Ensemble differential evolution algorithm for CEC2011 problems

Differential Evolution (DE) is a simple yet efficient stochastic algorithm for solving real world problems. To achieve optimal performance with DE, time consuming parameter tuning is essential as its performance is sensitive to the choice of the mutation and crossover strategies and their associated control parameters. During different stages of DE's evolution, different combinations of mutation and crossover strategies with different parameter settings can be appropriate. Based on this observation different adaptive and self-adaptive techniques have been proposed. In this paper, we employ a DE with an ensemble of mutation and crossover strategies and their associated control parameters known as EPSDE. 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 is evaluated on a set of real world problems taken from different fields of engineering and presented in the technical report of Conference on Evolutionary Computation (CEC) 2011.