Research On Equipment Application And Configuration Model Based On Swarm Intelligence

In modern war,how to rationally organize combat forces,use weapons and equipment to attack targets,use the lowest cost to obtain the maximum damage to the impact effect,and achieve tactical objectives has always been a very important problem.In this thesis,the existing gray wolf optimization algorithm has some problems,such as slow convergence speed,easy to fall into local optimal and not suitable for discrete target optimization.The gray wolf optimization algorithm has been improved,and applied to the research of aiming point selection when attacking a single target and joint ammunition fire distribution when attacking multiple targets:(1)Aiming at the problem of how to choose the optimal aiming point to maximize the damage degree of the target with the given ammunition quantity,this thesis proposes an optimization model to obtain the optimal aiming point by using the improved grey wolf optimization algorithm.Firstly,the initial quality of the grey wolf population was improved by using quasi-reflection,and the convergence factor of the grey wolf optimization algorithm was improved,so as to improve the population diversity,global and local search ability of the grey wolf optimization algorithm.Then,gaussian variation is used to perturb the optimal solution to prevent the algorithm from falling into local optimum.Finally,the improved algorithm is applied to the evaluation function of the damage evaluation model to obtain the optimal aiming point.Simulation experimental results show that the improved grey wolf optimization algorithm has better searching ability and convergence speed than the standard grey wolf optimization algorithm.(2)Aiming at the optimization problem of firepower allocation when joint ammunition strikes targets,this thesis proposes a joint ammunition firepower allocation model based on discrete QGGTA.The main purpose of this model is to reasonably match ammunition and target on the premise of meeting specific damage threshold,so as to minimize the attack cost and maximize the attack benefit.Firstly,the population of grey wolf optimization algorithm was re-coded,and the population was initialized under the condition of satisfying the constraint conditions.Then,genetic algorithm was used to update part of the population to improve the diversity of the population,and the improved grey wolf optimization algorithm was used to update the whole population to make it suitable for integer coding rules and constraints.Finally,the tabu search algorithm is used to generate the optimal firepower allocation scheme and improve the local search ability.The simulation experimental results show that the proposed algorithm has better searching ability and convergence speed than PSO and grey wolf optimization algorithm.In this thesis,the above two research models are applied to the design and development of a damage evaluation system.The combined ammunition firepower distribution model is used to obtain the optimal firepower distribution scheme,and the aiming point optimization model is used to obtain the optimal aiming point,damage prediction and ammunition consumption calculation,so as to obtain the best damage effect.