Advanced Cauchy Mutation for Differential Evolution in Numerical Optimization

Tae Jong Choi, Julian Togelius, Yun Gyung Cheong

    Research output: Contribution to journalArticlepeer-review


    Among many evolutionary algorithms, differential evolution (DE) has received much attention over the last two decades. DE is a simple yet powerful evolutionary algorithm that has been used successfully to optimize various real-world problems. Since it was introduced, many researchers have developed new methods for DE, and one of them makes use of a mutation based on the Cauchy distribution to increase the convergence speed of DE. The method monitors the results of each individual in the selection operator and performs the Cauchy mutation on consecutively failed individuals, which generates mutant vectors by perturbing the best individual with the Cauchy distribution. Therefore, the method can locate the consecutively failed individuals to new positions close to the best individual. Although this approach is interesting, it fails to take into account establishing a balance between exploration and exploitation. In this paper, we propose a sigmoid based parameter control that alters the failure threshold for performing the Cauchy mutation in a time-varying schedule, which can establish a good ratio between exploration and exploitation. Experiments and comparisons have been done with six conventional and six advanced DE variants on a set of 30 benchmark problems, which indicate that the DE variants assisted by the proposed algorithm are highly competitive, especially for multimodal functions.

    Original languageEnglish (US)
    Article number8950112
    Pages (from-to)8720-8734
    Number of pages15
    JournalIEEE Access
    StatePublished - 2020


    • Artificial intelligence
    • differential evolution
    • evolutionary algorithm
    • numerical optimization

    ASJC Scopus subject areas

    • General Computer Science
    • General Materials Science
    • General Engineering


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