Solving The Time Optimal Traveling Salesman Problem Based On Hybrid Shuffled Frog Leaping Algorithm-Genetic Algorithm

With the rapid development of China's tourism industry,there are more and more tourists that need to be accepted in the scenic spot.How to better manage the scenic spot and serve tourists has become the trend of the current tourism development.China's tourism industry can be divided into the off-season and the peak-season.In order to provide a recommended-path service for tourists with the shortest tour time in the peak-season,the Time Optimal Traveling Salesman Problem(TOTSP)is further studied,the fit function and Tanimoto coefficient are introduced into the fitness function of the algorithm and the tour time is used as the value of the fitness function,which is based on the classic and Symmetrical Traveling Salesman Problem(STSP).The main research work of this thesis can be summarized as the following:(1)The thesis takes into account the change of traffic over time and through the use of normal distribution to solve the TOTSP;(2)In order to provide a recommended-path service for tourists with the shortest tour time in the peak-season,the fit fmction and Tanimoto coefficient are introduced into the fitness function of the algorithm and the tour time is used as the value of the fitness function;(3)Firstly,the fitness function of the hybrid PSO-GA,SFLA and the hybrid SFLA-GA for solving the TOTSP is introduced.Secondly,the basic principle and the algorithm procedure of the hybrid PSO-GA,SFLA and the hybrid SFLA-GA for solving the TOTSP is introduced;(4)The experimental results of the SFLA,the hybrid PSO-GA and the hybrid SFLA-GA are compared and analyzed,it can be seen that the hybrid SFLA-GA has better performance in solving the TOTSP.(5)The hybrid SFLA-GA is studied for different number of the scenic spots.The experimental results show that the hybrid SFLA-GA is suitable for different scenic spot which has different number of the scenic spots,that is the number of the scenic spots does not affect the hybrid SFLA-GA to find the optimal solution quickly.The experimental results show that compared with the random tour path,the tour path significantly saves the tour time which is obtained by the hybrid SFLA-GA.Compared with SFLA and hybrid Particle Swarm Optimization-Genetic Algorithm(PSO-GA),the hybrid SFLA-GA has some advantages,such as less amount of calculation,fast speed of convergence,low dependency on initial population,good global superiority and so on.The hybrid SFLA-GA has stronger search capability and less search time in solving the TOTSP.Compared with the SFLA,the hybrid SFLA-GA has stronger adaptability and robustness.The hybrid SFLA-GA is suitable for different scenic spots with different number of scenic spots,that is the different number of scenic spots does not affect the hybrid SFLA-GA to quickly find the best solution and the hybrid SFLA-GA can provide a recommended-path service with the least time for tourists with the shortest tour time in the peak-season.