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The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection

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  • Petr Bujok

    (Department of Informatics and Computers, Faculty of Science, University of Ostrava, 30. Dubna 22, 70103 Ostrava, Czech Republic)

Abstract

This paper proposes the real-world application of the Differential Evolution (DE) algorithm using, distance-based mutation-selection, population size adaptation, and an archive for solutions (DEDMNA). This simple framework uses three widely-used mutation types with the application of binomial crossover. For each solution, the most proper position prior to evaluation is selected using the Euclidean distances of three newly generated positions. Moreover, an efficient linear population-size reduction mechanism is employed. Furthermore, an archive of older efficient solutions is used. The DEDMNA algorithm is applied to three real-life engineering problems and 13 constrained problems. Seven well-known state-of-the-art DE algorithms are used to compare the efficiency of DEDMNA. The performance of DEDMNA and other algorithms are comparatively assessed using statistical methods. The results obtained show that DEDMNA is a very comparable optimiser compared to the best performing DE variants. The simple idea of measuring the distance of the mutant solutions increases the performance of DE significantly.

Suggested Citation

  • Petr Bujok, 2021. "The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection," Mathematics, MDPI, vol. 9(16), pages 1-15, August.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:16:p:1909-:d:612038
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    References listed on IDEAS

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    1. Nicola Bellomo & Giovanni Dosi & Damian A. Knopoff & Maria Enrica Virgillito, 2020. "From particles to firms: a kinetic model of climbing up evolutionary landscapes," LEM Papers Series 2020/04, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    2. Giovanni Dosi & Andrea Roventini, 2019. "More is different ... and complex! the case for agent-based macroeconomics," Journal of Evolutionary Economics, Springer, vol. 29(1), pages 1-37, March.
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