Abstract
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.
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Liang, J., Qu, B., Mao, X., Chen, T. (2012). Differential Evolution Based on Fitness Euclidean-Distance Ratio for Multimodal Optimization. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_72
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DOI: https://doi.org/10.1007/978-3-642-31837-5_72
Publisher Name: Springer, Berlin, Heidelberg
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