A New Island Genetic Algorithm for Path Planning Problems

Genetic algorithm (GA) is an effective method for path planning problems. As a powerful variant of GA, island genetic algorithm (IGA) has considerable improvement in performance. In this paper, a new island model of GA is proposed to avoid the premature phenomenon and achieve better efficiency. Firs...

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Bibliographic Details
Published in2023 IEEE International Conference on Real-time Computing and Robotics (RCAR) pp. 390 - 395
Main Authors Zhang, Jinghao, Chen, Zhe, Li, Ning
Format Conference Proceeding
LanguageEnglish
Published IEEE 17.07.2023
Subjects
Costs
Heuristic algorithms
Monte Carlo methods
Path planning
Real-time systems
Sociology
Uncertainty
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DOI10.1109/RCAR58764.2023.10249875

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Summary:Genetic algorithm (GA) is an effective method for path planning problems. As a powerful variant of GA, island genetic algorithm (IGA) has considerable improvement in performance. In this paper, a new island model of GA is proposed to avoid the premature phenomenon and achieve better efficiency. First, a new method of creating subpopulations is presented based on K-means to expand the searching area for the optima. Meanwhile, recombining subpopulations is proposed as a new strategy to improve the diversity of populations and save computational time. Moreover, a method is designed based on Monte Carlo sampling to handle the uncertainty of maps. Comparative experiments are presented to verify the efficiency of the proposed algorithm. Then, a proper number of samples is found by simulation to balance the accuracy and the time cost of Monte Carlo sampling.
DOI:10.1109/RCAR58764.2023.10249875