Selecting solutions problem in geometric constraint solving

To solve the constraint multi-solution problem, the constraints are separated to two sets, the original constraint set and the additional constraint set. First, the solver finds out multiple solutions. Then genetic algorithm and ant algorithm are combined in the process of searching optimal solution. We adopt genetic algorithm in the former process to produce the initiatory distribution of information elements, and then ant algorithm in the latter process. The random colony is adopted in genetic algorithm, which can not only accelerate the convergence process of ant algorithm but also avoid the local best solution. The heuristic searching algorithm maximizes the fitness of the additional constraint set, thereby reaches the final result that can satisfy the user´s expectation.