Majorization of Artillery Fire Distribution Based on Quantum Genetic Algorithm

Artillery fire Distribution is a typical NP-hard problem, it will fall into the plight of local optimum when we use traditional methods to solve the problem. The idea of qubit and quantum gate are introduced to QGA(quantum genetic Algorithm ), which combine quantum computing with genetic algorithms and it possesses those characters such as higher velocity of convergence and better optimization seeking compared with traditional evolution algorithm. This thesis solve the problem of artillery fire distribution by QGA and It has been proved this method is more effective than traditional GA(genetic algorithm) in solving optimization of Artillery fire distribution by simulation experiment.