Historical Information-based Differential Evolution for Dynamic Optimization Problem

Dynamic optimization problems (DOP) widely exist in many application fields and remain a challenge. The multi-population evolutionary computation approach is an efficient framework for solving the DOPs. The key issue of the multi-population approach is how to efficiently generate subpopulations in new environment by well reusing historical information in past environments. However, most existing works try to divide the population into small ones merely considering the individuals' distribution, which may have the limitation that the partition leads to the unbalanced computational resources allocation among subpopulations. To address this limitation, we propose a historical information-based differential evolution (HIDE) to effectively solve the DOP. Firstly, a region-based subpopulation initialization (RSI) strategy is proposed to generate multiple subpopulations in the new environment in a balanced way. By initializing multiple subpopulations in different regions of the search space, the diversity of the population is enhanced, which is helpful to solve the DOP with multiple peaks. Secondly, to fully make use of the found peaks of the previous environments, an archive-based historical information reuse (AHIR) strategy is put forward to manage and reuse the historical information to guide the search in the new environment. Experiments have been carried out on the moving peaks benchmark (MPB), which is a commonly used DOP test suite. Experimental results show that the proposed HIDE algorithm generally outperforms the classic DE algorithms with different parameter settings and some existing competitive state-of-the-art DOP approaches.