RT info:eu-repo/semantics/article
T1 Enhancing unmanned marine vehicle path planning: A fractal-enhanced chaotic grey wolf and differential evolution approach
A1 Zhu, Chaoyang
A1 Bouteraa, Yassine
A1 Khishe, Mohammad
A1 Martín De Andrés, Diego
A1 Hernando Gallego, Francisco
A1 Vaiyapuri, Thavavel
K1 Robótica
K1 Navegación marítima
K1 Inteligencia artificial
K1 Optimización matemática
K1 Planificación de rutas
K1 Vehículo marítimo no tripulado
K1 Mapas caóticos fractales
K1 Optimizador del lobo gris
K1 Evolución diferencial
K1 12 Matemáticas
K1 1203 Ciencia de Los Ordenadores
K1 3319 Tecnología Naval
AB Efficient path planning is challenging for optimizing the trajectory of uncrewed marine vehicles navigating complex environments. However, when the global optimum is zero, path planning optimization encounters a significant challenge, a major shortcoming of the grey wolf optimizer (GWO). This study intentionally integrates multiple approaches to present a comprehensive methodology called fractal-enhanced chaotic GWO (FECGWO) in conjunction with differential evolution (DE) to fill this research gap. This method uses DE to strengthen the local search or exploitation phases, chaotic maps to improve the exploration phase, and fractals to fine-tune the transition between the two phases. In addition to testing against 46 sophisticated benchmark maps, this study carries out practical experimentation over commonly utilized meta-heuristic algorithms to comprehensively evaluate the proposed hybrid model's performance (FECGWO-DE). This thorough evaluation demonstrates notable advancements in unmanned marine vehicle path planning. The evaluation criteria include path length, consistency, time complexity, and success rate. These metrics illustrate the statistical significance of the novel methodology's improvements. The study demonstrates that FECGWO can precisely identify the best routes in given test maps, offering insightful information for developing path planning optimization—especially concerning unmanned marine vehicles.
PB Elsevier
SN 0950-7051
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/83811
UL https://uvadoc.uva.es/handle/10324/83811
LA eng
NO Knowledge-Based Systems, 2025, vol. 317, artículo 113481.
NO Producción Científica
DS UVaDOC
RD 28-mar-2026