自适应全局最优和声搜索的模型相似性计算  

Model Similarity Calculation Based on Adaptive Global Optimal Harmony Search

作　　者：高雪瑶[1] 董欣然 张春祥 GAO Xue-yao;DONG Xin-ran;ZHANG Chun-xiang(School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China;School of Software and Microelectronics, Harbin University of Science and Technology, Harbin, 150080, China)

机构地区：[1]哈尔滨理工大学计算机科学与技术学院,哈尔滨150080 [2]哈尔滨理工大学软件与微电子学院,哈尔滨150080

出　　处：《哈尔滨理工大学学报》2020年第6期150-156,共7页Journal of Harbin University of Science and Technology

基　　金：国家自然科学基金(61502124,60903082);中国博士后科学基金(2014M560249);黑龙江省普通高校基本科研业务费专项资金(LGYC2018JC014);黑龙江省自然科学基金(F2015041,F201420).

摘　　要：为了度量模型之间的差异,提出了一种基于自适应全局最优和声搜索算法(self-adaptive global best harmony search algorithm,SGHS)的三维模型相似性计算方法。根据面的组成边数和面的邻接关系来构造2个模型之间的面相似度矩阵。从面相似度矩阵中,利用自适应全局最优和声搜索算法获得2个模型之间的最优面匹配序列。根据最优面匹配序列,累积源模型面与目标模型面之间的相似度来计算模型之间的相似性。实验结果表明:所提出方法更能准确地度量模型之间的差异。In order to measure the difference of models,a method of computing three dimension model’s similarity based on self-adaptive global best harmony search algorithm is proposed.Two models’face similarity matrix is constructed according to the number of face’s edges and adjacency relationship of faces.Self-adaptive global best harmony search algorithm is used to find an optimal face matching sequence between source model and target one from face similarity matrix.Based on an optimal face matching sequence,similarities between source model faces and target ones are summed to compute two models’similarity.Experimental results show that the proposed method can measure two models’difference more accurately.

关 键 词：和声搜索 面相似度矩阵 面匹配 粒子群 

分 类 号：TP391.7[自动化与计算机技术—计算机应用技术]