With applying the information technology to the military field,the advantages and importance of the networked combat are more and more obvious.In order to make full use of limited battlefield resources and maximally destroy enemy targets from arbitrary angle in a limited time,the research on firepower nodes dynamic deployment becomes a key problem of command and control.Considering a variety of tactical indexes and actual constraints in air defense,a mathematical model is formulated to minimize the enemy target penetration probability.Based on characteristics of the mathematical model and demands of the deployment problems,an assistance-based algorithm is put forward which combines the artificial potential field (APF) method with a memetic algorithm.The APF method is employed to solve the constraint handling problem and generate feasible solutions.The constrained optimization problem transforms into an optimization problem of APF parameters adjustment,and the dimension of the problem is reduced greatly.The dynamic deployment is accomplished by generation and refinement of feasible solutions.The simulation results show that the proposed algorithm is effective and feasible in dynamic situation. With applying the information technology to the military field, the advantages and importance of the networked combat are more and more pronounce.In order to make full use of limited battlefield resources and maximally destroy enemy targets from arbitrary angle in a limited time, the research on firepower nodes dynamic deployment becomes a key problem of command and control. Considering a variety of tactical indexes and actual constraints in air defense, a mathematical model is formulated to minimize the enemy target penetration probability. Based on characteristics of the mathematical model and demands of the deployment problems, an assistance-based algorithm is put forward which combines the artificial potential field (APF) method with a memetic algorithm. The APF method is employed to solve the constraint handling problem and generate feasible solutions. constrained optimization problem transforms into an optimization problem of APF parameters adjustment, and the dimension of the problem i s reduced greatly. the dynamic deployment is accomplished by generation and refinement of feasible solutions. The simulation results show that the proposed algorithm is effective and feasible in dynamic situation.