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An Efficient End-to-End Obstacle Avoidance Path Planning Algorithm for Intelligent Vehicles Based on Improved Whale Optimization Algorithm

Author

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  • Chia-Hung Wang

    (College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
    Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou 350118, China)

  • Shumeng Chen

    (College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China)

  • Qigen Zhao

    (College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China)

  • Yifan Suo

    (College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China)

Abstract

End-to-end obstacle avoidance path planning for intelligent vehicles has been a widely studied topic. To resolve the typical issues of the solving algorithms, which are weak global optimization ability, ease in falling into local optimization and slow convergence speed, an efficient optimization method is proposed in this paper, based on the whale optimization algorithm. We present an adaptive adjustment mechanism which can dynamically modify search behavior during the iteration process of the whale optimization algorithm. Meanwhile, in order to coordinate the global optimum and local optimum of the solving algorithm, we introduce a controllable variable which can be reset according to specific routing scenarios. The evolutionary strategy of differential variation is also applied in the algorithm presented to further update the location of search individuals. In numerical experiments, we compared the proposed algorithm with the following six well-known swarm intelligence optimization algorithms: Particle Swarm Optimization (PSO), Bat Algorithm (BA), Gray Wolf Optimization Algorithm (GWO), Dragonfly Algorithm (DA), Ant Lion Algorithm (ALO), and the traditional Whale Optimization Algorithm (WOA). Our method gave rise to better results for the typical twenty-three benchmark functions. In regard to path planning problems, we observed an average improvement of 18.95% in achieving optimal solutions and 77.86% in stability. Moreover, our method exhibited faster convergence compared to some existing approaches.

Suggested Citation

  • Chia-Hung Wang & Shumeng Chen & Qigen Zhao & Yifan Suo, 2023. "An Efficient End-to-End Obstacle Avoidance Path Planning Algorithm for Intelligent Vehicles Based on Improved Whale Optimization Algorithm," Mathematics, MDPI, vol. 11(8), pages 1-31, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1800-:d:1120154
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    References listed on IDEAS

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    1. Marcelo Becerra-Rozas & Felipe Cisternas-Caneo & Broderick Crawford & Ricardo Soto & José García & Gino Astorga & Wenceslao Palma, 2022. "Embedded Learning Approaches in the Whale Optimizer to Solve Coverage Combinatorial Problems," Mathematics, MDPI, vol. 10(23), pages 1-18, November.
    2. Alejandro Rodríguez-Molina & Axel Herroz-Herrera & Mario Aldape-Pérez & Geovanni Flores-Caballero & Jarvin Alberto Antón-Vargas, 2022. "Dynamic Path Planning for the Differential Drive Mobile Robot Based on Online Metaheuristic Optimization," Mathematics, MDPI, vol. 10(21), pages 1-28, October.
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    Cited by:

    1. Chia-Hung Wang & Qigen Zhao & Rong Tian, 2023. "Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network," Energies, MDPI, vol. 16(11), pages 1-24, May.
    2. Yu, Wenjin & Zhou, Peijian & Miao, Zhouqian & Zhao, Haoru & Mou, Jiegang & Zhou, Wenqiang, 2024. "Energy performance prediction of pump as turbine (PAT) based on PIWOA-BP neural network," Renewable Energy, Elsevier, vol. 222(C).

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