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The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

Fabio Caraffini Orcid Logo, Giovanni Iacca Orcid Logo

Mathematics, Volume: 8, Issue: 5, Start page: 785

Swansea University Author: Fabio Caraffini Orcid Logo

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DOI (Published version): 10.3390/math8050785

Abstract

We present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as para...

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Published in: Mathematics
ISSN: 2227-7390
Published: MDPI AG 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa60958
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last_indexed 2023-01-13T19:21:28Z
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spelling 2022-09-21T13:37:04.8345556 v2 60958 2022-08-28 The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2022-08-28 SCS We present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including: (1) customised implementations of statistical tests, such as the Wilcoxon rank-sum test and the Holm–Bonferroni procedure, for comparing the performances of optimisation algorithms and automatically generating result tables in PDF and LATEX formats; (2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; (3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation on each testbed function. Moreover, we briefly comment on the current state of the literature in stochastic optimisation and highlight similarities shared by modern metaheuristics inspired by nature. We argue that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them. Journal Article Mathematics 8 5 785 MDPI AG 2227-7390 algorithmic design; metaheuristic optimisation; evolutionary computation; swarm intelligence; memetic computing; parameter tuning; fitness trend; Wilcoxon rank-sum; Holm–Bonferroni; benchmark suite 13 5 2020 2020-05-13 10.3390/math8050785 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University This research received no external funding. 2022-09-21T13:37:04.8345556 2022-08-28T20:48:24.5777381 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Fabio Caraffini 0000-0001-9199-7368 1 Giovanni Iacca 0000-0001-9723-1830 2 60958__25181__f562ab1855d346e880b200b7c299c1e2.pdf 60958_VoR.pdf 2022-09-21T13:36:02.0707800 Output 2485480 application/pdf Version of Record true Copyright: 2020 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng http://creativecommons.org/licenses/by/4.0/
title The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms
spellingShingle The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms
Fabio Caraffini
title_short The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms
title_full The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms
title_fullStr The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms
title_full_unstemmed The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms
title_sort The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms
author_id_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author Fabio Caraffini
author2 Fabio Caraffini
Giovanni Iacca
format Journal article
container_title Mathematics
container_volume 8
container_issue 5
container_start_page 785
publishDate 2020
institution Swansea University
issn 2227-7390
doi_str_mv 10.3390/math8050785
publisher MDPI AG
college_str Faculty of Science and Engineering
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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 We present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including: (1) customised implementations of statistical tests, such as the Wilcoxon rank-sum test and the Holm–Bonferroni procedure, for comparing the performances of optimisation algorithms and automatically generating result tables in PDF and LATEX formats; (2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; (3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation on each testbed function. Moreover, we briefly comment on the current state of the literature in stochastic optimisation and highlight similarities shared by modern metaheuristics inspired by nature. We argue that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them.
published_date 2020-05-13T04:19:30Z
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score 11.017797

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