An Improved Seagull Optimization Algorithm for Multi-Center Maintenance Task Allocation of UAV Swarm Under Resource Constraints

This paper addresses the optimization problem of maintenance and support task allocation and scheduling for unmanned aerial vehicle (UA V) swarms when performing multi - batch continuous sortie missions. Against the backdrop of multiple maintenance centers, multiple maintenance personnel, and multiple equipment spare parts, a maintenance and support task allocation method based on the improved seagull optimization algorithm is studied. Under the condition of limited maintenance resources, this algorithm aims to minimize the batch sortie preparation time, optimize the workload balance of maintenance personnel, and minimize the total maintenance and support time of the UA V swarm. By introducing chaos mapping, non - linear additional variables, greedy insertion, Levy flight, and random multi - point exchange strategies into the seagull optimization algorithm, the optimization of the maintenance and support task allocation scheme is achieved. Through experiments with background case data, compared with traditional algorithms, the improved seagull algorithm proposed in this paper has significantly improved in optimization indicators such as the sortie time of the next task, task balance, and total maintenance and support time.