[1]JI Jun-zhong,LIU Chun-nian,HUANG Zhen.An ant colony optimization algorithm based on a decouplingcontrol strategy of pheromone diffusion model[J].CAAI Transactions on Intelligent Systems,2007,2(4):1-8.
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An ant colony optimization algorithm based on a decouplingcontrol strategy of pheromone diffusion model

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 2 Number of periods: 2007 4 Page number: 1-8 Column: 学术论文—人工智能基础 Public date: 2007-08-25
Title:
An ant colony optimization algorithm based on a decouplingcontrol strategy of pheromone diffusion model
Author(s):
JI Jun-zhong;  LIU Chun-nian;  HUANG Zhen
College of Computer Science and Technology, Beijing University of Technology, Beijing 100022, China
Keywords:
ant colony optimization;  diffusion model;  coupling characteristic;  de coupling control strategy
CLC:
TP18
DOI:
-
Abstract:
Ant colony optimization (ACO) is a metaheuristic search technique. P h eromones are an important media ants use to communicate with each other and impl ement swarm intelligence. Thus research on pheromone updating strategies is a ho tspot in ACO. A new decoupling control strategy model of pheromone diffusion is proposed based on the coupling characteristic of pheromone diffusion. First, the algorithm sets up a new pheromone diffusion model with the path that the ant tr avels as the pheromone source. Then, according to the coupling degree of the con centration field of pheromone diffusion, a decoupling control strategy is employ ed to revise the pheromone updating formula. Experimental results for many TSP p roblems demonstrate that the proposed algorithm can not only generate better sol utions but also accelerate the speed of convergence. 
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