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Dynamic strategy-based hybrid genetic algorithm for solving multi-task decision-making problems for heterogeneous surface vessels
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AI
DOI：10.1016/j.swevo.2025.102206.png)
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Abstract
En 中文
The rapid advancement of marine robotics, especially marine unmanned surface vessels, has revolutionized their use in maritime missions ranging from military operations to civilian tasks. This development is critical for sustainable marine assignments, decreasing costs, enhancing surface vessel collaboration flexibility, and improving safety. The control of multiple vessel formations for efficiently performing specific tasks is a significant achievement in a complex marine environment. For huge task domains and a heterogeneous fleet, a significant optimization challenge is to enhance the efficiency of cooperative heterogeneous surface vessels to accomplish multiple tasks and satisfy task demands under limited temporal intervals and vessel operation capabilities. In this paper, a mathematical framework of the heterogeneous surface vessel multi-task decision-making problem is proposed. Several dynamic algorithms are adopted to optimize the population distribution of the genetic algorithm. A 2-opt algorithm is adopted to construct the hybrid genetic algorithm. A novel dynamic hybrid genetic algorithm containing the dynamic algorithm and the 2-opt algorithm is developed, which can improve local search ability and enhance computational efficiency. Compared with several common evolutionary algorithms, hybrid evolutionary algorithms and other latest state-of-the-art (SOTA) algorithms, the dynamic hybrid genetic algorithm can achieve a better route sequence and shorter sailing time for this multi-task decision-making problem.
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