基于混合反馈机制的扩展蚁群算法
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华中科技大学

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TP273

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科技创新2030—“新一代人工智能”重大项目(2018AAA0101200)


Extended Ant Colony Algorithm Based on Mixed Feedback Mechanism
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Huazhong University of Science and Technology

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    摘要:

    由于传统蚁群算法基于正反馈机制的单一搜索方式,导致其存在收敛速度慢、易陷入局部极值的缺点。本文提出一种基于混合反馈机制的扩展蚁群算法(MF-ACO),该算法在传统蚁群算法的基础上,定义一种具有较强全局搜索能力的扩展型蚂蚁,帮助算法跳出局部极值;并参考蚁群劳动分工行为,设计了基于刺激-响应分工模型的负反馈平衡机制,动态平衡算法的收敛能力和全局搜索能力;最后依据分工模型对蚂蚁个体的信息素更新策略进行改进,进一步加快算法收敛速度。本文以多个TSP实例作为测试对象进行仿真实验,实验结果表明了本文算法的优越性,之后又将该算法用于机器人路径规划问题,在实际应用中进一步验证了算法的有效性。

    Abstract:

    Because of the traditional ant colony algorithm based on positive feedback search way, lead to the existence of slow convergence speed and shortcoming of easily trapped in local minima. This paper propose a kind of extended ant colony algorithm based on mixed feedback mechanism(MF-ACO), the algorithm on the basis of traditional ant colony algorithm, define a kind of extension type ants which have strong global search ability, help algorithm out of local minima; In addition, the negative feedback balance mechanism based on stimulus-response model, the convergence ability and global search ability of dynamic balance algorithm were designed by referring to the labor division behavior of ant colony. Finally on the basis of division of labor model of individual ants pheromone update strategy was improved, to further accelerate the algorithm convergence speed Based on multiple TSP instance as the test object simulation experiment, the experimental results show that the superiority of the algorithm in this paper, and then use this algorithm for robot path planning problem, to verify the effectiveness of the proposed algorithm in actual application.

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历史
  • 收稿日期:2021-05-14
  • 最后修改日期:2021-08-13
  • 录用日期:2021-08-18
  • 在线发布日期: 2021-09-01
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