کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
409946 | 679106 | 2014 | 9 صفحه PDF | دانلود رایگان |
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.
Journal: Neurocomputing - Volume 137, 5 August 2014, Pages 252–260