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Differential evolution with two-level adaptive mechanism for numerical optimization

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Summary: Over the past two decades, Differential Evolution (DE) has been proven to be one of the most popular and successful evolutionary algorithms for solving global optimization problems over continuous space. This paper provides a comprehensive analysis of basic and novel mutation strategies proposed between 1995 and 2020, presenting a new taxonomy based on the structure of novel mutations. Through numerical experiments and discussion of theoretical and empirical convergence behavior, the paper offers insights and recommendations for designing and developing effective and efficient DE algorithms.

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