A mutation operator self-adaptive differential evolution particle swarm optimization algorithm for USV navigation

To keep the global search capability and robustness for unmanned surface vessel (USV) path planning, an improved differential evolution particle swarm optimization algorithm (DePSO) is proposed in this paper. In the optimization process, approach to optimal value in particle swarm optimization algor...

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Veröffentlicht in:Frontiers in neurorobotics Jg. 16; S. 1076455
Hauptverfasser: Gong, Yuehong, Zhang, Shaojun, Luo, Min, Ma, Sainan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Research Foundation 06.12.2022
Frontiers Media S.A
Schlagworte:
Algorithms
differential evolution algorithm
Genetic algorithms
Hybridization
Mathematical models
Mutation
Neuroscience
Optimization algorithms
particle swarm optimization
Planning
scaling factor
Traveling salesman problem
Unmanned aerial vehicles
unmanned surface vessel path planning
Water area
mutation
differential evolution algorithm
particle swarm optimization
scaling factor
hybridization
unmanned surface vessel path planning
ISSN:1662-5218, 1662-5218
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Zusammenfassung:To keep the global search capability and robustness for unmanned surface vessel (USV) path planning, an improved differential evolution particle swarm optimization algorithm (DePSO) is proposed in this paper. In the optimization process, approach to optimal value in particle swarm optimization algorithm (PSO) and mutation, hybridization, selection operation in differential evolution algorithm (DE) are combined, and the mutation factor is self-adjusted. First, the particle population is initialized and the optimization objective is determined, the individual and global optimal values are updated. Then differential variation is conducted to produces new variables and cross over with the current individual, the scaling factor is adjusted adaptively with the number of iterations in the mutation process, particle population is updated according to the hybridization results. Finally, the convergence of the algorithm is determined according to the decision standard. Numerical simulation results show that, compared with conventional PSO and DE, the proposed algorithm can effectively reduce the path intersection points, and thus greatly shorten the overall path length.
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Reviewed by: Tianrui Zhou, Shanghai Maritime University, China; Zhihui Hu, Jimei University, China
Edited by: Namkyun I. M., Mokpo National Maritime University, South Korea
ISSN:1662-5218
1662-5218
DOI:10.3389/fnbot.2022.1076455