Improved differential evolution for global optimization

Abstract

Differential Evolution (DE) is a recently proposed population based evolutionary technique, which attracts much attention for its simple concept, easy implementation and robustness. In order to enhance the performance of classical DE, this paper presents an improved DE algorithm for global optimization. The proposed approach IDE employs a mutation operator based on an opposition-based learning concept. To verify the performance of IDE, we test it on 13 well-known benchmark functions. The simulation results show that the proposed approach outperforms the compared algorithm on most of test problems.