Abstract
In this paper, the global path planning research is carried out using particle swarm algorithm in combination with the characteristics of unmanned boat navigation environment. To address the problem that the particle swarm algorithm is easy to fall into local optimum at the later stage, we first integrate chaos theory into the basic particle swarm algorithm, and generate chaotic population and replace some particles that fall into local optimum by chaotic iteration of contemporary global optimum, which improves the problem of insufficient particle diversity at the later stage of population search; meanwhile, to strengthen the local search ability of the algorithm, we combine the particle swarm algorithm with the following bee. To enhance the local search capability of the algorithm, we combine the particle swarm algorithm with the following bee strategy in the swarm search algorithm and propose the chaotic particle swarm-bee swarm algorithm. The improved algorithm is applied to the global path planning, and the simulation verifies the advantages of the search algorithm in terms of convergence speed and search accuracy.
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Liu, C., Liu, K. (2023). Global Path Planning for Unmanned Ships Based on Improved Particle Swarm Algorithm. In: Pan, L., Zhao, D., Li, L., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2022. Communications in Computer and Information Science, vol 1801. Springer, Singapore. https://doi.org/10.1007/978-981-99-1549-1_9
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DOI: https://doi.org/10.1007/978-981-99-1549-1_9
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