutionary dynamic optimization for single-objective unconstrained continuous problems over the last two decades. In Part A of this survey, we propose a new taxonomy for the components of dynamic optimization algorithms, namely, convergence detection, change detection, explicit archiving, diversity control, and population division and management. In comparison to the existing taxonomies, the proposed taxonomy covers some additional important components, such as convergence detection and computational resource allocation. Moreover, we significantly expand and improve the classifications of diversity control and multi-population methods, which are under-represented in the existing taxonomies. We then provide detailed technical descriptions and analysis of different components according to the suggested taxonomy. Part B of this survey provides an indepth analysis of the most commonly used benchmark problems, performance analysis methods, static optimization algorithms used as the optimization components in the dynamic optimization algorithms, and dynamic real-world applications. Finally, several opportunities for future work are pointed out.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > QA Mathematics T Technology > TA Engineering (General). Civil engineering (General) |
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| Divisions: | Faculty of Social Sciences > Warwick Business School > Operational Research & Management Sciences Faculty of Social Sciences > Warwick Business School |
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| Library of Congress Subject Headings (LCSH): | Structural optimization , Mathematical optimization -- Data processing, Evolutionary computation, Genetic algorithms | ||||||||
| Journal or Publication Title: | IEEE Transactions on Evolutionary Computation | ||||||||
| Publisher: | IEEE | ||||||||
| ISSN: | 1089-778X | ||||||||
| Official Date: | August 2021 | ||||||||
| Dates: |
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| Volume: | 25 | ||||||||
| Number: | 4 | ||||||||
| Page Range: | pp. 609-629 | ||||||||
| DOI: | 10.1109/TEVC.2021.3060014 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Publisher Statement: | © 2021 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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