最短路径问题的多目标蚁群优化算法

Q1 Social Sciences HumanMachine Communication Journal Pub Date : 2010-04-24 DOI:10.1109/MVHI.2010.67
Xiankun Sun, Xiaoming You, Sheng Liu
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引用次数: 8

摘要

针对最短路径问题,提出了一种新的多目标蚁群优化算法。首先,将每个路径段上的信息素初始化为初始值,并将蚂蚁随机分布在城市中。其次,采用自适应算子,即前期利用较高的概率探索更多的搜索空间,收集有用的全局信息;否则在后期我们使用更高的概率来加速收敛。MACO算法采用自适应算子使后期搜索范围缩小,从而大大缩短了算法的搜索时间。实际最短路由结果证明了MACO算法的优越性。
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Multi-objective Ant Colony Optimization Algorithm for Shortest Route Problem
A novel Multi-objective Ant Colony Optimization algorithm for shortest route problem (MACO) is proposed. Firstly, the pheromone on every path segment is initialized to an initial value and ants are randomly distributed among cities. Secondly, self-adaptive operator is used, namely in prophase we use higher probability to explore more search space and to collect useful global information; otherwise in anaphase we use higher probability to accelerate convergence. MACO algorithm adopts self-adaptive operator to make the search scope reduced in anaphase, thus the search time of this algorithm is reduced greatly. Real shortest route results demonstrate the superiority of MACO in this paper.
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来源期刊
CiteScore
10.00
自引率
0.00%
发文量
10
审稿时长
8 weeks
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