Differential evolution in constrained numerical optimization: An empirical study

  • Authors:

  • Efrén Mezura-Montes;Mariana Edith Miranda-Varela;Rubí del Carmen Gómez-Ramón

  • Affiliations:

  • Laboratorio Nacional de Informática Avanzada (LANIA A.C.), Rébsamen 80, Centro, Xalapa, Veracruz 91000, Mexico;Universidad del Istmo, Campus Ixtepec, Ciudad Universitaria s/n, Cd. Ixtepec, Oaxaca 70110, Mexico;Universidad del Carmen, C. 56 #4, Ciudad del Carmen, Campeche 24180, Mexico

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2010

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Abstract

Motivated by the recent success of diverse approaches based on differential evolution (DE) to solve constrained numerical optimization problems, in this paper, the performance of this novel evolutionary algorithm is evaluated. Three experiments are designed to study the behavior of different DE variants on a set of benchmark problems by using different performance measures proposed in the specialized literature. The first experiment analyzes the behavior of four DE variants in 24 test functions considering dimensionality and the type of constraints of the problem. The second experiment presents a more in-depth analysis on two DE variants by varying two parameters (the scale factor F and the population size NP), which control the convergence of the algorithm. From the results obtained, a simple but competitive combination of two DE variants is proposed and compared against state-of-the-art DE-based algorithms for constrained optimization in the third experiment. The study in this paper shows (1) important information about the behavior of DE in constrained search spaces and (2) the role of this knowledge in the correct combination of variants, based on their capabilities, to generate simple but competitive approaches.