通信学报 ›› 2014, Vol. 35 ›› Issue (1): 1-6.doi: 10.3969/j.issn.1000-436x.2014.01.001

• 学术论文 •    下一篇

自适应视野的人工鱼群算法求解最短路径问题

马宪民,刘妮   

  1. 西安科技大学 电气与控制工程学院,陕西 西安 710054
  • 出版日期:2014-01-25 发布日期:2017-06-17
  • 基金资助:
    陕西省自然科学基金资助项目

Improved artificial fish-swarm algorithm based on adaptive vision for solving the shortest path problem

Xian-min MA,IUN L   

  1. College of Electrical and Control Engineering,Xi'an University of Science & Technology,Xi'an 710054,China
  • Online:2014-01-25 Published:2017-06-17
  • Supported by:
    The Natural Science Foundation of Shaanxi Province

摘要:

针对基本人工鱼群算法的参数视野固定不变导致算法后期收敛速度慢、运算量大、易陷入局部最优等问题,提出自适应视野的改进人工鱼群算法。改进后的算法只对人工鱼的觅食行为的视野进行调整,使其随着算法的迭代次数的增加而逐渐减小,但当视野小于初始值的一半时,停止减小,使其等于初始值的一半。将提出的改进型人工鱼群算法应用到求解基于道路网络的最短路径问题中,并通过实验证明了改进后的人工鱼群算法比基本人工鱼群算法及蚁群优化算法收敛速度快、计算量小,而且更加准确和稳定。

关键词: 最短路径, 人工鱼群算法, 自适应视野, 蚁群优化算法

Abstract:

To solve basic artificial fish-swarm algorithm(AFSA)’s drawbacks of low convergence rate in the latter stage,a large amount of computation and easiness of trapping in local optimal solution,caused by the constant vision of the artificial fish,an improved artificial fish-swarm algorithm based on adaptive vision(AVAFSA) was proposed.The improved algorithm only adjusted the vision of the preying behavior of artificial fish to make the vision gradually decrease with the increase of the number of iterations of the algorithm.When the value became less than half the initial value,it made the value be equal to half the initial value.The proposed improved artificial fish swarm algorithm was applied to the static shortest path problem based on road network to provide customers with the best path.Simulation results depict the improved algorithm has higher convergence rate,a smaller amount of calculation,and is more accurate and stable than the basic AFSA and ant colony optimization(ACO).

Key words: shortest path, artificial-fish swarm algorithm, adaptive vision, ant colony optimization

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