Ant Colony Optimization Algorithm Based on Directional Pheromone Diffusion  

作　　者：HUANG Guorui WANG Xufa CAO Xianbin 

机构地区：[1]New Star Research Institute of Applied Technology, Hefei 230031, China [2]Computer Department, University of Science and Technolgy of China, Hefei 230027, China

出　　处：《Chinese Journal of Electronics》2006年第3期447-450,共4页电子学报（英文版）

基　　金：This work is supported by the National Natural Science Foundation of China （No.60204009）.

摘　　要：Ant colony optimization （ACO） Algorithm is a novel search algorithm, which simulates the social behaviors of ant colony for solving complicated combinatorial optimization problems. With the analysis of shortcomings of basic ACO such as lack and lag of collaboration among ants, ACO algorithm based on Pheromone diffusion （ACOPD） has been proposed. It is proved that ACOPD can improve the collaboration among nearby ants and converge to a local solution soon, but it often gets into a local optimum solution without escaping from it. In order to avoid the problem, this article will introduce an ACO algorithm based on Directional pheromone diffusion （ACODPD）, which is based on the idea that pheromone strength on a path affects the amount of pheromone diffusing to this path. The contrastive simulation results for TSP problem show that our new algorithm ACODPD has much higher convergence speed and stronger capability of finding optimal solutions than the ACOPD.

关 键 词：Ant colony optimization Directional pheromone diffusion CONVERGENCE Stagnation. 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]