Control parameters and mutation based variants of differential evolution algorithm

Abstract

Differential Evolution (DE) is considered as a simple yet influential search engine used for optimization of real-valued, multimodal and nonlinear functions. Here two new variants of the parent DE are presented with self-tuned control parameters and a modified mutation scheme. The First variant, designed by applying self-adaptive control parameters to the parent DE, is named as CPDE and the second one, the enhanced version of CPDE in which CPDE is embedded with a modified mutation scheme, is named as CPMDE. The crucial role of the self-adaptive control parameters can't be abandoned, which is used to improve the quality of the solution. The next application of self-adapted control parameters is drawn with a modified mutation operation in which the whole search space is divided into three equal parts for the sake of maximum exploration of the search space. A set of 14 traditional and 12 non-traditional (6 shifted and 6 hybrid) test problems is chosen for validation of the performance of the proposed algorithms which is then compared with the parent DE and some other variants of DE in terms of number of function evaluations, CPU time, error and standard deviation. Numerical and statistical results show that the proposed algorithm helps in providing a better trade-off between convergence rate and efficiency.