一种基于改进遗传变异算子的差分进化算法
An Enhanced Differential Evolution Algorithm Using a Novel Clustering-based Mutation Operator
摘要：
差分进化算法是一种有效的基于种群的元启发式算法，用于求解复杂优化问题。然而，DE的性能是敏感的变异算子。在本文中，我们提出了一种新的DE算法，Clu-DE，提高了DE的效率，使用一种新的基于聚类的变异算子。首先，我们发现，使用聚类算法，在搜索空间的赢家集群，并选择最佳的候选解决方案，在这个集群作为基向量的变异算子。然后，引入一个更新方案，以包括新的候选解决方案在当前的人口。在CEC-2017基准函数上的实验结果证实，与DE相比，Clu-DE的性能有所提高。
英文摘要：
Differential evolution (DE) is an effective population-based metaheuristic algorithm for solving complex optimisation problems. However, the performance of DE is sensitive to the mutation operator. In this paper, we propose a novel DE algorithm, Clu-DE, that improves the efficacy of DE using a novel clustering-based mutation operator. First, we find, using a clustering algorithm, a winner cluster in search space and select the best candidate solution in this cluster as the base vector in the mutation operator. Then, an updating scheme is introduced to include new candidate solutions in the current population. Experimental results on CEC-2017 benchmark functions with dimensionalities of 30, 50 and 100 confirm that Clu-DE yields improved performance compared to DE.
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