Combining Genetic Algorithm with Sim lex Method for Geometric Constraint Solving
The geometric constraint solving can transform into the numerical optimization solving. A new hybrid algorithm is proposed which combines the merits of global search of the Genetic Algorithm and the good property of local search of the simplex algorithm approach. This algorithm uses Genetic Algorithm 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 simplex 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.
Genetic Algorithm geometric constraint solving simplex algorithm
Hua Yuan Xin Chang
School of Computer Science & Engineering Changchun University of Technology Changchun, China School of Computer Science & Engineering, Changchun University of Technology Changchun, China
国际会议
长春
英文
453-455
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）