Differential Evolution Based on Intelligent Mutation Scheme
نویسندگان
چکیده
Differential Evolution (DE) is a random search algorithm used for function optimization basing on population evolution. It has been proved effective and efficient through years of studies. Among three evolutionary operations, the mutation scheme is believed to have high relation to convergence and diversity during the evolution process. This paper is going to introduce a modified differential evolution algorithm based on intelligent mutation scheme (IMSDE) which can improve the efficiency of traditional DE. With a whole new “evolutionary strategy”, IMSDE adopts a selfadaptive differential variation named evolution difference which records individual’s improvements during the evolution process to replace the totally random one. The experimental results of 23 benchmark functions show that IMSDE outperforms traditional DE.
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