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MPADE: An Improved Adaptive Multi-Population Differential Evolution Algorithm Based on JADE
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-3425-3837
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9857-4317
2018 (English)In: 2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE , 2018, p. 1139-1146Conference paper, Published paper (Refereed)
Abstract [en]

JADE is an state-of-the-art adaptive differential evolution algorithm which implements "DE/current-to-pbest" as its mutation strategy, adapts its mutation factor and crossover rate, and uses an optional external archive to keep track of potential removed individuals in previous generations. This paper proposes MPADE, which extends JADE by using a multi-populated approach to solve high dimensional real-parameter optimization problems. This mechanism helps preventing the two well-known problems affecting the differential evolution algorithm performance, which are premature convergence and stagnation. The algorithm was tested using the benchmark functions in IEEE Congress on Evolutionary Computation 2014 test suite. MPADE was compared using Wilcoxon test to JADE algorithm and with other state-of-the-art algorithms that either use a multi-population approach or adapt their parameters. The experimental results show that the proposed new algorithm improves significantly its precursor and it is also suggested that other state-of-the-art algorithms could benefit from the multi-populated based approach.

Place, publisher, year, edition, pages
IEEE , 2018. p. 1139-1146
Keywords [en]
Differential evolution, multi-population, coarse-grained parallel, population topology, adaptive parameter, global optimization, evolutionary optimization
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-41826ISI: 000451175500146Scopus ID: 2-s2.0-85056280928OAI: oai:DiVA.org:mdh-41826DiVA, id: diva2:1273988
Conference
IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)
Available from: 2018-12-27 Created: 2018-12-27 Last updated: 2019-01-04Bibliographically approved

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Leon, MiguelXiong, Ning

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CiteExportLink to record
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  • ieee
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