Research Of Swarm Intelligence To Surface Mount Technology Optimization

In the process of print circuit board (PCB) production, the surface mount is an extremely important step, it's speed directly affects the production efficiency. At first it is completed by experience. In recent years, although the suppliers also provide certain methods, but it is not usually perfect. In view of this situation, this article took the Swarm Intelligence as the main source, and proposed a new optimized method. Established the corresponding mathematical model, and then made the improvement to the Swarm Intelligence, and will apply to the surface mount technology route optimized question.Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) agents interacting locally with their environment cause coherent functional global patterns to emerge. SI provides a basis with which it is possible to explore collective (or distributed) problem solving without centralized control or the provision of a global model. Some of the swarm intelligent algorithms, such as ant colony system and particle swarm optimization, also fall into the category of evolutionary computation, so that they are similar to evolutionary algorithms in principle and mechanism, and also the flaw: contradiction between the speed by which they converge and the quality of solution to which they converge. On the other hand, lots of ideas of improving derive from the area of evolutionary computation in the process of their development.This article mainly aims at the surface mount technology question and carries on to the ant algorithm and particle swarm optimization algorithm analyzes, and proposes the improvement strategy, causes it to be suitable to the surface mount. After a series of data test, obtained a good result.Because the research of Swarm Intelligence is not consummated, therefore algorithm parameter at present still did not have the theoretical basis, and aims at the specific question. Parameter auto-adapted, is one goal which the future will pursue. Moreover, the balance optimization question of multi- production lines which have themany machines is the next key question.