候选集自适应控制提高蚁群算法的性能

The 2003 Congress on Evolutionary Computation, 2003. CEC '03. Pub Date : 2003-12-08 DOI:10.1109/CEC.2003.1299826

I. Watanabe, Shouichi Matsui

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引用次数: 31

摘要

具有候选集的蚁群优化算法在求解大型优化问题时具有较高的性能，但难以预先确定候选集的最优大小。我们提出了一种基于信息素浓度的候选集自适应控制机制，以提高蚁群算法的性能，并报告了使用图着色问题的计算实验结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Improving the performance of ACO algorithms by adaptive control of candidate set

The performance of ant colony optimization (ACO) algorithms with candidate sets is high for large optimization problems, but it is difficult to set the size of candidate sets to optimal in advance. We propose an adaptive control mechanism of candidate sets based on pheromone concentrations for improving the performance of ACO algorithms and report the results of computational experiments using the graph coloring problems.

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The 2003 Congress on Evolutionary Computation, 2003. CEC '03.

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