Journal article 95 views 12 downloads
A supervised parallel optimisation framework for metaheuristic algorithms
,
Jac Clarke,
Djordje Peric ,
Zhaoxin Ren ,
Lloyd Fletcher
Swarm and Evolutionary Computation, Volume: 84, Start page: 101445
Swansea University Authors:
Eugenio Muttio Zavala, Wulf Dettmer , Jac Clarke, Djordje Peric , Zhaoxin Ren
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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...
| Published in: | Swarm and Evolutionary Computation |
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| ISSN: | 2210-6502 2210-6510 |
| Published: |
Elsevier BV
2024
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa65251 |
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| 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 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. |
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| Keywords: |
Optimisation, Parallel computation, Metaheuristics, Population-based algorithms |
| College: |
Faculty of Science and Engineering |
| Funders: |
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 . |
| Start Page: |
101445 |