An Effective Initialization Strategy of Pheromone for Ant Colony Optimization

被引：0

作者：

Dai, Qiguo ^{[1
]}

Ji, Junzhong ^{[1
]}

Liu, Chunnian ^{[1
]}

机构：

[1] Beijing Univ Technol, Beijing Municipal Key Lab Multimedia & Intelligen, Coll Comp Sci & Technol, Beijing 100124, Peoples R China

来源：

2009 FOURTH INTERNATIONAL CONFERENCE ON BIO-INSPIRED COMPUTING: THEORIES AND APPLICATIONS, PROCEEDINGS | 2009年

关键词：

TSP; MST; Pheromone; ACO;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Aiming at the poor performance of convergence of ant colony optimization (ACO), in this paper, a novel pheromone initialization strategy of ACO for the traveling salesman problem (TSP) is put forward. More precisely, the pheromone matrix of a specific ACO algorithm is initialized by the minimal spanning tree (MST) information. Simulation results demonstrate that the proposed strategy could improve the convergence performance of ACO both in term of quality of solution and speed of convergence.

引用

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页码：398 / 401

页数：4

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