Research On Ant Ocolony Algorithm And Its Application In Flight Simulator

In twenty-first century, the development of national Air Force represent the level of modernization of the military construction, and the flight simulator provides the complete technical support for Air Force training system, there are a lot of advantages of flight simulator such as saving training fund, protecting pilots'lives and performing special and complex training, and we have carried out a large number of research projects of flight simulator. The flight simulator is composed by platform system, computer system, visualization system, electronic controling system and console system. With the years of experiences, the platform system, computer system and visualization system have become increasingly stable. The group operation in complex situation is a very urgent problem that needs to be solved due to the requirement of higher combat abilities of flight simulator. A increasing number of integrated circuits also increase the difficulty of maintain flight simulator, and fault diagnosis of large-scale integrated circuits has became an international issue.Ant colony algorithm originated from the observation and simulation of the ants'behaviors. Compared with the traditional optimization methods, AC is widely applied in scientific research and industrial production in virtue of their higher adaptability, robustness and parallel processing capability. This paper ccommence the study in multi-target data association, digital circuit test set generation and test set optimization with AC combined with genetic algorithm and simulated annealing algorithm. The result of experiments has shown that AC-GADA, AC-GATSG and AC-SATSO can effectively improve the convergence speed of the ant colony algorithm, and overcome the emergence of local extremum. The main contents of this thesis are as follows:For multi-sensor multi-target data association problem, this paper presents an AC-GADA algorithm which combines ant colony algorithm with genetic algorithm. This algorithm designed difference pheromone for each ant and improves global pheromone increment model, and combined crossover and mutation strategy with fitness of population model in order to improve rate of convergence and avoid the appearance of local extremum. In AC-GADA, the pheromone was desicided not only by the number of ants which have chosen the path but also the pheromone code of each ant. The comparison with ACDA(Ant Colony Data Association) and JPAD(Joint Pobabilistic Data Association) proved its efficiency and superiority.For large scale integrated circuit fault diagnosis problem, this paper presents AC-GATSG algorithm. The algorithm converts scale integrated circuit into a graph, and conducts the study of automatic generation by using the advantage AC algorithm. In AC-GATSG, the individual stained will be reconstructed when optimal solution occurs in each generation, and the pheromone will be updated at the same time. Mutation probability of individual mutations and threshold which limits generated during crossover are useful elements in AC-GATSG which reduce the execution of generating local minimum one by one. We select combinational circuits and typical flight simulator circuit units as experiment objects, and the results show that AC-GATSG algorithm can satisfy the requirement of practical production, and has a widely application prospect.The test set of large scale intefrated circuit, which is generated automatically, has a large number of redundant vectors that cause high waste of resources and low efficiency of production and development in standardized tests. This paper presents AC-SATSO to optimize the sets of the circuit tests. AC-SATSO builds the model of test vector nodes and fault nodes hierarchically, and identify these nodes with in-out degree. AC-SATSO algorithm combines AC with simulated annealing algorithm to accelerate the convergence rate and determine whether to accept the new population with Boltzmann mechanism. Under this optimization strategy, AC-SATSO can make a great convergence rate although lacking of pheromone at the beginning, and avoid the appearance of local minimum in subsequent stage. Besides, this paper puts forward the concept of optimum complete test set which complete the test with the lowest consumption. The result of experiment demonstrated that AC-SATSO method of solution quality and stability of the algorithm are better than literature. In recent years, researches on ant colony algorithm, as well as its applications, have been paid great attentions by many researchers globally. Based on ant colony algorithm, genetic algorithm and simulated annealing algorithm, this paper focuses on the research of flight simulator, and proposes several optimization methods. There are both theoretical and practical significance of this research on the improvement of ant colony algorithm.