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- W4386071924 abstract "The improved particle swarm optimization (PSO) is proposed for the three-dimensional space path planning of unmanned underwater vehicle (UUV) to solve the problem that the classical PSO is easy to fall into the local optimal solution. Firstly, the elevation environment model is established according to the UUV navigation environment, and the fitness evaluation function is formulated by comprehensively considering the path length, threat factors, UUV performance constraints and other conditions. Secondly, the inertial weight of the particle swarm algorithm introduces the beta distribution based on the cosine dynamic adjustment. Thirdly, adopt the reverse learning mechanism for the optimal particle in the algorithm iteration. Finally, the encirclement strategy of the grey wolf algorithm optimizes the iterative update formula. By designing two application scenarios and comparing GWO-PSO with PSO, grey wolf optimizer (GWO) and GA-PSO, the simulation results show that GWO-PSO has stronger global search ability and higher optimization accuracy." @default.
- W4386071924 created "2023-08-23" @default.
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- W4386071924 date "2023-08-06" @default.
- W4386071924 modified "2023-10-18" @default.
- W4386071924 title "Global Path Planning for UUV Based on Grey Wolf-Particle Swarm Optimization" @default.
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- W4386071924 doi "https://doi.org/10.1109/icma57826.2023.10216252" @default.
- W4386071924 hasPublicationYear "2023" @default.
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