When transferring a geometric constraint equation group into an optimization model

we need a method to jump out of the local beat solution so that we can find a best global solution.Considering the speed and global capability

we adopt a composite particle group optimization algorithm.Particle swarm optimization algorithm is a kind of evolution computation technology based on group intelligence.In all evolution computations heuristic function should be included to control its own characteristic.These parameters are usually correlated with a specific problem and are defined by the users.Suitable parameter choice needs user's abundant experience and correct judgment on the information offered by the problem.More important thing is that these heuristic parameters will influence the convergence characteristic of the algorithm.Because of this even experienced users may choose an inappropriate parameter and make the problem unable to reach an effective solution.Some research on these parameters need to be carried on more and more.Here we choose the controlling parameters as an optimization solution to the particle swarm algorithm.Thus we can control the heuristic function in the PSO using the ordinal genetic algorithm and propose the composite particle swarm optimization algorithm.Finally we use this algorithm to solve the geometric constraint successfully.The experiment shows that the algorithm can find the best solution in a short time.