Improved ant colony optimization based on particle swarm optimization and its application

被引：0

作者：

Zhang, Chao ^{[1
]}

Li, Qing ^{[1
]}

Chen, Peng ^{[2
]}

Yang, Shou-Gong ^{[1
]}

Yin, Yi-Xin ^{[1
]}

机构：

[1] School of Automation, University of Science and Technology Beijing, Beijing 100083, China

[2] National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China

来源：

Beijing Keji Daxue Xuebao/Journal of University of Science and Technology Beijing | 2013年 / 35卷 / 07期

关键词：

Elitist strategies - Improved ant colony optimization - Inspection robots - Iteration step - ITS applications - Novel algorithm - Path planning problems - Search speed;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

This article introduces a novel algorithm to solve the large time-consuming problem of the existing improved ant colony optimization (ACO) based on particle swarm optimization (PSO). A new pheromone update method which combines the global asynchronous feature and elitist strategy was used in the algorithm. Moreover, the iteration steps of ACO invoked by PSO were reasonably reduced. The algorithm was applied to solve the path planning problem of landfill inspection robots in Asahikawa, Japan. It is shown that the algorithm has a better performance in search speed compared with other algorithms recently reported.

引用

页码：955 / 960

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