Research And Application Of Particle Swarm Optimization In Ship-borne Weapon Target Assignment

With the development of information technology,command and control have become the key consideration when studying the battlefield command and decision automation in various countries at present and in the future.One of the most crucial issues in the study of command and control decision-making is the study of Weapon Target Assignment(WTA),which is how to make weapon target assignment decisions quickly and effectively to achieve the set combat effectiveness.WTA is a typical optimization problem.Based on the existing research results,this paper studies the problem of ship-borne weapon target assignment based on particle swarm optimization in detail.In this paper,the current research status of particle swarm optimization and the weapon target assignment problem model and solution algorithm are described in detail.The concepts and principles of multi-objective optimization,multi-objective optimization,particle swarm optimization,and multi-objective particle swarm optimization are also discussed to provide some theoretical basis for the later research.Aiming at the shortcomings of the standard particle swarm algorithm,some improvements are made to it.Firstly,the standard particle swarm optimization algorithm is improved,and the simulation results obtained by using the benchmark functions show that the improved PSO achieves better effect than the standard particle swarm algorithm and genetic algorithm in terms of accuracy and convergence times.Then the multi-objective particle swarm optimization algorithm for solving multi-objective optimization problems is improved.Compared with the MOPSO and fast non-dominated sorting genetic algorithm(NSGA-II)by the ZDT functions,the simulation results show that the improved MOPSO has achieved better results.The improved particle swarm optimization algorithm is applied to solve the ship-borne weapon target assignment single objective optimization model with the lowest probability of striking failure as the objective function.The simulated battlefield simulation results show that the improved PSO has smaller failure probability than the standard PSO and GA.On this basis,consider that if the number of weapons consumed is reduced as much as possible in the case of better combat effectiveness,a ship-borned weapon target assignment multi-objective optimization model with the lowest probability of striking failure and the least number of weapons used is established.The improved multi-objective particle swarm algorithm was used to solve the problem.Compared with MOPSO and NSGA-II,both the combat effectiveness and weapon consumption were significantly improved.