A new differential evolution algorithm with two dynamic speciation-based mutation strategies (DSM-DE) is proposed to solve single-objective optimization problems. An explorative mutation “DE/seeds-to-seeds” and an exploitative mutation “DE/seeds-to-rand” are employed simultaneously in DSM-DE in the evolutionary process. A Dynamic Speciation Technique is designed to assist the two mutations in order to utilize the potential of selective portioning of critical individuals in the population. It dynamically divides the population into numbers of species whilst taking species seeds as centers. The best individuals for each species are used as base vectors in each species in the proposed mutation strategies. “DE/seeds-to-seeds” selects individuals from species seeds and current species to constitute difference vectors whereas “DE/seeds-to-rand” selects from the whole population. Thus the two mutation strategies can accelerate the convergence process without decreasing diversity of the population. Comparison results with four classic DE variants, one state-of-art DE variant and two improved non-DE variants on CEC2014, CEC2015 benchmark, and Lennard-Jones potential problem reveal that the overall performance of DSM-DE is better than that of the other seven DE algorithms. In addition, experiments also substantiate the effectiveness and superiority of two seeds-guided mutation strategies in DSM-DE.

中文翻译：

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DSM-DE：具有动态形态学变异的差分进化，用于单目标优化

为了解决单目标优化问题，提出了一种基于两种动态基于物种的变异策略的差分进化算法（DSM-DE）。DSM-DE在进化过程中同时采用了探索性突变“ DE / seeds-to-seeds”和开发性突变“ DE / seeds-rand”。动态物种形成技术旨在协助这两个突变，以便利用人口中关键个体的选择性分配潜力。它以种群种子为中心，动态地将种群划分为物种数量。在提出的突变策略中，将每个物种的最佳个体用作每个物种的基础载体。“ DE /种子到种子”从物种种子和当前物种中选择个体以构成差异载体，而“ DE /种子到种子”则从整个种群中选择。因此，这两种突变策略可以在不降低种群多样性的情况下加速收敛过程。在CEC2014，CEC2015基准测试和Lennard-Jones潜在问题上与四种经典DE变体，一种最新的DE变体和两种改进的非DE变体的比较结果表明，DSM-DE的总体性能要优于DSM-DE的整体性能。其他七个DE算法。此外，实验还证实了DSM-DE中两种种子指导的突变策略的有效性和优越性。在CEC2014，CEC2015基准测试和Lennard-Jones潜在问题上与四种经典DE变体，一种最新的DE变体和两种改进的非DE变体的比较结果表明，DSM-DE的总体性能要优于DSM-DE的整体性能。其他七个DE算法。此外，实验还证实了DSM-DE中两种种子指导的突变策略的有效性和优越性。在CEC2014，CEC2015基准测试和Lennard-Jones潜在问题上与四种经典DE变体，一种最新的DE变体和两种改进的非DE变体的比较结果表明，DSM-DE的总体性能要优于DSM-DE的整体性能。其他七个DE算法。此外，实验还证实了DSM-DE中两种种子指导的突变策略的有效性和优越性。