Differential evolution based on fitness Euclidean-distance ratio for multimodal optimization

The prime target of multi-modal optimization is to find multiple global and local optima of a problem in one single run. Differential evolution is a recently proposed stochastic optimization technique. Though variants of differential evolution (DE) are highly effective in locating single global optimum, few DE algorithms perform well when solving multi-optima problems. In this paper, a modified Fitness Euclidean-distance Ratio (FER) technique is incorporated into DE to enhance the DE׳s ability of locating and maintaining multiple peaks. The proposed algorithm is tested on a number of benchmark test functions and the experimental results show that the proposed simple algorithm performs better comparing with a number of state-of-the-art multimodal optimization approaches.