GeoSSA: Geometric Sparrow Search Algorithm for UAV Path Planning and Engineering Design Optimization

2026-01-27

AI Summary

This study addresses the limitations of the original Sparrow Search Algorithm, which suffers from an imbalance between exploration and exploitation, premature convergence, and slow convergence speed. To overcome these issues, this work proposes a geometrically enhanced sparrow search framework that integrates a high-quality node set for initialization, a sine-cosine-enhanced position update mechanism for producers, and a triangle-walk-driven strategy for edge sparrows. The proposed approach significantly improves global exploration capability, local exploitation efficiency, and convergence stability while maintaining structural simplicity. Evaluated on 23 benchmark functions, the method achieves an overall effectiveness of 95.65%, outperforming state-of-the-art comparative algorithms. Furthermore, it demonstrates superior accuracy and robustness in both 3D unmanned aerial vehicle path planning and four distinct engineering design optimization tasks.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The Sparrow Search Algorithm (SSA), characterized by its simple structure and ease of implementation, nevertheless suffers from an insufficient balance between exploration and exploitation, making it prone to premature convergence and slow optimization progress. To address these shortcomings, this paper proposes a Geometric Sparrow Search Algorithm (GeoSSA). By integrating Good Nodes Set initialization, a Sine-Cosine Enhanced Producer position update strategy, and a Triangular-Walk Enhanced Edge Sparrow update strategy, GeoSSA significantly improves the global exploration ability, local exploitation efficiency, and convergence stability of the original SSA. To thoroughly validate the effectiveness of GeoSSA, we conducted ablation studies, qualitative analysis, and comparative experiments on 23 benchmark functions against state-of-the-art algorithms. Experimental results show that GeoSSA achieves the best or near-best performance in terms of average fitness, standard deviation, Wilcoxon tests, and Friedman rankings, with an Overall Effectiveness ($OE$) of 95.65\%. Its overall performance is significantly superior to all compared algorithms. In three-dimensional UAV path planning tasks, GeoSSA demonstrates excellent stability and superior path quality. In four categories of engineering design optimization problems, GeoSSA consistently attains the highest solution accuracy and strongest stability. GeoSSA not only exhibits outstanding global optimization performance on standard benchmark functions but also shows strong robustness and generalization ability in practical applications such as UAV path planning and engineering design. Therefore, GeoSSA provides an efficient and reliable solution framework for complex optimization problems.

Problem

• Sparrow Search Algorithm
• premature convergence
• exploration-exploitation balance
• UAV path planning
• engineering design optimization

Innovation

• Geometric Sparrow Search Algorithm
• Good Nodes Set initialization
• Sine-Cosine Enhanced Producer
• Triangular-Walk Enhanced Edge Sparrow
• UAV path planning