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Erschienen in: Soft Computing 12/2019

12.03.2018 | Methodologies and Application

Accelerating differential evolution based on a subset-to-subset survivor selection operator

verfasst von: Jinglei Guo, Zhijian Li, Shengxiang Yang

Erschienen in: Soft Computing | Ausgabe 12/2019

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Abstract

Differential evolution (DE) is one of the most powerful and effective evolutionary algorithms for solving global optimization problems. However, just like all other metaheuristics, DE also has some drawbacks, such as slow and/or premature convergence. This paper proposes a new subset-to-subset selection operator to improve the convergence performance of DE by randomly dividing target and trial populations into several subsets and employing the ranking-based selection operator among corresponding subsets. The proposed framework gives more survival opportunities to trial vectors with better objective function values. Experimental results show that the proposed method significantly improves the performance of the original DE algorithm and several state-of-the-art DE variants on a series of benchmark functions.

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Metadaten
Titel
Accelerating differential evolution based on a subset-to-subset survivor selection operator
verfasst von
Jinglei Guo
Zhijian Li
Shengxiang Yang
Publikationsdatum
12.03.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 12/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3060-x

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