Optimizing Weapon Target Assignment in Air to Ground Strike Based on Adaptive Immune Genetic Algorithm

Abstract

Striking hostile ground targets for air force is a crucial way to achieve air superiority in modern warfare. For the maximum efficiency of limited munitions, the optimal weapon target assignment (WTA) scheme should be developed. Aimed at the fact that lots of approaches have been presented for the WTA problem of stationary targets, while less for the ones of moving targets, this paper proposes an adaptive immune genetic algorithm (AIGA) by combining the acceleration and restrain mechanism of immunity system with the adaptive adjustment strategy of crossover operations and mutation operations. Besides, a swap operation is adopted to raise the diversity of individuals and improve the ability of AIGA in search of the goal optimal solution. The simulation result proves the effectiveness and feasibility of the proposed AIGA, and through comparison analysis, advantages of the algorithm over the adaptive genetic algorithm, the immune genetic algorithm and the standard genetic algorithm are revealed.