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蚁群算法中基于知识引导的信息素控制策略 被引量:3

Knowledge-guiding Pheromone Control Strategy of Ant Colony Optimization
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摘要 针对蚁群算法在求解旅行商问题性能方面的不足,提出了一种基于知识引导的信息素控制策略.该策略利用问题先验知识初始化信息素,旨在提高算法运行初期信息素对蚂蚁搜索的启发能力;采用群知识引导信息素更新,加强信息素对蚂蚁搜索的引导能力,增强蚂蚁搜索的目的性.实验结果表明,基于这种信息素控制策略的蚁群算法的总体性能明显优于当前最先进的蚁群算法. An ACO (Ant Colony Optimization) algorithm for TSP (Traveling Salesman Problem) with knowledge guiding pheromone control strategy is put forward. On the one hand, aiming at accelerating the convergence, the pheromone is initialized by the MST (Minimal Spanning Tree) information. On the other hand, the pheromone updating is guided by swarm knowledge, which is the intersection information of paths constructed by all ants. It can strengthen the collaboration of ants. The experimental results indicate that the proposed algorithm outperforms other ACO algorithms.
出处 《北京工业大学学报》 EI CAS CSCD 北大核心 2011年第8期1236-1241,共6页 Journal of Beijing University of Technology
基金 国家自然科学基金资助项目(60496322) 北京市自然科学基金资助项目(4102010)
关键词 旅行商问题(TSP) 蚁群算法 知识引导 信息素控制 traveling salesman problem (TSP) ant colony optimization (ACO) algorithm knowledge guide pheromone control
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参考文献10

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二级参考文献27

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