• 中国科技核心期刊
  • JST收录期刊
Volume 30 Issue 5
Oct  2022
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FAN Xue-man, XUE Chang-you, ZHANG Hui. Task Assignment Method for Multiple UUVs Based on Multi-population Genetic Algorithm[J]. Journal of Unmanned Undersea Systems, 2022, 30(5): 621-630. doi: 10.11993/j.issn.2096-3920.202107001
Citation: FAN Xue-man, XUE Chang-you, ZHANG Hui. Task Assignment Method for Multiple UUVs Based on Multi-population Genetic Algorithm[J]. Journal of Unmanned Undersea Systems, 2022, 30(5): 621-630. doi: 10.11993/j.issn.2096-3920.202107001

Task Assignment Method for Multiple UUVs Based on Multi-population Genetic Algorithm

doi: 10.11993/j.issn.2096-3920.202107001
  • Received Date: 2021-07-05
  • Rev Recd Date: 2021-09-05
  • Available Online: 2022-09-15
  • The quality of task assignment schemes for multiple unmanned undersea vehicles(UUVs) during cooperative reconnaissance is key to the operational effectiveness or even the success of missions. A multi-population genetic algorithm based on man-machine fusion was proposed for task allocation of multi-base, multi-target, and multi-constraint cooperative reconnaissance to solve the problems of premature convergence and low efficiency of traditional genetic algorithms. The algorithm can better balance global optimization and local search and break through the performance bottleneck of the classical genetic algorithm, which relies only on a single population for optimization, by introducing multiple populations to search the solution space cooperatively. In addition, human prior knowledge was used as heuristic information to assist population initialization in improving the convergence efficiency of the algorithm, and a forgetting strategy was introduced to alleviate possible incomplete evolution. The results of the simulation based on typical scenarios show that the proposed multi-population genetic algorithm is robust, has high optimization efficiency, and can generate high-quality collaborative task allocation schemes..

     

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