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Modeling And Algorithm For Joint Target Assignment Problem In Fire Strike

Posted on:2019-11-08Degree:MasterType:Thesis
Country:ChinaCandidate:Y XiangFull Text:PDF
GTID:2416330572951618Subject:Engineering
Abstract/Summary:PDF Full Text Request
In modern warfare,a combination of multidimensional combat space and the participation of multiple combat forces has become the norm.How to rationally organize combat forces,arrange various weapons,and allocate different combat targets in systematic operations,so as to achieve the minimum battle damage,and finally complete the tactical purpose has always been the concern of all countries in the world.Designing a set of scientific methods and strategies for pre-war planning and assisting commanders in making rational decisions are of practical and theoretical significance.This thesis makes a study of the distribution of the target in the joint strike weapons,the main problem and innovations are as follows:Aiming at the issue of how to rationally allocate weapons targets when equipment is limited,the thesis aims to maximize the profit per unit attack cost.This thesis designs a global optimization model with the goal of maximizing unit damage cost.A Two-population coevolutionary genetic algorithm was used to solve the model.Two crossover operators based on steepest descent and goal-driven were designed to improve the population diversity.The mutation operator of neighborhood feasible stochastic search and the single-target roulette game and the selection operator to divide the shortest distance uniformly are proposed to ensure the search ability of the algorithm.Finally,the convergence of the proposed algorithm is proved.Aiming at how to rationally allocate atoms targets when equipment is limited,the profit of fire strikes is maximized and the cost is minimized.This thesis designs a multi-objective optimization model aiming at maximizing damage effects and minimizing strike costs.Based on the hybrid memetic algorithm framework,a dual-population hybrid genetic algorithm based on MOEA/d is adopted.In the algorithm,the crossover operator and random mutation operator of neighborhood approximation and circular diffusion,and the discretization steepest descent local search operator with good Pareto front are designed to effectively accelerate the convergence of the algorithm.The multi-target roulette and the selection operator for evenly dividing the shortest distance are improved,which improves the diversity and uniformity of the next generation.In this thesis,three sets of simulation experiments are designed for the combined firepower combat global optimization model and the multi-objective optimization model and the corresponding solution algorithms,from the number of fixed equipment categories,the number of fixed potential target strikes,and the average number of fixed each category of weaponry.Compared with the DGA and BAN algorithms,the proposed global optimization model and the Two-population co-evolutionary genetic algorithm can not only determine the targets to be hit,but also allocate limited weapons and equipment to different targets,so as to maximize the damage cost per unit cost.The proposed multi-objective optimization model and the Two-population hybrid genetic algorithm based on MOEA/D can provide more assistant decision-making options for commanders.It has better convergence,wideness and uniformity.It can get a better firepower combat weapon target distribution scheme than the DGA and BAN algorithm.
Keywords/Search Tags:Joint fire strike, Limited equipment, Global constrained optimization, Multi-objective optimization problem, Genetic algorithm
PDF Full Text Request
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