Improved genetic & ant colony optimization algorithm for regional air defense WTA problem

Facing the complex air defense situation, it is an urgent mission to improve the efficiency of regional air defense weapon-target assignment of warship formation. The weapon-target assignment problem is NP hard. Classical methods for solving such problems are based on graph search and usually result in exponential complexities. Some intelligent algorithms usually result in local optimal. An improved genetic and ant colony optimization algorithm is proposed. The phase of genetic algorithm adopts crowding technique and changeable mutation operator to maintain multiple populations. As a result, the phase of ant colony optimization can avoid getting into local optimization. Furthermore, an intensive study of how to use this algorithm in weapon-target assignment is made. Experiments results demonstrate that the improved algorithm achieves better efficiency than some classical optimization algorithms. The proposed algorithm can solve regional air defense weapon-target assignment problem well.