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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. Department of Signals and systems, Chalmers University of Technology. (PTW)
2016 (English)In: Computational Intelligence, 2015 IEEE Symposium Series on, 2016, 1703-1710 p., 7376815Conference 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
2016. 1703-1710 p., 7376815
Keyword [en]
Benchmark testing Collaboration Complexity theory, Evolutionary computation, Optimization Partitioning, algorithms
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production Technology
Identifiers
URN: urn:nbn:se:hv:diva-8900DOI: 10.1109/SSCI.2015.239ScopusID: 2-s2.0-84964940225ISBN: 978-1-4799-7560-0 (print)OAI: oai:DiVA.org:hv-8900DiVA: 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: 2017-01-02Bibliographically approved

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Glorieux, EmileSvensson, BoDanielsson, FredrikLennartson, Bengt
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