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A new moving peaks benchmark with attractors for dynamic evolutionary algorithms

Matthew Fox, Shengxiang Yang Orcid Logo, Fabio Caraffini Orcid Logo

Swarm and Evolutionary Computation, Volume: 74, Start page: 101125

Swansea University Author: Fabio Caraffini Orcid Logo

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DOI (Published version): 10.1016/j.swevo.2022.101125

Abstract

Prediction in evolutionary dynamic optimization (EDO), such as predicting the movement of optima, or when and how an environment will change, is a topic that is still under investigation and presents unsolved challenges. A few studies approach prediction based on re-initialising a population or requ...

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Published in: Swarm and Evolutionary Computation
ISSN: 2210-6502
Published: Elsevier BV 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa60896
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spelling 2024-07-11T14:08:07.1266540 v2 60896 2022-08-28 A new moving peaks benchmark with attractors for dynamic evolutionary algorithms d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2022-08-28 MACS Prediction in evolutionary dynamic optimization (EDO), such as predicting the movement of optima, or when and how an environment will change, is a topic that is still under investigation and presents unsolved challenges. A few studies approach prediction based on re-initialising a population or requirement satisfaction problems such as Robust Optimization Over Time. The benchmark problems in these studies inherently use randomly changing parameters and therefore such randomness may make it difficult to compare these algorithms with other EDO approaches. In this paper, we introduce a new benchmark, called Moving Peaks Benchmark with Attractors, which incorporates an attractor heuristic that attracts peaks to a certain location in the environment into the moving peaks problem. The proposed benchmark is fully flexible where the dynamics of the attractors and the rate at which a peak is attracted to such attractors can be modified. By adjusting these characteristics, certain styles of movements can be achieved by a peak. We also introduce a new performance measure that focuses on the comparison of algorithms that use prediction. Seven EDO algorithms based on different working logics are chosen to give a wide representation of the state-of-the-art in this area. We argue that having predictable characteristics in the benchmark problem is more adequate for studying the performances and behaviours of those algorithms that embed prediction mechanisms. Experimental results obtained with the proposed benchmark show it’s suitability for the EDO domain as all algorithms featuring prediction capabilities display higher accuracy than their competitors. Journal Article Swarm and Evolutionary Computation 74 101125 Elsevier BV 2210-6502 Benchmark; Attractor; Evolutionary dynamic optimization; Evolutionary algorithm; Performance metric 1 10 2022 2022-10-01 10.1016/j.swevo.2022.101125 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Another institution paid the OA fee 2024-07-11T14:08:07.1266540 2022-08-28T18:47:35.6782601 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Matthew Fox 1 Shengxiang Yang 0000-0001-7222-4917 2 Fabio Caraffini 0000-0001-9199-7368 3 60896__25202__f898f6447c944691abfbbf7c6a0fc51e.pdf 60896_VoR.pdf 2022-09-23T12:43:04.3451886 Output 2108495 application/pdf Version of Record true © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license true eng http://creativecommons.org/licenses/by/4.0/
title A new moving peaks benchmark with attractors for dynamic evolutionary algorithms
spellingShingle A new moving peaks benchmark with attractors for dynamic evolutionary algorithms
Fabio Caraffini
title_short A new moving peaks benchmark with attractors for dynamic evolutionary algorithms
title_full A new moving peaks benchmark with attractors for dynamic evolutionary algorithms
title_fullStr A new moving peaks benchmark with attractors for dynamic evolutionary algorithms
title_full_unstemmed A new moving peaks benchmark with attractors for dynamic evolutionary algorithms
title_sort A new moving peaks benchmark with attractors for dynamic evolutionary algorithms
author_id_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author Fabio Caraffini
author2 Matthew Fox
Shengxiang Yang
Fabio Caraffini
format Journal article
container_title Swarm and Evolutionary Computation
container_volume 74
container_start_page 101125
publishDate 2022
institution Swansea University
issn 2210-6502
doi_str_mv 10.1016/j.swevo.2022.101125
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Prediction in evolutionary dynamic optimization (EDO), such as predicting the movement of optima, or when and how an environment will change, is a topic that is still under investigation and presents unsolved challenges. A few studies approach prediction based on re-initialising a population or requirement satisfaction problems such as Robust Optimization Over Time. The benchmark problems in these studies inherently use randomly changing parameters and therefore such randomness may make it difficult to compare these algorithms with other EDO approaches. In this paper, we introduce a new benchmark, called Moving Peaks Benchmark with Attractors, which incorporates an attractor heuristic that attracts peaks to a certain location in the environment into the moving peaks problem. The proposed benchmark is fully flexible where the dynamics of the attractors and the rate at which a peak is attracted to such attractors can be modified. By adjusting these characteristics, certain styles of movements can be achieved by a peak. We also introduce a new performance measure that focuses on the comparison of algorithms that use prediction. Seven EDO algorithms based on different working logics are chosen to give a wide representation of the state-of-the-art in this area. We argue that having predictable characteristics in the benchmark problem is more adequate for studying the performances and behaviours of those algorithms that embed prediction mechanisms. Experimental results obtained with the proposed benchmark show it’s suitability for the EDO domain as all algorithms featuring prediction capabilities display higher accuracy than their competitors.
published_date 2022-10-01T20:14:28Z
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score 11.04748

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