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    改进型蚁群算法求解单任务Agent联盟

    Searching for Agent Coalition for Single Task Using Improved Ant Colony Algorithm

    • 摘要: 联盟是多Agent之间一种重要的合作方法,如何生成面向某个任务的最优联盟是一个复杂的 组合优化问题.首次引入蚁群算法来解决这一问题,在求解过程中蚂蚁倾向于选择曾经合作 过并且合作效果比较好的Agent组成联盟,充分实现了熟人机制;创新地引入“第2种信息素 ”对蚁群算法进行改进,不再易于陷入局部极小.对比实验结果表明,本算法在解的性能和 收敛速度上均优于相关算法.

       

      Abstract: Coalition is an important cooperative method in multi-agent system. It is a comp licated combinatorial optimization problem to search for the optimal, task-orien ted agent coalition. An ant colony algorithm is adopted to solve the problem. Du ring the process of solution, ants incline to select those agents who cooperate well before to form coalitions, which realizes the acquaintance mechanism perfec tly. A novel “second pheromone” is proposed to improve the ACA so as not to ge t in the premature convergence easily. The results of contrastive experiments sh ow that this algorithm is superior to other related methods both on the quality of solution and on the convergence rate.

       

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