An Ensemble Differential Evolution for Numerical Optimization
Xiaobing Yu, Xuming Wang, Jie Cao, Mei Cai · International Journal of Information Technology & Decision Making · 2015
The success of differential evolution (DE) in solving a specific problem crucially depends on appropriately choosing generation strategies and control parameter values. The mutation strategies of DE are classified into two groups: DE/rand/k without best solution and DE/best/k with best solution. The proposed algorithm utilizes two mutation strategies. The first one is from DE/rand/k and the second one is from DE/best/k. The proposed algorithm uses two control parameter settings. It randomly combines them to generate trial vectors. The novel mechanism improves the convergence rate of DE and maintains diversity of the population. The performance of the proposed algorithm is extensively evaluated on all the CEC2005 test functions and compares favorably with the several DE variants.