Abstract
A novel approach based on three differential evolution variants to solve numerical constrained optimization problems is presented. Each variant competes to get more vectors for reproduction from the population. Such competition is based on two performance measures for convergence and solution improvement. Two of the variants adopted in this work were precisely proposed to deal with constrained search spaces. Two experiments are carried out: one to analyze the behavior of each variant with respect to the features of the problem solved and another to compare the performance of the proposed approach with respect to state-of-the-art multi-operator algorithms. The results obtained show that the specialized variants are more useful in the search, either combined or just using one of them. Finally, the final results of the proposed approach were highly competitive, and better in some cases, with respect to those of the algorithms used in the comparison.
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Gordián-Rivera, LA., Mezura-Montes, E. (2012). A Combination of Specialized Differential Evolution Variants for Constrained Optimization. In: Pavón, J., Duque-Méndez, N.D., Fuentes-Fernández, R. (eds) Advances in Artificial Intelligence – IBERAMIA 2012. IBERAMIA 2012. Lecture Notes in Computer Science(), vol 7637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34654-5_27
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DOI: https://doi.org/10.1007/978-3-642-34654-5_27
Publisher Name: Springer, Berlin, Heidelberg
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