Self-Adaptive Differential Evolution Algorithm in Constrained Real-Parameter Optimization

Abstract

Differential Evolution (DE) has been shown to be a powerful evolutionary algorithm for global optimization in many real problems. Self-adaptation has been found to be high beneficial for adjusting control parameters during evolutionary process, especially when done without any user interaction. In this paper we investigate a self-adaptive differential evolution algorithm where more DE strategies are used and control parameters F and CR are self-adapted. The performance of the self-adaptive differential evolution algorithm is evaluated on the set of 24 benchmark functions provided for the CEC2006 special session on constrained real parameter optimization.