|Table of Contents|

Citation:
　Gongxiang Cui,Jie Song,Ying Han,et al.Chaos-Based Inertia Weight Particle Swarm Optimization for Path Planning of Unmanned Surface Vehicles[J].Journal of Marine Science and Application,2026,(5):1446-1455.[doi:10.1007/s11804-025-00713-5]
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Chaos-Based Inertia Weight Particle Swarm Optimization for Path Planning of Unmanned Surface Vehicles

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Title:
Chaos-Based Inertia Weight Particle Swarm Optimization for Path Planning of Unmanned Surface Vehicles
Author(s):
Gongxiang Cui1;  Jie Song1;  Ying Han1;  Haifang Wu1;  Fengyu Zhou2;  Yugang Wang1
Affilations:
Author(s):
Gongxiang Cui1;  Jie Song1;  Ying Han1;  Haifang Wu1;  Fengyu Zhou2;  Yugang Wang1
1 College of Naval Architecture and Marine Engineering, Shandong Jiaotong University, Weihai 264200, China;
2 School of Control Science and Engineering, Shandong University, Jinan 250061, China
Keywords:
Unmanned surface vehicles (USVs); Particle swarm optimization (PSO); Path planning; Chaos sequences; Adaptive inertia weights
分类号:
-
DOI:
10.1007/s11804-025-00713-5
Abstract:
Unmanned surface vehicles (USVs) play a vital role in marine construction, such as maritime patrol and search–rescue operations. However, USVs face complex challenges when performing tasks in path planning. An adaptive chaotic-enhanced particle swarm optimization (ACA-PSO) algorithm is presented in this paper to increase the efficiency of static path planning for USVs in intricate maritime environments. The proposed ACA-PSO algorithm addresses the dual challenges of path optimality and navigational safety while considering path length, smoothness, safety, and convergence speed. First, a sine chaotic sequence for initialization is introduced, which enhances global search capabilities and effectively escapes local optima. Second, an adaptive inertia weight decay strategy is proposed to adjust the weight dynamically to enhance convergence efficiency and solution quality. Third, sinusoidal acceleration learning factors are considered to alleviate the limitations of static cognitive and social learning factors. Fourth, a historical velocity weighting strategy is developed to improve trajectory smoothness and convergence robustness. Finally, extensive simulation results confirm that the proposed ACA-PSO algorithm significantly outperforms conventional PSO and other optimization algorithms in path planning effectiveness, convergence efficiency, and operational stability. Specifically, in optimal path distance, the proposed ACA-PSO algorithm achieves 2.4% and 4.9% improvements compared with traditional PSO and simulated annealing (SA) algorithms, respectively. The proposed ACA-PSO algorithm also demonstrates a 29.8% better grayscale value in path smoothness compared with the SA algorithm. Moreover, with a much lower standard deviation of 0.201 × 10-2, the ACA-PSO algorithm is superior in stability.

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Memo

Memo:
Received date:2025-4-20;Accepted date:2025-7-29。<br>Corresponding author:Yugang Wang,E-mail:sdu201413070@gmail.com
Last Update: 2026-11-04