基于SFLA-PSO算法的几何约束求解

Geometric Constraint Solving Based on Shuffled Frog Leaping Algorithm and Particle Swarm Optimization

为提高求解几何约束问题的效率和收敛性,将几何约束问题等价为求解非线性方程组问题。并将约束问题转化为一个优化问题,采用基于混洗蛙跳(SFLA:Shuffled Frog Leaping Algorithm)和粒子群优化(PSO:Particle Swarm Optimization)算法求解该问题。SFLA-PSO算法采用将SFLA和PSO二者相结合的方法,利用PSO算法进行族群局部搜索,利用SFLA的多种群的进化方法进行族群的混选,相互取长补短,以达到收敛速度快和全局搜索的目的。实验表明,该方法可以提高几何约束求解的效率和收敛性。

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.