A novel ant colony optimization algorithm in application of pheromone diffusion

Authors:
Peng Zhu;Ming-Sheng Zhao;Tian-Chi He
Affiliations:
Department of Information Management, Nanjing University, Nanjing, China;Nanjing Forest Police College, Nanjing, China;Nanjing University of Financial and Economics, Nanjing, China
Venue:
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
Year:
2010

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Abstract

Ant Colony Optimization (ACO) Algorithm is a novel stochastic search technology, which simulates the social behavior of ant colony. This paper firstly analyzes the shortcomings of basic ACO, then presents an enhanced ACO algorithm which is more faithful to real ants' behavior in application of pheromone diffusion. By setting up the pheromone diffusion model, the algorithm improves the collaboration among the nearby ants. The simulation results show that the proposed algorithm can not only get much more optimal solutions but also greatly enhance convergence speed.