Information preserving selection strategy for Differential Evolution algorithm

Abstract

Differential Evolution (DE) is a popular technique for solving real parameter global optimization problems. Several variants of DE are proposed in literature which aims at further strengthening its performance for solving complex problems. In the present study we suggest a simple and efficient modification in the selection strategy of basic DE. The proposed strategy is named Information Preserving (IP) selection strategy. It makes use of most of the information that is generated during the different phases of DE. The proposed IP scheme is embedded in the structure of basic DE and also in DERL, another variant of DE. The numerical results indicate that the inclusion of proposed scheme significantly improves the performance in terms of convergence rate while maintaining the solution quality.