Dissertation > Industrial Technology > Arms industry > Rockets, missiles > Missile > General issues > Basic theory

Effectiveness Evaluation on the Jointed Combat of the Multiple Missiles and Research on Combinatorial Optimization Algorithm

Author FuYingFeng
Tutor ZengQingShuang
School Harbin Institute of Technology
Course Control Science and Engineering
Keywords Penetration performance Ant Colony Algorithm Stochastic Method Pheromone Declining
CLC TJ760.1
Type Master's thesis
Year 2008
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With the development of the technology, battle command and decision system is the neural system of the wars, which become the key of victory or defeat. Intelligent optimization display a good prospect, and become the core technology. This paper is devoted to the research of multi-missile cooperating intelligent optimization, and emphasizes the research of ant colony algorithm applying to the multi-missile cooperating intelligent optimization problem.Firstly, based on the theory of queuing network, this paper presents a mathematical model on the multiple missile joined combat in one turn attack to assess the penetrating performance. The model could discuss the random factors from probability and statistical average.Secondly, aiming at non-differential, constrained, nonlinear characteristics of the multi-missile cooperating intelligent optimization problem, ant colony algorithm is adopted to tackle this problem. According to the high effectiveness of the problem requirements, the further development of the algorithm is probed into. Main objective is to quicken the convergence rate and shorten the computation time of the algorithm. 1. Improvement of the selective strategy, stochastic method is adopted to increase the variety of solutions. 2. Improvement of the pheromone renewal, pheromone declining is adopted.Finally, this paper realizes steps of using ant colony algorithm to tackle the multi-missile cooperating intelligent optimization problem in detail. By the simulation experiments, the traditional ant colony algorithm and the improved ant colony algorithm are compared, and the validity of the improved algorithm is showed. Simulating results shows the improved algorithm has the better performance: higher precision, faster convergence, shorter computation time. Ant colony algorithm is an excellent choice to solute the multi-missile cooperating intelligent optimization.

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