Research Of Cooperative Air Combat Decision-making Based On ACA

Cooperative multi-target attack air combat is the main form of future air combat and development trends. Yet the key problem is the air combat decision-making. It is best to ensure that an important means to combat the effect and effective measures. Air combat decision-making is a large-scale nonlinear programming problem with a large number of variables and constraint conditions. With the expansion of air combat, air combat decision-making is very complex, so it is hard to solve with conventional methods.According to the characteristics of air combat decision-making problem, Ant Colony Algorithm (ACA) has been applied to solve the question after analyzing the mathematical model of air combat decision-making. ACA is a kind of heuristic algorithm based on swarm intelligence. It is a general framework algorithm, which has robustness and is parallel distributed computing. Aiming at the shortcoming of ACA algorithm, that is, slow convergence performance and easily plunging into the local minimum, an advanced ACA algorithm is invented. It is improved in the path selection mechanism, pheromone update mechanism and reconnaissance elements and set the initial pheromone based on Polymorphic ant colony algorithm combining Ant-Q algorithm. When applied to TSP problem, it proves superiority in global convergences and convergence precision compared to standard ACA.At last, the advanced ACA is applied to the air combat decision-making problem. The simulation experiments showed that the advanced ACA overcomes the local convergence limitation of ACA, has rapid convergence speed and global convergence capability, which is very feasible and effective in air combat decision-making.