关键词: 几何约束求解, 混洗蛙跳算法, 粒子群优化算法

Abstract:

In order to improve the efficiency and   convergence property,geometric constraint solving problem is the current hot issues in the constraint-based design research.Geometric constraint problem is equivalent to the problem of solving a set of nonlinear equations substantially.The constraint problem can be transformed to an optimization  problem.We can solve the problem with SFLAPSO (Shuffled Frog Leaping Algorithm-Particle Swarm Optimization).SFLA-PSO algorithm uses the combination of the two algorithms.It can search in local communities by PSO and use the population of more evolutionary approach to racial mix by SFLA.It can get mutual complementarities and  attain convergence fast and the global search.The experiment shows that it can improve the geometric constraint solving efficiency and possess better convergence property than the compared algorithms.

Key words: geometric constraint solving, shuffled frog leaping algorithm (SFLA), particle swarm optimization(PSO)