基于遗传算法的散乱点云最小包围盒求解

提出一种将遗传算法和O’Rourke算法相融合的最小包围盒求解算法,以O’Rourke算法中的体积函数作为遗传算法的目标函数,采用遗传算子指导解的搜索方向,通过新种群的迭代生成过程缩小搜索区域与体积误差,种群迭代结束后对最优个体解码获得最小包围盒.实验结果表明,该算法可在满足最小包围盒体积精度的同时显著提高算法的运行效率,能够有效处理各种复杂散乱点云数据的最小包围盒快速求解问题.

Solution of minimum bounding box of scattered points based on genetic algorithm

An algorithm of minimum bounding box combining genetic algorithm with O'Rourke's algorithm was proposed, which regarded the volume function in O'Rourke's algorithm as the objective function and used the evolutionary factors to guide the searching directions. Through the process of the population's iterative generation, this algorithm narrowed the search area and the volume error. When the iterative process was over, the minimum bounding box was obtained by decoding the optimal individuals. The experimental results show that the algorithm can improve algorithmic efficiency and satisfy the volume accuracy simultaneously. The algorithm can deal with sorts of problems related to minimum bounding box fast solving of complex scattered point cloud.