Multi-UCAVs targets assignment using opposition-based genetic algorithm

Abstract

The article presents a novel targets assignment method for multiple UCAVs. In this work, minimization total attack time is chosen as the objective of the targets assignment problem, and the attack benefit of each target is affected by the target value. To solve this challenging problem, the tailored genetic algorithm (GA) incorporated with the opposition-based learning technique is proposed, denoted as OGA. By introducing the opposition-based learning technique into the evolutionary process, the global search capability is enhanced and the convergence and optimality of the algorithm could be improved. Finally, OGA is compared with ordinary GA on several multi-UCAVs targets assignment simulations. The comparison results show that the proposed method is more efficient and stronger in escaping from the local optimum in solving the multi-UCAVs targets assignment.