Improved differential evolution based on multi-armed bandit for multimodal optimization problems

摘要

The main aim of multimodal optimization problems (MMOPs) is to find and deal with multiple optimal solutions using an objective function. MMOPs perform the exploration and exploitation simultaneously in the search space. The novelty of this paper includes the following improvements in differential evolution to be able to solve MMOPs. Clusters are formed from the whole population by applying a niching technique which uses the softmax strategy to assign a cutting probability to the species. Then iterative mutation strategy is followed to generate the unbiased mutant vector. Further, Multi-Armed Bandit (MAB) strategy is used to ensure that new individuals are generated in promising areas. The experimentation of the proposed algorithm has been performed on 20 benchmark functions from IEEE Congress on Evolutionary Computation 2013 (CEC2013). The results depict that the proposed algorithm can be compared with 15 state-of-the-art multimodal optimization algorithms in terms of locating accurate optimal solutions.