具有拥挤机制的基于方向的进化算法

Chi Cuong Vu, L. Bui
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引用次数: 1

摘要

在进化计算中,拥挤是处理多模态优化问题的常用技术,多模态优化问题包含许多可能的局部或全局最优解。在我们之前的文章中,我们提出了一种新的进化算法,称为DEAL (Direction-guided evolutionary algorithm)。它能有效地解决非线性优化问题。在本文中,我们通过应用拥挤机制(称为CrowdingDEAL)将DEAL进一步扩展到多模态领域。我们用广泛的基准问题验证了CrowdingDEAL算法。实验结果表明,CrowdingDEAL算法在处理多模态数据方面具有较强的性能,并与其他算法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The direction-based evolutionary algorithm with a crowding mechanism
In evolutionary computation, crowding is a popular technique to handle multi-modal optimization problems, which include many possible local or global optimal solutions. In our previous publication, we proposed a new evolutionary algorithm, called DEAL (Direction-guided Evolutionary Algorithm). It works effectively on non-linear optimization problems. In this paper, we extend further DEAL towards the area of multi-modality by applying a crowding mechanism, called as CrowdingDEAL. We validated CrowdingDEAL algorithm with a wide range of benchmark problems. The obtained results indicated a strong performance of CrowdingDEAL in dealing with multi-modality and in comparison with other algorithms.
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