Ant Colony Optimization for task allocation in Multi-Agent Systems

Abstract

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 Optimization (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 analyzed; CPACO overcomes such problems by modifying the heuristic function and the update strategy in the Ant-Cycle Model and establishing a threedimensional path pheromone 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. Based on the traditional parallel computing task allocation method and Ant Colony Optimization, Collection Path Ant Colony Optimization is proposed to achieve global optimization and reduce processing time. CPACO overcomes the existing problems of ACO by modifying the heuristic function and the update strategy in the Ant-Cycle Model and establishing a threedimensional path pheromone storage space.

Applications of soft computing in RFID system: A review

“…(5) Ant colony optimization (ACO). ACO is an optimization algorithm inspired by collective behaviors of ant colonies when they search for food [5]. Taking advantage of the information that ants released, the optimal path/solution can be obtained.…”

A Survey of Self-Organization Mechanisms in Multiagent Systems

“…Centralised task allocation mechanisms, e.g., [42] and [43], have the single point of failure and do not consider the change of tasks and agents. To overcome these drawbacks, decentralised task allocation mechanisms were developed, e.g., [44], [45], [46], [47], [48]. These decentralised mechanisms can avoid the single point of failure, but they still have some limitations.…”

“…Chapman et al's approach [47] is based on a distributed stochastic algorithm which is fast and needs few communication messages, but it may get stuck in local minima. Wang et al's mechanism [48] is based on ant colony algorithm which requires global pheromone matrix to achieve optimal solutions.…”

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