Ant colony optimization algorithm based on pheromone diffusion

Guo Rui Huang; Xian Bin Cao; Xu Fa Wang

Ant colony optimization algorithm based on pheromone diffusion

Guo Rui Huang^*
, Xian Bin Cao
, Xu Fa Wang

^*此作品的通讯作者

University of Science and Technology of China

科研成果: 期刊稿件 › 文章 › 同行评审

43 引用（Scopus）

摘要

Ant Colony Optimization (AGO) 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 AGO such as lack and lag of collaboration among ants, this paper proposes a new AGO 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.

源语言	英语
页（从-至）	865-868
页数	4
期刊	Tien Tzu Hsueh Pao/Acta Electronica Sinica
卷	32
期	5
出版状态	已出版 - 5月 2004
已对外发布	是

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引用此

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title = "Ant colony optimization algorithm based on pheromone diffusion",

abstract = "Ant Colony Optimization (AGO) 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 AGO such as lack and lag of collaboration among ants, this paper proposes a new AGO 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.",

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AU - Cao, Xian Bin

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AB - Ant Colony Optimization (AGO) 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 AGO such as lack and lag of collaboration among ants, this paper proposes a new AGO 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.

KW - Ant colony optimization algorithm

KW - Ant colony system

KW - Diffusion mechanism

KW - Pheromone

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Ant colony optimization algorithm based on pheromone diffusion

摘要

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