A GA-ACO hybrid algorithm for the multi-UAV mission planning problem

Multi-UAV mission planning is a combinational optimization problem, that aims at planning a set of paths for UAVs to visit targets in order to collect the maximum surveillance benefits, while satisfying some constraints. In this paper, a genetic algorithm and ant colony optimization hybrid algorithm is proposed to solve the multi-UAV mission planning. The basic idea of the proposed hybrid algorithm is replacing the bad individuals of the GA´s population by new individuals constructed by ant colony algorithm. Also, an efficient recombination operator called path relinking is used for mating. A population partition strategy is adopted for improving the evolving efficiency. Experimental results suggested that the proposed hybrid algorithm can solve the test instances effectively in a reasonable time. The comparison study with several existing algorithms shows that the proposed algorithm is competitive and promising.