Title:	INTEGRATING ANT COLONY OPTIMIZATION IN A MOBILE-AGENT BASED RESOURCE DISCOVERY ALGORITHM
Author(s):	Yasushi Kambayashi , Yoshikuni Harada
ISBN:	978-972-8924-87-4
Editors:	António Palma dos Reis
Year:	2009
Edition:	Single
Keywords:	P2P, Multi-agent system, Mobile agent, Resource discovery, Swarm intelligence, Ant colony optimization
Type:	Full Paper
First Page:	149
Last Page:	158
Language:	English
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Paper Abstract:	A peer-to-peer (P2P) system consists of a number of decentralized distributed network nodes that are capable of sharing resources without centralized supervision. Many applications such as IP-phone, contents delivery networks (CDN) and distributed computing adopt P2P technology into their base communication systems. One of the most important functions in P2P systems is the location of resources, and it is generally hard to achieve due to the intrinsic nature of P2P, i.e. dynamic re-configuration of the network. We have proposed and implemented an efficient resource locating method in a pure P2P system based on a multiple agent system. All the resources as well as resource information are managed by cooperative multiple agents. In order to optimize the behaviors of cooperative multiple agents, we utilize the ant colony optimization (ACO) algorithm that assists mobile agents to migrate toward relatively resource-rich nodes. Quasioptimally guided migrating multiple agents are expected to find desired resources effectively while reducing communication traffic in the network. Efficient migration is achieved through indirect communications that are typical of social insects, called stigmergy. When an agent finds a resource-rich node, it strengthens the path toward the node to gain efficiency. Strengthening of the route is achieved by pheromone laid down by preceding agents that guides succeeding agents. In this paper, we report the integration of the ACO method to optimize the behaviors of the mobile multiple agents.
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