Differential evolution algorithm with co‐evolution of control parameters and penalty factors for constrained optimization problems
Qinqin Fan, Xuefeng Yan · Asia-Pacific Journal of Chemical Engineering · 2010
ABSTRACT Considering that it is difficult to set suitable penalty factors for the penalty function method, which is one of the most important ways to solve constrained optimization problems, and that the quality of obtained optimal solution mainly depends on the optimization algorithm's performance and handling constraints capacity, a novel differential evolution algorithm with co‐evolution of control parameters and penalty factors, named as CoE‐DE, is proposed. In CoE‐DE, differential evolution operator is applied for evolving the original individuals, which consist of optimal variables. To improve the performance of CoE‐DE and the handling constraints capacity, Alopex algorithm is used to co‐evolve the symbiotic individuals, which consist of two DE control parameters and the penalty factors. To illustrate the whole performance of CoE‐DE, several algorithms are applied to solve 13 benchmark functions and five constrained engineering problems. The results show that the performance of CoE‐DE is better than SR algorithm and similar to α SIMPILE in 13 benchmark functions, and the satisfactory result is obtained in five constrained engineering problems. Copyright © 2010 Curtin University of Technology and John Wiley & Sons, Ltd.