An Optimization Method Of Intelligent Deployment Of Air Defense Based On Improved Genetic Algorithm

The intelligent deployment of air defense forces is an important prerequisite for modern cooperative air defense operations.Its general process is to design a reasonable deployment space grid based on the operational scenarios and mission requirements,and then calculate a reasonable deployment scheme of air defense forces based on the optimization algorithm.The deployment of air defense forces based on heuristic algorithms such as genetic algorithm has some defects such as premature convergence,easy to fall into local optimization,low efficiency and slow speed under the condition of large population and iteration time with complex objective function.Facing the needs of multiple weapons to jointly attack multiple air attack targets of the enemy,this paper carries out mathematical modeling and analysis based on the scenario of single important place precise air defense force deployment optimization problem,and combines the improved genetic algorithm based on agent model to study the optimization method of efficient air defense force deployment,mainly including the following aspects:(1)Explain and describe the deployment tasks of air defense forces,and analyze the air defense operational scenarios and related elements on this basis.Based on the gridding of the deployment area and the discretization of the deployment location,the defense area is divided into a circular grid,and the grid intersection is used as the deployment point to be selected.This paper discusses the constraints and interception depth requirements in the process of operation.At the same time,considering the parameters such as weapon performance,geographical conditions and interception contribution rate of key directions,a comprehensive optimization objective function is designed.(2)An improved genetic algorithm based on agent model is proposed to solve the problem of fast convergence and low efficiency when traditional genetic algorithm is applied to large population and iteration condition with complex optimization objectives and constraints.In the process of the algorithm,the agent model evaluation is replaced by the lengthy and accurate calculation process of fitness,which realizes the intelligent and efficient improvement of the classical genetic algorithm,and effectively improves the running efficiency of the genetic evolutionary algorithm in the force deployment problem.(3)Aiming at the optimization of single position precision defense force deployment,the improved genetic algorithm is used to simulate the simulation results.Sufficient sample data are randomly generated based on the constraints,with the agent model trained offline.For the battlefield communication requirements,a communication strategy for the current deployment space is designed.The visualization scheme of air defense force deployment optimization software is designed,and the rationality and effectiveness of the test algorithm and its visualization are verified by simulation examples.