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Benchmarking continuous dynamic optimization : survey and generalized test suite
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Yazdani, Danial, Omidvar, Mohammad Nabi, Cheng, Ran, Branke, Jürgen, Nguyen, Trung Thanh and Yao, Xin (2022) Benchmarking continuous dynamic optimization : survey and generalized test suite. IEEE Transactions on Cybernetics, 52 (5). 3380 -3393. doi:10.1109/TCYB.2020.3011828 ISSN 2168-2267.
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Official URL: http://dx.doi.org/10.1109/TCYB.2020.3011828
Abstract
Dynamic changes are an important and inescapable aspect of many real-world optimization problems. Designing algorithms to find and track desirable solutions while facing challenges of dynamic optimization problems is an active research topic in the field of swarm and evolutionary computation. To evaluate and compare the performance of algorithms, it is imperative to use a suitable benchmark that generates problem instances with different controllable characteristics. In this article, we give a comprehensive review of existing benchmarks and investigate their shortcomings in capturing different problem features. We then propose a highly configurable benchmark suite, the generalized moving peaks benchmark, capable of generating problem instances whose components have a variety of properties, such as different levels of ill-conditioning, variable interactions, shape, and complexity. Moreover, components generated by the proposed benchmark can be highly dynamic with respect to the gradients, heights, optimum locations, condition numbers, shapes, complexities, and variable interactions. Finally, several well-known optimizers and dynamic optimization algorithms are chosen to solve generated problems by the proposed benchmark. The experimental results show the poor performance of the existing methods in facing new challenges posed by the addition of new properties.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||
| Divisions: | Faculty of Social Sciences > Warwick Business School | ||||||||
| Library of Congress Subject Headings (LCSH): | Mathematical optimization -- Computer programs, Dynamics, Evolutionary computation, Computer algorithms, Computer science -- Mathematics | ||||||||
| Journal or Publication Title: | IEEE Transactions on Cybernetics | ||||||||
| Publisher: | IEEE Computer Society | ||||||||
| ISSN: | 2168-2267 | ||||||||
| Official Date: | May 2022 | ||||||||
| Dates: |
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| Volume: | 52 | ||||||||
| Number: | 5 | ||||||||
| Page Range: | 3380 -3393 | ||||||||
| DOI: | 10.1109/TCYB.2020.3011828 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | This article is protected by copyright. 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: | 28 July 2020 | ||||||||
| Date of first compliant Open Access: | 30 July 2020 | ||||||||
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