Differential Evolution Based on Fitness Euclidean-Distance Ratio for Multimodal Optimization

In this paper, fitness euclidean-distance ratio (FER) is incorprated into differential evolution to solve multimodal optimization problems. The prime target of multi-modal optimization is to finding multiple global and local optima of a problem in one single run. Though variants of differential evolution (DE) are highly effective in locating single global optimum, few DE algorithms perform well when solving multi-optima problems. This work uses the FER technique to enhance the DE's ability of locating and maintaining multiple peaks. The proposed algorithm is tested on a number of benchmark test function and the experimental results show that the proposed simple algorithm performs better comparing with a number of state-of-the-art multimodal optimization approaches.