文章基本信息

标题：Dynamic ant colony algorithm based on cw saving Value
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作者：Yao Jiang-Gu ; Gao Jian
期刊名称：MATEC Web of Conferences
电子版ISSN：2261-236X
出版年度：2018
卷号：246
DOI：10.1051/matecconf/201824603015
语种：English
出版社：EDP Sciences
摘要：As a swarm intelligence optimization algorithm, ant colony algorithm (ACO) has a good application in combinatorial optimization problems, in which traveling salesman problem (TSP) is an important application of ACO algorithm. It shows the powerful ability of ant colony algorithm to find short paths through graphics. However, there are obvious defects in the ant colony algorithm. When the scale of the ant colony is large, the convergence time of the algorithm becomes longer and the local optimal state is easy to fall into. In this paper, a dynamic pheromone ant colony optimization algorithm based on CW saving algorithm is proposed. Initially, a general path range is found by CW saving value algorithm, and the pheromone matrix can be reasonably configured, so that the ant colony algorithm can quickly get a better solution in the initial optimization. At the same time, the optimization scheme can be adjusted in real time according to the situation of path optimization. Large ant colony searches for other paths. Combined with 3-opt local search algorithm, the ant colony can find the optimal path more quickly. The experimental results show that the improved ant colony algorithm has better convergence speed and solution quality than other ant colony algorithms.