A multi-population differential evolution with best-random mutation strategy for large-scale global optimization

Differential evolution (DE) is an efficient population-based search algorithm with good robustness, but it faces challenges in dealing with Large-Scale Global Optimization (LSGO). In this paper, we proposed an improved multi-population differential evolution with best-random mutation strategy (called mDE-brM). The population is divided into three sub-populations based on the fitness values, each sub-population uses different mutation strategies and control parameters, individuals share different mutation strategies and control parameters by migrating among sub-populations. A novel mutation strategy is proposed, which uses the best individual and a randomly selected individual to generate base vector. The performance of mDE-brM is evaluated on the CEC 2013 LSGO benchmark suite and compared with 5 state-of-the-art optimization techniques. The results show that, compared with other contestant algorithms, mDE-brM has a competitive performance and better efficiency in LSGO.