A new method for a cooperative multi-task allocation problem (CMTAP) is proposed in this paper,
taking into account the multi-ship, multi-target, multi-task and multi-constraint characteristics in a multi-ship
cooperative driving (MCD) system. On the basis of the general CMTAP model, an MCD task assignment model
is established. Furthermore, a genetic ant colony hybrid algorithm (GACHA) is proposed for this model using
constraints, including timing constraints, multi-ship collaboration constraints and ship capacity constraints. This
algorithm uses a genetic algorithm (GA) based on a task sequence, while the crossover and mutation operators
are based on similar tasks. In order to reduce the dependence of the GA on the initial population, an ant colony
algorithm (ACA) is used to produce the initial population. In order to meet the environmental constraints of
ship navigation, the results of the task allocation and path planning are combined to generate an MCD task
planning scheme. The results of a simulated experiment using simulated data show that the proposed method
can make the assignment more optimized on the basis of satisfying the task assignment constraints and the ship
navigation environment constraints. Moreover, the experimental results using real data also indicate that the
proposed method can find the optimal solution rapidly, and thus improve the task allocation efficiency.
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