摘要
蚁群算法是一种新型的搜索算法 ,其模拟的是蚁群依赖信息素进行通信而表现出的社会性行为 .在基本蚁群算法中 ,蚂蚁之间协作不足 ,存在滞后的缺陷 .本文在分析这一算法的基础上 ,提出了一种新的更加忠实了真实蚁群信息系统的蚁群算法 .该算法通过建立信息素扩散模型 ,使相距较近的蚂蚁之间能更好地进行协作 .TSP问题的仿真结果表明了该算法的有效性 .
Ant Colony Optimization (ACO) Algorithm is a novel search algorithm which simulates the social behavior of ant colony depending on pheromone′s communication.Based on the analysis of shortcomings of basic ACO such as lack and lag of collaboration among ants,this paper proposes a new ACO which is more faithful to real ant colony system.By setting up the pheromone diffusion model,this algorithm improves the collaboration among ants which are nearby.The simulation results for TSP problem show the validity of it.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2004年第5期865-868,共4页
Acta Electronica Sinica
基金
国家自然科学基金 (No .60 2 0 4 0 0 9)
关键词
蚁群算法
蚁群系统
信息素
扩散机制
ant colony optimization algorithm
ant colony system
pheromone
diffusion mechanism
