Abstract:
A differential evolution algorithm with group strategy and dynamic parameter setting is proposed. To balance the global exploration and local exploitation effectively, a strategy is designed to dynamically adjust the range of elite solutions among the whole population. The population is grouped according to individual fitness values, and different adaptive scaling factors are adopted, respectively.An adaptive cross rate is dynamically set to help the search jump out of the local optima. The proposed algorithm is evaluated on 30 benchmark problems from CEC2014. Compared with six existing evolution algorithms, the experimental results show that the proposed algorithm has a better optimization performance.
