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

Chia-Hung Wang (), Shumeng Chen (), Qigen Zhao and Yifan Suo
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Chia-Hung Wang: College of Computer Science and Mathematics, Fujian University of Technology, 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

Mathematics, 2023, vol. 11, issue 8, 1-31

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.

Keywords: path planning; end-to-end routing; obstacle avoidance method; swarm intelligence algorithms; heuristic random search (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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