Differential evolution with spatially neighbourhood best search in dynamic environment
Dingcai Shen, Longyin Zhu · International Journal of Computational Science and Engineering · 2019
In recent years, there has been a growing interest in applying differential evolution (DE) to optimisation problems in a dynamic environment. The ability of tracking a changing optimum over time is concerned in dynamic optimisation problems (DOPs). In this study, an improved niching-based scheme named spatially neighbourhood best search DE (SnDE) for DOPs is proposed. The SnDE adopts DE with DE/best/1/bin scheme. The best individual in the selected scheme is searched around the considered individual in a predefined neighbourhood size, thus keeping a balance between exploitation ability and exploration ability. A comparative study with several algorithms with different characteristics on a common platform by using the moving peaks benchmark (MPB) and various problem settings are presented in this study. The results indicate that the proposed algorithm can track the changing optimum in each circumstance effectively on the selected benchmark function.