Research Of Ant Colony Agorithm To Surface Mount Technology Optimization

The process of print circuit board's surface mounting is one of the most important phase in the Surface Mount Technology. The mounting speed will directly influence the productivity. The process about optimization of surface mounting is finished manually at before. Although in recent years, device providers have equipped some method for hardware devices, those methods are simple, unsuitable for the practical production. The characteristics of distributed computing, self-organize, and positive feedback in Ant Colony algorithm are very suitable for path optimization problem in print circuit board surface mounting, and provides an effective and superior solution.This paper extends the thinking of ant colony algorithm to the path optimization of surface mounting problem, gives an optimized model and a new Adaptive Ant Colony Algorithm catering for the problem. This paper transfers this problem to the Vehicle Routing Problem, and on the basis of which builds the math model, makes the ant colony optimization method adaptive to settle the surface mounting path optimization problem and settles the collateral mounting problem.This paper discusses the principle of basic ant colony algorithm, and on the basis of which analyses two kinds of representative ant colony optimization algorithm: Ant Colony System and Ant-Q System. According to a series of simulation tests, this paper makes some useful exploration about the application of ant colony algorithm to the surface mounting path algorithm problem, and gives a new adaptive ant colony algorithm on the basis of above, improves the pheromone updating rule, and settles part parameter adaptive problem.At present, the strict math theory base of ant colony algorithm is not established, relative research in and abroad is still in the phase of experiment and abecedarian application, the parameter announced aims only at the specific question, therefore the mathematical theory analysis of ant colony algorithm and the problem of parameter auto-adapted are the main goals which the future will pursue. Moreover, the balance optimization question of multi-production lines which have the many machines and is the next key question.