Abstract

Although differential evolution (DE) algorithms have been widely proposed for tackling various of problems, the trade-off among population diversity, global and local exploration ability, and convergence rate is hard to maintain with the existing strategies. From this respective, this paper presents some new mutation strategies of DE by applying the clearing niche mechanism to the existing mutation strategies. Insteading of using random, best or target individuals as base vector, the niche individuals are utilized in these strategies. As the base vector is from a subpopulation, which is made up of the best individuals in each niche, the base vector can be guided by the global or local best ones. This mechanism is beneficial to the balance among population diversity, search capability, and convergence rate of DE, since it can both enhance the population diversity and search capability. Extensive experimental results indicate that the proposed strategies based on clearing niche mechanism can effectively enhance DE’s performance.

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