Abstract
Differential evolution has shown success in solving different optimization problems. However, its performance depends on the control parameters and search operators. Different from existing approaches, in this paper, a new framework which dynamically configures the appropriate choices of operators and parameters is introduced, in which the success of a search operator is linked to the proper combination of control parameters (scaling factor and crossover rate). Also, an adaptation of the population size is adopted. The performance of the proposed algorithm is assessed using a well-known set of constrained problems with the experimental results demonstrating that it is superior to state-of-the-art algorithms.
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Acknowledgment
This work was supported by an Australian Research Council Discovery Project (Grant# DP150102583) awarded to A/Prof. Ruhul Sarker.
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Elsayed, S.M., Sarker, R. (2016). Dynamic Configuration of Differential Evolution Control Parameters and Operators . In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_7
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DOI: https://doi.org/10.1007/978-3-319-28270-1_7
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