Enhancing Whale Optimization Algorithm with Differential Evolution and Lévy Flight for Robot Path Planning

Abstract

Path planning is a prominent and essential part of mobile robot navigation in robotics. It allows robots to determine the optimal path from a given beginning point to a desired end goal. Additionally, it enables robots to navigate around obstacles, recognize secure pathways, and select the optimal route to follow, considering multiple aspects. The Whale Optimization Algorithm (WOA) is a frequently adopted approach to planning mobile robot paths. However, conventional WOA suffers from drawbacks such as a sluggish convergence rate, inefficiency, and local optimization traps. This study presents a novel methodology integrating WOA with Lévy flight and Differential Evolution (DE) to plan robot paths. As WOA evolves, the Levy flight promotes worldwide search capabilities. On the other hand, DE enhances WOA's ability to perform local searches and exploitation while also maintaining a variety of solutions to avoid getting stuck in local optima. The simulation results demonstrate that the proposed approach offers greater planning efficiency and enhanced route quality.