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A supervised parallel optimisation framework for metaheuristic algorithms

Eugenio Muttio Zavala, Wulf Dettmer Orcid Logo, Jac Clarke, Djordje Peric Orcid Logo, Zhaoxin Ren Orcid Logo, Lloyd Fletcher Orcid Logo

Swarm and Evolutionary Computation, Volume: 84, Start page: 101445

Swansea University Authors: Eugenio Muttio Zavala, Wulf Dettmer Orcid Logo, Jac Clarke, Djordje Peric Orcid Logo, Zhaoxin Ren Orcid Logo

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DOI (Published version): 10.1016/j.swevo.2023.101445

Abstract

A Supervised Parallel Optimisation (SPO) is presented. The proposed framework couples different optimisation algorithms to solve single-objective optimisation problems. The supervision balances the exploration and exploitation capabilities of the distinct optimisers included, providing a general fra...

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Published in: Swarm and Evolutionary Computation
ISSN: 2210-6502 2210-6510
Published: Elsevier BV 2024
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The good solutions generated by SPO are shown to be generally reproducible, while isolated algorithms, at best, render good solutions only occasionally.</abstract><type>Journal Article</type><journal>Swarm and Evolutionary Computation</journal><volume>84</volume><journalNumber/><paginationStart>101445</paginationStart><paginationEnd/><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>2210-6502</issnPrint><issnElectronic>2210-6510</issnElectronic><keywords>Optimisation, Parallel computation, Metaheuristics, Population-based algorithms</keywords><publishedDay>1</publishedDay><publishedMonth>2</publishedMonth><publishedYear>2024</publishedYear><publishedDate>2024-02-01</publishedDate><doi>10.1016/j.swevo.2023.101445</doi><url/><notes/><college>COLLEGE NANME</college><department>Science and Engineering - Faculty</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>FGSEN</DepartmentCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders>Eugenio J. 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spelling v2 65251 2023-12-07 A supervised parallel optimisation framework for metaheuristic algorithms ee7320f4fba56d3fc7eea1bcdd28e615 Eugenio Muttio Zavala Eugenio Muttio Zavala true false 30bb53ad906e7160e947fa01c16abf55 0000-0003-0799-4645 Wulf Dettmer Wulf Dettmer true false e1479e5768c270417e8a2cb734295626 Jac Clarke Jac Clarke true false 9d35cb799b2542ad39140943a9a9da65 0000-0002-1112-301X Djordje Peric Djordje Peric true false 62a1a0da0fa78e05c3deafcdee5551ce 0000-0002-6305-9515 Zhaoxin Ren Zhaoxin Ren true false 2023-12-07 FGSEN A Supervised Parallel Optimisation (SPO) is presented. The proposed framework couples different optimisation algorithms to solve single-objective optimisation problems. The supervision balances the exploration and exploitation capabilities of the distinct optimisers included, providing a general framework to solve problems with diverse characteristics. In this work, five optimisation algorithms are included in the ensemble: Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), Covariance Matrix Adaption - Evolution Strategy (CMA-ES), Differential Evolution (DE), and Modified Cuckoo Search (MCS). A geometric path-finding problem with numerous local minima is used to demonstrate the advantage of SPO. The effectiveness of the approach is compared with that of stand-alone incidences of the integrated optimisation strategies and with state-of-the-art algorithms. In addition, a benchmark test suit composed of engineering applications is utilised to validate the general applicability of SPO with respect to a variety of problems. The good solutions generated by SPO are shown to be generally reproducible, while isolated algorithms, at best, render good solutions only occasionally. Journal Article Swarm and Evolutionary Computation 84 101445 Elsevier BV 2210-6502 2210-6510 Optimisation, Parallel computation, Metaheuristics, Population-based algorithms 1 2 2024 2024-02-01 10.1016/j.swevo.2023.101445 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University SU Library paid the OA fee (TA Institutional Deal) Eugenio J. Muttio gratefully acknowledges research support provided by UKAEA and EPSRC through the Doctoral Training Partnership (DTP) scheme. This work has been part-funded by the EPSRC Energy Programme [grant number EP/W006839/1]. We acknowledge the support of Supercomputing Wales and AccelerateAI projects, which are part-funded by the European Regional Development Fund (ERDF) via the Welsh Government . 