Self-adaptive differential evolution algorithm with improved mutation strategy
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Different mutation strategies and control parameters settings directly affect the performance of differential evolution (DE) algorithm. In this paper, a self-adaptive differential evolution algorithm with improved mutation strategy (IMSaDE) is proposed to improve optimization performance of DE. IMSaDE improves the “DE/rand/2” mutation strategy by incorporating elite archive strategy and control parameters adaptation strategy. Both strategies diversify the population and improve the convergence performance of the algorithm. IMSaDE was compared with eleven DE algorithms and six non-DE algorithms by using a set of 20 benchmark functions taken from the literature. Experimental results show that the overall performance of IMSaDE is better than the other competitors. In addition, the size of elite population has a significant impact on the performance of IMSaDE.
KeywordsDifferential evolution Global optimization Mutation strategy Control parameters adaptation Elite archive
The authors would like to thank the reviewers for their critical and constructive review of the manuscript. This study was funded by National Natural Science Foundation of China (71573184).
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Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
This study does not involve any human participants.
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