Hybrid genetic algorithm research and its application in problem optimization

Abstract

There is a lot of research in genetic algorithm about structural optimization. But as far as the large multi-goal program is concerned, it limits the application of genetic algorithm for the reason of its specialty and large calculation. In order to explore a new resolution, the author proposed a combining algorithm for structural optimization, which is based on genetic algorithm and gradient algorithm. Gradient algorithm is used to superpose, and the result got is used to improve the herd of the genetic algorithm. The superior genetic algorithm is compared with the root of the gradient algorithm and the best point is chosen to be the incipient point of the next step of the super position. This method can keep the best root of the course and can also speed up searching, and keep the best global root. Numerical examples show that the combining algorithm possesses both the merit of genetic algorithm on strong global searching ability and gradient algorithm.