Surrogate model assisted ensemble differential evolution algorithm

Differential Evolution (DE) is a simple and effective approach for solving numerical optimization problems. However, the performance of DE is sensitive to the choice of the mutation and crossover strategies and their associated control parameters. Therefore, to obtain optimal performance, time consuming parameter tuning is necessary. In DE, different mutation and crossover strategies with different parameter settings can be appropriate during different stages of the evolution. Therefore, to obtain optimal performance using DE, various adaptation and self-adaptation techniques have been proposed. Recently, a DE algorithm with an ensemble of parameters and strategies (EPSDE) was proposed. In EPSDE, a pool of distinct mutation and crossover strategies along with a pool of values for each control parameter coexists throughout the evolution process and competes to produce offspring. The performance of EPSDE degrades if the population members get struck with a combination of strategies and parameters values that produce successful offspring but lead to premature convergence in the due course of the evolution. In this paper, we try to improve the performance of the EPSDE algorithm with the help of a surrogate model that assists in generating competitive trial vectors corresponding to each parent in every generation of the evolution. The proposed algorithm is referred to as surrogate model assisted EPSDE (SMA-EPSDE) and employs a simple Kriging model to construct the surrogate. The performance of EPSDE is evaluated on a set of 17 bound-constrained problems and is compared with state-of-the-art algorithms