This paper adopts a combined optimization approach using Simulated Annealing and Genetic Algorithm based on the travel purpose as a determining criterion. It selects one destination among multiple travel destinations and derives the optimal route based on the latitude and longitude information of the destination. The research aims to overcome the limitations of traditional navigation software, which requires explicit destination input to plan a route, and achieve intelligent route planning based on travel purposes. By identifying the travel purpose and selecting appropriate destinations to form the optimal solution, this study successfully addresses the limitations of conventional travel planning. The combined algorithm makes full use of the global search capability of Simulated Annealing and the optimization effectiveness of Genetic Algorithm, achieving superior travel routes by optimizing the initial solution. Experimental results and comparative analysis demonstrate that the travel route planning method based on the combination of Simulated Annealing and Genetic Algorithm exhibits significant advantages in solving complex travel problems, providing users with intelligent and efficient travel decision support. This research contributes to the innovation and improvement of travel planning technology, bringing greater convenience and comfort to the travel experience.


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    Title :

    Optimization of urban travel routes based on simulated annealing and genetic algorithm


    Contributors:
    Saxena, Sandeep (editor) / Zhao, Cairong (editor) / Qiu, Peizhen (author) / Wang, Jing (author) / Gu, Shuhuai (author) / Wu, Dongran (author) / Xi, Qi (author)

    Conference:

    International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023) ; 2023 ; Yinchuan, China


    Published in:

    Proc. SPIE ; 12941


    Publication date :

    2023-12-07





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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