Abstract:In order to provide a recommended-path service for tourists with the shortest traveling time in the peak-season, the Time Optimal Traveling Salesman Problem (TOTSP) is further studied and the fit function is introduced into the fitness function of the hybrid Shuffled Frog Leaping Algorithm-Genetic Algorithm (SFLA-GA) to reflect the change of traffic over time, which is based on the classic and Symmetrical Traveling Salesman Problem (STSP). 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.
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