Aimed at a multiple traveling salesman problem(MTSP)with multiple depots and closed paths,this paper proposes a k-means clustering donkey and a smuggler algorithm(KDSA).The algorithm first uses the k-means clustering ...Aimed at a multiple traveling salesman problem(MTSP)with multiple depots and closed paths,this paper proposes a k-means clustering donkey and a smuggler algorithm(KDSA).The algorithm first uses the k-means clustering method to divide all cities into several categories based on the center of various samples;the large-scale MTSP is divided into multiple separate traveling salesman problems(TSPs),and the TSP is solved through the DSA.The proposed algorithm adopts a solution strategy of clustering first and then carrying out,which can not only greatly reduce the search space of the algorithm but also make the search space more fully explored so that the optimal solution of the problem can be more quickly obtained.The experimental results from solving several test cases in the TSPLIB database show that compared with other related intelligent algorithms,the K-DSA has good solving performance and computational efficiency in MTSPs of different scales,especially with large-scale MTSP and when the convergence speed is faster;thus,the advantages of this algorithm are more obvious compared to other algorithms.展开更多
Multi-traveling salesman problem(MTSP) is an extension of traveling salesman problem,which is a famous NP hard problem,and can be used to solve many real world problems,such as railway transportation,routing and pipel...Multi-traveling salesman problem(MTSP) is an extension of traveling salesman problem,which is a famous NP hard problem,and can be used to solve many real world problems,such as railway transportation,routing and pipeline laying.In this paper,we analyze the general properties of MTSP,and find that the multiple depots and closed paths in the graph is a big issue for MTSP.Thus,a novel method is presented to solve it.We transform a complicated graph into a simplified one firstly,then an effective algorithm is proposed to solve the MTSP based on the simplified results.In addition,we also propose a method to optimize the general results by using 2-OPT.Simulation results show that our method can find the global solution for MTSP efficiently.展开更多
基金the Natural Science Basic Research Program of Shaanxi(2021JQ-368).
文摘Aimed at a multiple traveling salesman problem(MTSP)with multiple depots and closed paths,this paper proposes a k-means clustering donkey and a smuggler algorithm(KDSA).The algorithm first uses the k-means clustering method to divide all cities into several categories based on the center of various samples;the large-scale MTSP is divided into multiple separate traveling salesman problems(TSPs),and the TSP is solved through the DSA.The proposed algorithm adopts a solution strategy of clustering first and then carrying out,which can not only greatly reduce the search space of the algorithm but also make the search space more fully explored so that the optimal solution of the problem can be more quickly obtained.The experimental results from solving several test cases in the TSPLIB database show that compared with other related intelligent algorithms,the K-DSA has good solving performance and computational efficiency in MTSPs of different scales,especially with large-scale MTSP and when the convergence speed is faster;thus,the advantages of this algorithm are more obvious compared to other algorithms.
基金supported by the National Natural Science Foundation of China (61073177)
文摘Multi-traveling salesman problem(MTSP) is an extension of traveling salesman problem,which is a famous NP hard problem,and can be used to solve many real world problems,such as railway transportation,routing and pipeline laying.In this paper,we analyze the general properties of MTSP,and find that the multiple depots and closed paths in the graph is a big issue for MTSP.Thus,a novel method is presented to solve it.We transform a complicated graph into a simplified one firstly,then an effective algorithm is proposed to solve the MTSP based on the simplified results.In addition,we also propose a method to optimize the general results by using 2-OPT.Simulation results show that our method can find the global solution for MTSP efficiently.