Optimization Genetic Algorithm for geometric constraint solving

The geometric constraint solving can transform into the numerical optimization solving. In the paper introduce a hybrid approach that simultaneously applies Genetic Algorithm (GA), and tabu search (TS) to create a generally well-performing search heuristics, and combat the problem of premature convergence. This algorithm uses GA to search the area where the best solution may exist in the whole space, and then. When the algorithm approaches to the best solution and the search speed is too slow, we can change to the effective local search strategy—TS 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.