Research on symmetric TSP Based on MATLAB

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Resource download address ：https://download.csdn.net/download/sheziqiong/85717797
Based on symmetry TSP A study of the problem
pick want Travel agent problem （ abbreviation TSP） It's a famous NP-Hard problem , It is also a classical and important problem of discrete optimization , It is very important to study the related algorithms . This paper introduces TSP The problem itself is related to the problem , The solution is also discussed in detail TSP The dynamic programming method of the problem 、 Improved circle algorithm 、 Two exchange algorithm 、 Simulated annealing algorithm 、 Ant colony algorithm 、 Genetic algorithm (ga) , And by integrating various optimization methods , The genetic algorithm is optimized a little . For the improved circle algorithm in the test library 、 Simulated annealing algorithm 、 Ant colony algorithm 、 Genetic algorithm (ga) . This article USES the MATLAB Software implements these algorithms , The running time and reconciliation of these algorithms are compared and analyzed . The results show that the performance of the improved circle algorithm is the worst under any emptying , The rest of the algorithms are in small scale TSP There is little difference under the question , In larger TSP Under the problem , Simulated annealing takes the shortest time , Ant colony algorithm takes the longest time , The solution of genetic algorithm is the best . Experimental results show that , The effect of the improved genetic algorithm is not obvious , Improve the size of the solution 1%-3%, But time has improved 10%.
key word symmetry TSP problem ; Algorithm Research ; The approximate algorithm ; Simulated annealing ; Genetic algorithm (ga) ; Ant colony algorithm
Study based on symmetric TSP problem
Xu Bu-Fan Yan Yang-Sheng Dong Xiu-Liang Wang You-Wei Yu Yong-Xun
Abstract Travelling salesman problem (TSP) is both a well-known problem as a NP-Hard problem and a classical problem in the field of discrete-optimization. This paper first introduce something about itself, then discusses many ways such as dynamic programming, 2-opt, exchange algorithm, simulated annealing algorithm, ant colony optimization and genetic algorithm in detail. After that, this paper proposes an improved genetic algorithm by the means of integrating many papers’ methods.Finaly, this paper .Experiments show that Improved cycle algorithm perFORms worst, and when it comes to small TSP problems, those algorithms perFORms familiar. However, when solving big TSP problems, simulated annealing algorithm solves quickest as ant colony optimization takes longest, and YICHUAN makes the best solution. Experiments also show that the improved algorithm makes approximately 3%~5% improvement FOR the last length but time takes 10% more.
Key words Symmetric TSP problem; Algorithm research; approximate algorithm; simulated annealing; genetic algorithm; ant colony algorithm
Catalog
Based on symmetry TSP A study of the problem 1
Study based on symmetric TSP problem 1
1 introduction 2
1.1 TSP problem 2
2 Related concepts and work 3
2.1 TSP Overview of related algorithms 3
2.2 Dynamic programming algorithm 4
2.3 Improved circle algorithm 4
2.4 Exchange algorithm 4
2.5 Simulated annealing algorithm 5
2.6 Ant colony algorithm 5
2.7 Genetic algorithm (ga) 5
2.8 Guotao algorithm 5
3 Algorithm design 7
3.1 Simulated annealing algorithm 7
3.1.1 Algorithm design idea 7
3.1.2 Pseudo code 7
3.2 Improved circle algorithm 7
3.2.1 Algorithm design idea 7
3.2.2 Pseudo code 7
3.3 Dynamic programming algorithm 8
3.3.1 Algorithm design idea 8
3.3.2 Pseudo code 8
3.4 Ant colony algorithm 8
3.4.1 Algorithm design idea 8
3.4.2 Pseudo code 8
3.5 Genetic algorithm (ga) 9
3.5.1 Algorithm design idea 9
3.5.2 Pseudo code 9
3.6 Optimization of genetic algorithm 10
3.6.1 The design idea of the algorithm 10
3.6.2 How to improve [5] 10
4 experiment 10
4.1 Experimental environment 10
4.2 Analysis of experimental results 10
4.2.1 Improved circle algorithm 10
4.2.2 Simulated annealing algorithm 12
4.4.3 Ant colony algorithm 13
4.4.4 Genetic algorithm (ga) 15
4.4.5 Improved genetic algorithm 16
4.3 Overall data and analysis 18
5 reference 20
I. appendix 1—— Division of labor 22
II. appendix 2—— Experience and experience 22
(1)： 22
(2： 23
(3)： 23
(4)： 23
(5)： 23
III. appendix 3—— One of the failed improvements 23
Design document + Defence PPT+ Source code and data files
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Resource download address ：https://download.csdn.net/download/sheziqiong/85717797
Resource download address ：https://download.csdn.net/download/sheziqiong/85717797