自适应视野的人工鱼群算法求解最短路径问题

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

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。

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 arti- ficial 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 im- proved algorithm has higher convergence rate, a smaller amotmt of calculation, and is more accurate and stable than the basic AFSA and ant colony optimization（ACO）.