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In order to solve the cooperative search problem of multiple unmanned aerial vehicles (multi-UAVs) in a large-scale area, we propose a genetic algorithm (GA) incorporating simulated annealing (SA) for solving the task region allocation problem among multi-UAVs on the premise that the large area is divided into several small areas. Firstly, we describe the problem to be solved, and regard the task areas allocation problem of multi-UAVs as a multiple traveling salesman problem (MTSP). And the objective function is established under the premise that the number of task areas to be searched by each UAV is balanced. Then, we improve the GA, using the advantages of the SA can jump out of the local optimal solution to optimize the new population of offspring generated by GA. Finally, the validity of the algorithm is verified by using the TSPLIB database, and the set MTSP problem is solved. Through a series of comparative experiments, the validity of GAISA and the superiority of solving the MTSP problem can be demonstrated.

Research on improved genetic simulated annealing algorithm for multi-UAV cooperative task allocation