A Hybrid Genetic-Ant Colony Optimization Algorithm for the Optimal Path Selection

被引：18
|
作者：
Liu, Jiping [1 ]
Xu, Shenghua [1 ]
Zhang, Fuhao [1 ]
Wang, Liang [1 ]
机构：
[1] Chinese Acad Surveying & Mapping, Res Ctr Govt GIS, Beijing, Peoples R China
来源：
INTELLIGENT AUTOMATION AND SOFT COMPUTING | 2017年 / 23卷 / 02期
基金：
国家高技术研究发展计划(863计划);
关键词：
The optimal path selection; Genetic algorithm; ACO; ROUTING PROBLEM; SYSTEM;
D O I：
10.1080/10798587.2016.1196926
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The shortest path problem lies at the heart of network flows that seeks for the paths with minimum cost from source node to sink node in networks. This paper presents a hybrid genetic-ant colony optimization algorithmic approach to the optimal path selection problem. First, some existing solutions for the optimal path selection problem are analyzed, and some shortages and flaws are pointed out. Second, the data organization method for road network based on the graph theory is proposed. Furthermore, the optimal path selection algorithm integrated of sinusoidal probability transfer rules, pheromone update strategy and dual population is presented. Finally, the experimental results show that the proposed algorithm speeds up the convergence rate and improves the efficiency.
引用
收藏
页码：235 / 242
页数：8
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