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Self-adaptive differential evolution incorporating a heuristic mixing of operators

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Abstract

A considerable number of differential evolution variants have been proposed in the last few decades. However, no variant was able to consistently perform over a wide range of test problems. In this paper, propose two novel differential evolution based algorithms are proposed for solving constrained optimization problems. Both algorithms utilize the strengths of multiple mutation and crossover operators. The appropriate mix of the mutation and crossover operators, for any given problem, is determined through an adaptive learning process. In addition, to further accelerate the convergence of the algorithm, a local search technique is applied to a few selected individuals in each generation. The resulting algorithms are named as Self-Adaptive Differential Evolution Incorporating a Heuristic Mixing of Operators. The algorithms have been tested by solving 60 constrained optimization test instances. The results showed that the proposed algorithms have a competitive, if not better, performance in comparison to the-state-of-the-art algorithms.

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Notes

  1. If the link does not work for any technical problem, feel free to contact the first author on s.elsayed@adfa.edu.au.

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Authors and Affiliations

  1. School of Engineering and Information Technology, University of New South Wales, ADFA Campus, Canberra, 2600, Australia

    Saber M. Elsayed, Ruhul A. Sarker & Daryl L. Essam

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  1. Saber M. Elsayed
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  2. Ruhul A. Sarker
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  3. Daryl L. Essam
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Correspondence to Saber M. Elsayed.

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Elsayed, S.M., Sarker, R.A. & Essam, D.L. Self-adaptive differential evolution incorporating a heuristic mixing of operators. Comput Optim Appl 54, 771–790 (2013). https://doi.org/10.1007/s10589-012-9493-8

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  • DOI: https://doi.org/10.1007/s10589-012-9493-8

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