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

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