改进的人工鱼群算法求解TSP问题的研究

Research on Improved Artificial Fish Swarm Algorithm Solving TSP Problem

针对旅行商(traveling salesman problem,TSP)是一个NP问题,本文使用改进的人工鱼群算法(improved artificial fish swarm algorithm,AFSA)进行线路的优化。首先阐述了TSP问题基本概念,其次针对基本的人工鱼群算法分别优化:(1)使用Laplace进行种群初始化,提高种群多样性;(2)使用正弦余弦算法取代觅食行为,保证算法在全局和局部范围内具有一定的平衡性;(3)利用人工蜂群算法对每一次迭代后的个体进行筛选,保证了算法的解的质量。仿真实验中本文算法在TSP路径规划方面具有一定的效果。

As Traveling Salesman Problem(TSP)is a NP problem,this paper uses Improved artificial fish swarm algorithm(IAFSA)to optimize the circuit.Firstly,the basic concept of TSP problem is described,and then the basic AFSA is optimized respectively:(1)Using Laplace for population initialization to improve population diversity;(2)Using sine and cosine algorithm to replace foraging behavior to ensure that the algorithm has a certain balance in the global and local range;(3)Using Artificial bee colony Algorithm to screen the individuals after each iteration to ensure the quality of the solution of the algorithm.In the simulation experiment,the algorithm in this paper has a certain effect in TSP path planning.