An Improved Grey Wolf Optimizer Inspired by Advanced Cooperative Predation for UAV Shortest Path Planning

TL;DR

This paper introduces an improved Grey Wolf Optimizer (IGWO) enhanced with advanced cooperative predation and opposition-based learning, demonstrating superior optimization and UAV shortest path planning performance in complex environments.

Contribution

The study presents a novel IGWO algorithm with integrated ACP and LOBL strategies, significantly improving optimization and path planning for UAVs over existing methods.

Findings

IGWO outperforms benchmark functions F1-F5, F7, and F9-F12.

Paths planned by IGWO are shorter by approximately 1.7 meters.

IGWO achieves better results than GWO, PSO, and WOA in UAV path planning.

Abstract

With the widespread application of Unmanned Aerial Vehicles (UAVs) in domains like military reconnaissance, emergency rescue, and logistics delivery, efficiently planning the shortest flight path has become a critical challenge. Traditional heuristic-based methods often suffer from the inability to escape from local optima, which limits their effectiveness in finding the shortest path. To address these issues, a novel Improved Grey Wolf Optimizer (IGWO) is presented in this study. The proposed IGWO incorporates an Advanced Cooperative Predation (ACP) and a Lens Opposition-based Learning Strategy (LOBL) in order to improve the optimization capability of the method. Simulation results show that IGWO ranks first in optimization performance on benchmark functions F1-F5, F7, and F9-F12, outperforming all other compared algorithms. Subsequently, IGWO is applied to UAV shortest path planning in various obstacle-laden environments. Simulation results show that the paths planned by IGWO are, on average, shorter than those planned by GWO, PSO, and WOA by 1.70m, 1.68m, and 2.00m, respectively, across four different maps.