2024-04-10T11:21:37.7721851 2023-12-07T22:11:30.4426411 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering Eugenio Muttio Zavala 1 Wulf Dettmer 0000-0003-0799-4645 2 Jac Clarke 3 Djordje Peric 0000-0002-1112-301X 4 Zhaoxin Ren 0000-0002-6305-9515 5 Lloyd Fletcher 0000-0003-2841-8030 6 65251__29975__12ed03d2ad8748b98a1c3186e956472a.pdf 65251.VOR.pdf 2024-04-10T11:20:11.5170866 Output 2404288 application/pdf Version of Record true © 2023 The Authors. 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 supervised parallel optimisation framework for metaheuristic algorithms
spellingShingle A supervised parallel optimisation framework for metaheuristic algorithms
Eugenio Muttio Zavala
Wulf Dettmer
Jac Clarke
Djordje Peric
Zhaoxin Ren
title_short A supervised parallel optimisation framework for metaheuristic algorithms
title_full A supervised parallel optimisation framework for metaheuristic algorithms
title_fullStr A supervised parallel optimisation framework for metaheuristic algorithms
title_full_unstemmed A supervised parallel optimisation framework for metaheuristic algorithms
title_sort A supervised parallel optimisation framework for metaheuristic algorithms
author_id_str_mv ee7320f4fba56d3fc7eea1bcdd28e615
30bb53ad906e7160e947fa01c16abf55
e1479e5768c270417e8a2cb734295626
9d35cb799b2542ad39140943a9a9da65
62a1a0da0fa78e05c3deafcdee5551ce
author_id_fullname_str_mv ee7320f4fba56d3fc7eea1bcdd28e615_***_Eugenio Muttio Zavala
30bb53ad906e7160e947fa01c16abf55_***_Wulf Dettmer
e1479e5768c270417e8a2cb734295626_***_Jac Clarke
9d35cb799b2542ad39140943a9a9da65_***_Djordje Peric
62a1a0da0fa78e05c3deafcdee5551ce_***_Zhaoxin Ren
author Eugenio Muttio Zavala
Wulf Dettmer
Jac Clarke
Djordje Peric
Zhaoxin Ren
author2 Eugenio Muttio Zavala
Wulf Dettmer
Jac Clarke
Djordje Peric
Zhaoxin Ren
Lloyd Fletcher
format Journal article
container_title Swarm and Evolutionary Computation
container_volume 84
container_start_page 101445
publishDate 2024
institution Swansea University
issn 2210-6502
2210-6510
doi_str_mv 10.1016/j.swevo.2023.101445
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 Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering
document_store_str 1
active_str 0
description A Supervised Parallel Optimisation (SPO) is presented. The proposed framework couples different optimisation algorithms to solve single-objective optimisation problems. The supervision balances the exploration and exploitation capabilities of the distinct optimisers included, providing a general framework to solve problems with diverse characteristics. In this work, five optimisation algorithms are included in the ensemble: Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), Covariance Matrix Adaption - Evolution Strategy (CMA-ES), Differential Evolution (DE), and Modified Cuckoo Search (MCS). A geometric path-finding problem with numerous local minima is used to demonstrate the advantage of SPO. The effectiveness of the approach is compared with that of stand-alone incidences of the integrated optimisation strategies and with state-of-the-art algorithms. In addition, a benchmark test suit composed of engineering applications is utilised to validate the general applicability of SPO with respect to a variety of problems. The good solutions generated by SPO are shown to be generally reproducible, while isolated algorithms, at best, render good solutions only occasionally.
published_date 2024-02-01T11:21:34Z
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