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Improved Constructive Cooperative Coevolutionary Differential Evolution for Large-Scale Optimisation
University West, Department of Engineering Science, Division of Automation Systems. (PTW)ORCID iD: 0000-0002-0044-2795
University West, Department of Engineering Science, Division of Automation Systems. (PTW)ORCID iD: 0000-0002-8878-2718
University West, Department of Engineering Science, Division of Automation Systems. (PTW)ORCID iD: 0000-0002-6604-6904
University West, Department of Engineering Science, Division of Automation Systems. University West, Department of Engineering Science, Division of Production Systems. Department of Signals and systems, Chalmers University of Technology. (PTW)
2016 (English)In: Computational Intelligence, 2015 IEEE Symposium Series on, IEEE, 2016, p. 1703-1710, article id 7376815Conference paper, Published paper (Refereed)
Abstract [en]

The Differential Evolution (DE) algorithm is widely used for real-world global optimisation problems in many different domains. To improve DE's performance on large-scale optimisation problems, it has been combined with the Cooperative Coevolution (CCDE) algorithm. CCDE adopts a divide-and-conquer strategy to optimise smaller subcomponents separately instead of tackling the large-scale problem at once. DE then evolves a separate subpopulation for each subcomponent but there is cooperation between the subpopulations to co-adapt the individuals of the subpopulations with each other. The Constructive Cooperative Coevolution (C3DE) algorithm, previously proposed by the authors, is an extended version of CCDE that has a better performance on large-scale problems, interestingly also on non-separable problems. This paper proposes a new version, called the Improved Constructive Cooperative Coevolutionary Differential Evolution (C3iDE), which removes several limitations with the previous version. A novel element of C3iDE is the advanced initialisation of the subpopulations. C3iDE initially optimises the subpopulations in a partially co-adaptive fashion. During the initial optimisation of a subpopulation, only a subset of the other subcomponents is considered for the co-adaptation. This subset increases stepwise until all subcomponents are considered. The experimental evaluation of C3iDE on 36 high-dimensional benchmark functions (up to 1000 dimensions) shows an improved solution quality on large-scale global optimisation problems compared to CCDE and DE. The greediness of the co-adaptation with C3iDE is also investigated in this paper.

Place, publisher, year, edition, pages
IEEE, 2016. p. 1703-1710, article id 7376815
Keywords [en]
Benchmark testing Collaboration Complexity theory, Evolutionary computation, Optimization Partitioning, algorithms
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production Technology; ENGINEERING, Manufacturing and materials engineering
Identifiers
URN: urn:nbn:se:hv:diva-8900DOI: 10.1109/SSCI.2015.239Scopus ID: 2-s2.0-84964940225ISBN: 978-1-4799-7560-0 (print)OAI: oai:DiVA.org:hv-8900DiVA, id: diva2:894995
Conference
2015 IEEE Symposium on Computational Intelligence SSCI 8-10 December 2015 Cape Town, South Africa
Available from: 2016-01-18 Created: 2016-01-18 Last updated: 2019-12-03Bibliographically approved

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Glorieux, EmileSvensson, BoDanielsson, FredrikLennartson, Bengt

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