Research on Weapon-Target Allocation Based on Genetic Algorithm

Weapon-target allocation (WTA) is a typical constrained combinatorial optimization problem, which is an important content of command and decision in air defense operation. WTA is known to be NP-complete problem, and the intelligent optimization methods are widely employed to solving it. A popular coding length is n*m corresponding to assigning n weapons to m targets. However, the coding length will increase greatly with the problem scale growing, and the computation is too heavy to meet the real-time requirements. This paper focuses on designing a new gene coding to improve computational efficiency. In our study, a sequence of weapons serves as gene coding, which is attached the two other codes, target code and capacity code respectively. This coding length is n and adapts to the constraints of WTA effectively. Then the operators of gene selection, crossover and mutation are designed. On the other hand, the maximum operational effectiveness is defined as the object function with the minimum consumption of ammunition. This model is based on multi-objective optimization, and is more realistic. An example shows that the method is feasible and can save computing time greatly.