蚁群优化的自适应参数控制策略

2007 International Conference on Computational Intelligence and Security (CIS 2007) Pub Date : 2007-12-15 DOI:10.1109/CIS.2007.156

Weixin Ling, Huanping Luo

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引用次数: 21

摘要

被证明是求解像TSP这样的NP-hard组合优化问题的最佳算法之一。蚁群算法的研究吸引了众多研究者，但对其直接影响算法的参数进行调优的方法较少。本文研究了蚁群算法的参数设置问题。引入人工鱼群算法(Artificial Fish Swarm Algorithm, AFSA)来解决参数整定问题，提出了一种自适应参数整定策略。基于TSPLIB测试的实验证明了该方法的有效性。关键词:人工鱼群算法，蚁群优化，参数，TSP

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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An Adaptive Parameter Control Strategy for Ant Colony Optimization

proved to be one of the best performing algorithms for NP-hard combinational optimization problems like TSP. Many researchers have been attracted in research for ACO but fewer tuning methodologies have been done on its parameters which influence the algorithm directly. The setting of ACO's parameters is studied in this paper. The Artificial Fish Swarm Algorithm (AFSA) is introduced to solve the parameter tuning problem, and an adaptive parameter setting strategy is proposed. It's proved to be effective by the experiment based on TSPLIB test. Keywords: Artificial Fish Swarm Algorithm, Ant Colony Optimization, parameters, TSP

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2007 International Conference on Computational Intelligence and Security (CIS 2007)

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