The geometric constraint solving based on memory particle swarm algorithm

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. PSO is an evolution computing method. It searches the solution space by creating a better next swarm. The new swarm is produced based on the new individuals. Memory Particle Swarm Algorithm is a PSO algorithm that adds a memory influence. We introduce MPSO into geometric constraint solving. The purpose of the added memory feature is to maintain spread and therefore diversity by providing individual specific alternate target points to be used at times instead of the current local best position. The experiment indicates that the algorithm is effective.