Adaptive multi-population inflationary differential evolution

Di Carlo, Marilena and Vasile, Massimiliano and Minisci, Edmondo (2019) Adaptive multi-population inflationary differential evolution. Soft Computing. ISSN 1433-7479

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    Abstract

    This paper proposes a multi-population adaptive version of inflationary differential evolution algorithm. Inflationary differential evolution algorithm (IDEA) combines basic differential evolution (DE) with some of the restart and local search mechanisms of Monotonic Basin Hopping (MBH). In the adaptive version presented in this paper, the DE parameters CR and F are automatically adapted together with the size of the local restart bubble and the number of local restarts of MBH. The proposed algorithm implements a simple but effective mechanism to avoid multiple detections of the same local minima. The novel mechanism allows the algorithm to decide whether to start or not a local search. The algorithm has been extensively tested over more than fifty test functions from the competitions of the Congress on Evolutionary Computation (CEC), CEC 2005, CEC 2011 and CEC 2014, and compared against all the algorithms participating in those competitions. For each test function, the paper reports best, worst, median, mean and standard deviation values of the best minimum found by the algorithm. Comparisons with other algorithms participating in the CEC competitions are presented in terms of relative ranking, Wilcoxon tests and success rates. For completeness, the paper presents also the single population adaptive IDEA, that can adapt only CR and F, and shows that this simpler version can outperform the multi-population one if the radius of the restart bubble and the number of restarts are properly chosen.

    Author(s):Di Carlo, Marilena ORCID logoORCID: https://orcid.org/0000-0001-5046-3028, Vasile, Massimiliano ORCID logoORCID: https://orcid.org/0000-0001-8302-6465 and Minisci, Edmondo ORCID logoORCID: https://orcid.org/0000-0001-9951-8528
    Item type:Article
    ID code:68572
    Keywords:global optimisation, differential evolution, multi-population algorithm, adaptive algorithm, Mechanical engineering and machinery, Electronic computers. Computer science, Geometry and Topology, Theoretical Computer Science, Software
    Subjects:Technology > Mechanical engineering and machinery
    Science > Mathematics > Electronic computers. Computer science
    Department:Faculty of Engineering > Mechanical and Aerospace Engineering
    Strategic Research Themes > Ocean, Air and Space
    Depositing user: Pure Administrator
    Date deposited:25 Jun 2019 09:39
    Last modified:21 Jan 2020 06:49
    URI:https://strathprints.strath.ac.uk/id/eprint/68572
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