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Road Tour Planning Based On ACO Intelligent Optimization Algorithm

Published: 31 December 2021 Publication History

Abstract

"The world is so big that I want to see it" is a strong expression of young people who want to discover the world. At the moment, the most popular way to see the world is a road trip, which includes not only road trips but also ocean sailing. The most basic and important thing of road trip is path planning which can be defined as the traveling salesman problem model. Aiming at the problems that the existing APP with multiple scenic spots and the long-term route planning scheme cannot be generated, this paper collected the path data of 33 classic scenic spots in Hainan Province, used ACO ant colony algorithm to solve the relatively satisfactory path solution and used the UI web interface to solve the needs of multiple scenic spots and long-term free choice. Compared with the greedy algorithm as a reference, the satisfactory solution obtained by the ACO algorithm saved 30% of the cost in only 7 scenic spots.

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  • (2023)Intelligent Tourism Route Optimization Based on Teaching and Learning Optimization Algorithms2023 World Conference on Communication & Computing (WCONF)10.1109/WCONF58270.2023.10235189(1-6)Online publication date: 14-Jul-2023

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    EITCE '21: Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering
    October 2021
    1723 pages
    ISBN:9781450384322
    DOI:10.1145/3501409
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 31 December 2021

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    Author Tags

    1. ACO
    2. greedy algorithm
    3. path planning
    4. road tour
    5. sail tour

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    EITCE '21 Paper Acceptance Rate 294 of 531 submissions, 55%;
    Overall Acceptance Rate 508 of 972 submissions, 52%

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    • (2023)Intelligent Tourism Route Optimization Based on Teaching and Learning Optimization Algorithms2023 World Conference on Communication & Computing (WCONF)10.1109/WCONF58270.2023.10235189(1-6)Online publication date: 14-Jul-2023

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