Evaluation for Acquiring Method for Agents’ Actions Using Pheromone Communication in Multi-Agent System

Abstract

This paper has presented acquiring method for agents' actions using Ant Colony System (ACS) in multi-agent system. ACS is one of powerful meta-heuristics algorithms and some researchers have reported the effectiveness of some applications with the algorithm. I have developed agents with proposed method in a multi-agent system. The final goal of this research is an achievement of co-operations for hetero-agent in multi-agent systems. In this research for implementation for one type of agents is the first step of this goal. Then I have reported results of evaluation experiments using these agents. Index Terms—Machine learning, multi-agent system, swarm intelligence, ant colony optimization. I. I NTRODUCTION Recently, some researchers have reported the effectiveness of systems installed swarm intelligence algorithms (1)-(3). Ant Colony Optimization (ACO) and Ant Colony System (ACS) have become a very successful and widely used in some applications. These algorithms have been used in programs for network routing, traffic control programs, Traveling Salesman Problem (TSP) and so on (4)-(6). In real ants' feeding actions, they are able to find the shortest path from a food source to their nest by pheromone information. Pheromone is one of chemical materials and ants deposit pheromone on a path between a food source to their nest and it become one candidate solution. In a case that other ants trail a path deposited pheromone, the candidate is reinforced. Moreover pheromone disappears into air as time progresses. Ants have done these processes iteratively. The behavior of real ants has inspired ACO and ACS. The system based on ACO and ACS are used artificial ants cooperate to the solution of a problem by exchanging information via pheromone.