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

2025-06-04

AI Summary

To address the limitations of conventional heuristic algorithms—namely, premature convergence to local optima and redundant path generation—in shortest-path planning for unmanned aerial vehicles (UAVs) under complex obstacle environments, this paper proposes an Improved Grey Wolf Optimizer (IGWO) integrating Advanced Cooperative Predation (ACP) and Lens Opposition-Based Learning (LOBL). ACP enhances global collaborative search capability, while LOBL improves initial population diversity and facilitates escape from local optima. Experimental evaluation on 12 benchmark functions demonstrates that IGWO achieves superior performance on seven functions. In path planning across four representative map scenarios, IGWO reduces average path length by 1.70 m, 1.68 m, and 2.00 m compared to standard GWO, PSO, and WOA, respectively. The results confirm significant improvements in global search capability, convergence accuracy, and practical path feasibility.

Technology Category

• Search and Optimization: Heuristic Search
• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Algorithmic accountability and transparency on the web

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.

Problem

• Optimizing UAV shortest path planning in obstacle environments
• Overcoming local optima in heuristic-based path planning methods
• Enhancing Grey Wolf Optimizer with cooperative predation for better performance

Innovation

• Improved Grey Wolf Optimizer (IGWO)
• Advanced Cooperative Predation (ACP)
• Lens Opposition-based Learning (LOBL)