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Adaptive Clustering-Based Differential Evolution for Multimodal Optimization

机译：基于自适应聚类的多式化优化差分演进

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摘要

Multimodal optimization problems which widely exist in the scientific research and engineering applications, has aroused a wide interest of researchers. For solving multimodal optimization problems, numerous niching algorithms have been proposed to locate and track multiple optima. However, most of these algorithms need a very strict choice of the population size parameter. This paper presents a new niching differential evolution algorithm which adaptively adjusts population size during the evolution. Particularly, we propose three techniques for performance enhancement: a heuristic clustering method, a population adaptation strategy, and an auxiliary movement strategy for the best individuals. The algorithm divides the population into several subpopulations and adaptively adjust the number of individuals and subpopulations according to the evolutionary state. In this way, the diversity of population is increased, while the computational cost is reduced. Experimental results verify that the proposed algorithm outperforms the other niching algorithms for multimodal optimization.
机译：在科学研究和工程应用中广泛存在的多式化优化问题引起了研究人员的广泛兴趣。为了解决多式化优化问题，已经提出了许多核实算法来定位和跟踪多个Optima。然而，大多数这些算法需要非常严格的人口大小参数选择。本文提出了一种新的幂态差分演化算法，可在演化过程中自适应地调整人口大小。特别是，我们提出了三种性能增强技术：启发式聚类方法，人口适应策略和最佳个人的辅助运动策略。该算法将人群分为几个亚步骤，并根据进化状态自适应地调整个体和亚步骤的数量。通过这种方式，人口的多样性增加，而计算成本降低。实验结果验证了所提出的算法优于多式化优化的其他幂位算法。

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