THE GEOMETRIC CONSTRAINT SOLVING BASED ON HYBRID GENETIC ALGORITHM OF CONJUGATE GRADIENT

Abstract

We transform the geometric constraint solving into the numerical optimization solving. A new hybrid algorithm is proposed which combines the merits of global search of the genetic algorithm (GA) and the good property of local search of the conjugate gradient approach. This algorithm uses GA to search the area where the best solution may exist in the whole space, and then performs fine searching. When the algorithm approaches to the best solution and the search speed is too slow, we can change to the effective local search strategy—the conjugate gradient algorithm in order to enhance the ability of the GA on fine searching. It makes the algorithm get rid off the prematurity convergence situation. We apply this algorithm into the geometric constraint solving. The experiment shows that the hybrid algorithm has the effective convergence property and it can find the global best solution.