Prediction based multi-strategy differential evolution algorithm for dynamic environments

Abstract

Many real world optimization problems are dynamic optimization problems (DOPs) whose optima change over time. In this paper, we propose new variants of differential evolution (DE) to solve DOPs. A hybrid method that combines population core based multi-population strategy and prediction strategy and new local search scheme is introduced into DE to enhance its performance for solving DOPs. The population core based multi-population strategy is useful to maintain the diversity of population by using the multi-population and population core concept. The prediction strategy is useful to rapidly adapt to the dynamic environment by using the prediction area. The local search scheme is useful to improve the searching accuracy by suing the new chaotic local search method. Experimental results on the moving peaks benchmark show that the proposed schemes enhance the performance of DE in the dynamic environments.