Abstract: Geometric constraint solving problem is the current hot issue of constraint solving techniques. The ultimate goal of geometric constraint solving is to determine the specific geometry of each coordinate position. The constraint equations of geometric constraint problem can be transformed into single target optimization model, so the constraint problem can be transformed into optimization problem. This paper uses an improved artificial bee colony algorithm (IABC) to solve geometric constraint problem. In the search phase, the IABC is through multidimensional searching towards optimal value instead of random one-dimensional searching of ABC, thereby enhancing the ability of local optimization, accelerating the convergence rate, eventually to find the global optimum. The experiments show that the improved artificial bee colony algorithm for solving geometric constraint problems has the advantages of high efficiency, fast convergence, high precision.

Key words: geometric constraint solving, artificial bee colony algorithm, particle swarm optimization, optimization algorithm

摘要： 几何约束求解问题是当前约束求解技术研究中的热点问题，几何约束问题求解的目的在于确定几何实体间的位置关系。几何约束问题的约束方程组可转化为单目标优化模型，因此几何约束求解问题可以转化为优化问题进行求解。采用改进的人工蜂群算法来求解几何约束问题，改进算法在搜索阶段向最优值进行多维搜索来代替原算法的随机一维搜索，从而增强了算法的局部寻优能力，加快了收敛速度，最终找到全局最优值。实验表明，基于改进人工蜂群算法求解几何约束问题具有效率高，收敛快，求解精度高的优点。

关键词: 几何约束求解, 人工蜂群算法, 粒子群优化, 优化算法