Niching-based Self-adaptive Ensemble DE with MMTS for solving dynamic optimization problems

Dynamic and non-stationary problems require optimization algorithms search for the best solutions in a time-varying fitness environment. Various methods and strategies such as niching, clustering and sub-population approaches have been implemented with Differential Evolution (DE) to handle such problems. With the help of crowding niching to maintain general population diversity, this paper attempts to extend the Self-adaptive Ensemble DE with modified multi-trajectory search attempt to solve CEC2014 dynamic optimization competition benchmark problems.