多智能体系统中的任务分配蚁群优化算法

Ant Colony Optimization for Task Allocation in Multi-Agent Systems

Task allocation is a key issue of agent cooperation mechanism in Multi-Agent Systems. The important features of an agent system such as the latency of the network infrastructure, dynamic topology, and node heterogeneity impose new challenges on the task allocation in Multi-Agent environments. Based on the traditional parallel computing task allocation method and Ant Colony Opti-mization (ACO), a novel task allocation method named Collection Path Ant Colony Optimization (CPACO) is proposed to achieve global optimization and reduce processing time. The existing problems of ACO are ana-lyzed; CPACO overcomes such problems by modifying the heuristic function and the up-date strategy in the Ant-Cycle Model and es-tablishing a three- dimensional path phero-mone storage space. The experimental results show that CPACO consumed only 10.3% of the time taken by the Global Search Algorithm and exhibited better performance than the Forward Optimal Heuristic Algorithm.