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A survey of evolutionary continuous dynamic optimization over two decades – part B
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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 B. IEEE Transactions on Evolutionary Computation, 25 (4). pp. 630-650. doi:10.1109/TEVC.2021.3060012 ISSN 1089-778X.
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WRAP-survey-evolutionary-continuous-dynamic-optimization-over-two-decades–part B-Branke-2021.pdf - Accepted Version - Requires a PDF viewer. Download (2142Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TEVC.2021.3060012
Abstract
This paper presents the second part of a two-part survey that reviews evolutionary dynamic optimization for singleobjective unconstrained continuous problems over the last two decades. While in the first part we reviewed the components of dynamic optimization algorithms, in this part, we present an indepth review of the most commonly used benchmark problems, performance analysis methods, static optimization methods used in the framework of dynamic optimization algorithms, and realworld applications. Compared to the previous works, this paper provides a new taxonomy for the benchmark problems used in the field based on their baseline functions and dynamics. In addition, this survey classifies the commonly used performance indicators into fitness/error based and efficiency based ones. Different types of plots used in the literature for analyzing the performance and behavior of algorithms are also reviewed. Furthermore, the static optimization algorithms which are modified and utilized in the framework of dynamic optimization algorithms as the optimization components are covered. We then comprehensively review some real-world dynamic problems which are optimized by evolutionary dynamic optimization methods. Finally, some challenges and opportunities are pointed out for future directions.
| 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. 630-650 | ||||||||
| DOI: | 10.1109/TEVC.2021.3060012 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Re-use 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 | ||||||||
| Date of first compliant deposit: | 16 March 2021 | ||||||||
| Date of first compliant Open Access: | 16 March 2021 |
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