An improved adaptive genetic algorithm for mobile robot path planning analogous to TSP with constraints on city priorities

Jiang, Junjie and Yao, Xifan and Yang, Erfu and Mehnen, Jorn (2020) An improved adaptive genetic algorithm for mobile robot path planning analogous to TSP with constraints on city priorities. In: IEEE World Congress on Computational Intelligence 2020, 2020-07-19 - 2020-07-24.

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Abstract

The material transportation planning with a mobile robot can be regarded as the ordered clustered traveling salesman problem. To solve such problems with different priorities at stations, an improved adaptive genetic simulated annealing algorithm is proposed. Firstly, the priority matrix is defined according to station priorities. Based on standard genetic algorithm, the generating strategy of the initial population is improved to prevent the emergence of non-feasible solutions, and an improved adaptive operator is introduced to improve the population ability for escaping local optimal solutions and avoid premature phenomena. Moreover, to speed up the convergence of the proposed algorithm, the simulated annealing strategy is utilized in mutation operations. The experimental results indicate that the proposed algorithm has the characteristics of strong ability to avoid local optima and the faster convergence speed.

  • Item type:Conference or Workshop Item(Paper)
    ID code:72064
    Dates:
    Date
    Event
    10 April 2020
    Published
    15 March 2020
    Accepted
    Notes:© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Motor vehicles. Aeronautics. Astronautics
    Department:Faculty of Engineering > Design, Manufacture and Engineering Management
    Depositing user: Pure Administrator
    Date deposited:16 Apr 2020 12:41
    Last modified:17 Apr 2024 01:05
    URI:https://strathprints.strath.ac.uk/id/eprint/72064
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