Opposition-Based Differential Evolution Using the Current Optimum for Function Optimization
Na Wang · 2011
When the global optimum is not located at the geometric center of the domain,the opposite numbers may lapse from the global optimum,leading to poor performance of opposition-based differential evolution.A novel opposition-based learning strategy using the current optimum is introduced,and it is combined with differential evolution for function optimization.The optimum in the current generation is served as a symmetry point between an estimate and the corresponding opposite estimate,resulting in a high rate of opposite population usage.Experiments results clearly show that the proposed algorithm can significantly improve the performance due to the opposite numbers.Additionally,an enhanced version of opposition-based differential evolution is proposed to reveal ideal and perfect results using opposition-based learning.