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International Conference on Soft Computing - MENDEL

ICSC-MENDEL 2016: Mendel 2015 pp 103–125Cite as

A Switched Parameter Differential Evolution for Large Scale Global Optimization – Simpler May Be Better

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 378))

Abstract

In this article we present two very simple modifications to Differential Evolution (DE), one of the most competitive evolutionary algorithms of recent interest, to enhance its performance for the high-dimensional numerical functions while still preserving the simplicity of its algorithmic framework. Instead of resorting to complicated parameter adaptation schemes or incorporating additional local search methods, we present a simple strategy where the values of the scale factor (mutation step size) and crossover rate are switched in a uniformly random way between two extreme corners of their feasible ranges for different population members. Also each population member is mutated either by using the DE/rand/1 scheme (where the base vector to be perturbed is a randomly chosen member from the population) or by using the DE/best/1 scheme (where the base vector is the best member of the population). The population member is subjected to that mutation strategy which was responsible for the last successful update at the same population index under consideration. Our experiments based on the benchmark functions proposed for the competitions on large-scale global optimization with bound constraints held under the IEEE CEC (Congress on Evolutionary Computation) 2008 and 2010 competitions indicate that the basic DE algorithm with these simple modifications can indeed achieve very competitive results against the currently best known algorithms.

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Authors and Affiliations

  1. Indian Statistical Institute, 203 B. T. Road, Kolkata, 700 108, India

    Swagatam Das, Arka Ghosh & Sankha Subhra Mullick

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  1. Swagatam Das
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  2. Arka Ghosh
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  3. Sankha Subhra Mullick
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Correspondence to Swagatam Das .

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Editors and Affiliations

  1. Faculty of Mechanical Engineering, Department of Applied Computer Science, Brno University of Technology, Brno, Czech Republic

    Radek Matoušek

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Das, S., Ghosh, A., Mullick, S.S. (2015). A Switched Parameter Differential Evolution for Large Scale Global Optimization – Simpler May Be Better. In: Matoušek, R. (eds) Mendel 2015. ICSC-MENDEL 2016. Advances in Intelligent Systems and Computing, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-319-19824-8_9

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  • DOI: https://doi.org/10.1007/978-3-319-19824-8_9

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