Reserach On Squirrel Serach Algorithma And Its Application To Solving Traveling Salesman Problem

Squirrel Search Algorithm is a swarm intelligence optimization algorithm proposed by Indian scholar Mohit Jain.This algorithm mainly simulates the dynamic foraging behavior and gliding flight mechanism of flying squirrels.Due to its excellent performance,it has attracted wide attention from researchers in recent years.Since squirrel search algorithm was proposed,it has been widely used to solve target optimization problems in many fields.TSP(Traveling Salesman Problem)is a classical combinatorial optimization problem,and many optimization problems can be reduced to solving TSP.In this paper,the basic squirrel search algorithm is improved to solve different types of travel agent problems.The main research work of this paper is as follows:(1)In order to solve the traveling salesman problem,natural numbers are used to encode the path,and a discrete squirrel search algorithm based on greedy strategy is proposed.In this algorithm,two operators and one strategy are added to the traveling salesman problem,and the initialization method and fitness function are adjusted accordingly.By testing the data set TSPLIB,the algorithm gets better results.(2)In order to solve the problem of multiple travel agents,a hybrid algorithm GSSA-Kmeans++ is proposed on the basis of the greedy squirrel search algorithm.All cities are clustered into several urban agglomerations by using the K-means++ algorithm.Secondly,a new coding method,mapping coding,is designed for urban agglomeration after clustering.Through the mapping relationship between virtual codes and real codes,the path update operator of GSSA algorithm is combined to complete the update of the path sequence.Moreover,the algorithm can be adjusted adaptively according to the scale of solving problems to reduce the time complexity of solving small and medium-sized multi-travel salesman problems.