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Constructive cooperative coevolution for large-scale global optimisation
University West, Department of Engineering Science, Division of Production System. (PTW)ORCID iD: 0000-0002-0044-2795
University West, Department of Engineering Science, Division of Production System. (PTW)ORCID iD: 0000-0002-8878-2718
University West, Department of Engineering Science, Division of Production System. (PTW)ORCID iD: 0000-0002-6604-6904
University West, Department of Engineering Science, Division of Production System. Department of Signals and Systems, Chalmers University of Technology, S-412 96 Gothenburg, Sweden. (PTW)
2017 (English)In: Journal of Heuristics, ISSN 1381-1231, E-ISSN 1572-9397, 1-21 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents the Constructive Cooperative Coevolutionary ( C3C3 ) algorithm, applied to continuous large-scale global optimisation problems. The novelty of C3C3 is that it utilises a multi-start architecture and incorporates the Cooperative Coevolutionary algorithm. The considered optimisation problem is decomposed into subproblems. An embedded optimisation algorithm optimises the subproblems separately while exchanging information to co-adapt the solutions for the subproblems. Further, C3C3 includes a novel constructive heuristic that generates different feasible solutions for the entire problem and thereby expedites the search. In this work, two different versions of C3C3 are evaluated on high-dimensional benchmark problems, including the CEC'2013 test suite for large-scale global optimisation. C3C3 is compared with several state-of-the-art algorithms, which shows that C3C3 is among the most competitive algorithms. C3C3 outperforms the other algorithms for most partially separable functions and overlapping functions. This shows that C3C3 is an effective algorithm for large-scale global optimisation. This paper demonstrates the enhanced performance by using constructive heuristics for generating initial feasible solutions for Cooperative Coevolutionary algorithms in a multi-start framework.

Place, publisher, year, edition, pages
Boston: Kluwer Academic Publishers, 2017. 1-21 p.
Keyword [en]
Evolutionary optimisation, Cooperative coevolution, Algorithm design and analysis, Large-scale optimisation
National Category
Robotics
Research subject
Production Technology
Identifiers
URN: urn:nbn:se:hv:diva-11264DOI: 10.1007/s10732-017-9351-zOAI: oai:DiVA.org:hv-11264DiVA: diva2:1129360
Funder
Region Västra Götaland, PROSAM 612-0974-14
Available from: 2017-08-02 Created: 2017-08-02 Last updated: 2017-08-02Bibliographically approved

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Glorieux, EmileSvensson, BoDanielsson, FredrikLennartson, Bengt
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CiteExportLink to record
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