Differential evolution algorithm with multi-population cooperation and multi-strategy integration

Abstract

Differential evolution with a multi-population based ensemble of mutation strategies (MPEDE) has been considered among the most efficient Evolutionary Algorithms for global optimization. Our research results reveal that the performance of MPEDE may be improved by adding an information sharing mechanism and modifying the grouping mechanism. In MPEDE, the entire population is divided into four subpopulations, and most computing resources are allocated to the best strategy, but a better strategy has the same computing resources as the worst strategy. In order to rationally distribute computational resources, a differential evolution variant with multi-population cooperation and multi-strategy integration (MPMSDE) is proposed in this paper. MPMSDE develops a new grouping method instead of the grouping method in MPEDE, and the new grouping method utilizes the ranking of strategies to assign computational resources to different strategies. Also, an information sharing mechanism is introduced in the largest sub-population to avoid falling into local optimum. In MPMSDE, a new mutation strategy, “DE/pbad-to-pbest-to-gbest/1”, is used to replace the mutation strategy “DE/rand/1” in MPEDE. The new strategy not only uses personal history optimal solution and the worst solution but also uses the global best solution to update individuals. The new strategy can not only balance exploration and exploitation but also can accelerate the convergence of the algorithm. The performance of MPMSDE is compared with MPEDE and other state-of-the-art evolutionary algorithms on CEC2005 and CEC2014 benchmark functions. The experimental results show that the performance of the MPMSDE algorithm is very competitive in calculation accuracy and convergence speed.