An Effective Improvement of JADE for Real-parameter Optimization

Although the metaheuristics cannot guarantee to find the optimum for global optimization, they are efficient indeed, especially for the problems very difficultly be optimized by traditional optimization methods. Differential evolution algorithm is one of the most competitive metaheuristics and the adaptive DE with optional external archive (JADE) is an excellent DE-variant. Based on the analysis of shortcomings of JADE, an effective improvement of JADE is put forward in this paper. Two parameters in JADE can be reinitialized and two new mutation strategies are added in the improved JADE. 28 benchmark problems for competition on real-parameter single objective optimization in 2013 IEEE Congress on Evolutionary Computation (CEC 2013) are used to test the performance of the proposed algorithm. The results compared with DE/rand/1 and original JADE shows the improvement is effective.