Objective Aiming at the challenges in matching the requirements and functional models caused by the dynamic nature of the requirement model for unmanned maritime swarm collaborative operations, an improved genetic algorithm (IGA) is proposed. This algorithm combines a hierarchical selection strategy based on elite retention with a crossover and mutation strategy based on adaptive probabilities to realize dynamic matching between the collaborative requirements and functional models.

Methods The method uses dynamic requirement data and multiple unmanned equipment logic models as inputs for dynamic matching between collaborative operation requirements and functional models of unmanned maritime swarms. Gene coding is performed based on the characteristics of unmanned maritime swarm collaborative operations. The hierarchical selection strategy based on elite retention and the crossover and mutation strategies based on adaptive probabilities are used to balance the global and local search performance. Afterwards, the optimal matching solution between the requirement and functional model could be dynamically generated.

Results The results show that under the same algorithm parameters, the fitness of the proposed method is averagely 7.8% and 7% higher, and the running time is averagely 5 and 21 times faster, than those of the bee algorithm and the artificial bee colony algorithm respectively.

Conclusion The proposed theory and method could be applied to optimizing maritime equipment design and operations, which could improve the intelligence of maritime equipment.