Ternary compound ontology matching for cognitive green computing

作     者：Zheng, Wei-Min Chai, Qing-Wei Zhang, Jie Xue, Xingsi 

作者机构：Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Yulin Normal Univ Sch Comp Sci & Engn Yulin 537000 Peoples R China Fujian Univ Technol Sch Comp Sci & Math Fuzhou 350118 Peoples R China 

出 版 物：《MATHEMATICAL BIOSCIENCES AND ENGINEERING》 (Math. Biosci. Eng.)

年 卷 期：2021年第18卷第4期

页      面：4860-4870页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：National Key Research and Development Program of China Natural Science Foundation of Fujian Province [2020J01875] 

主　　题：cognitive green computing ternary compound ontology matching compact evolutionary algorithm 

摘      要：Cognitive green computing (CGC) dedicates to study the designing, manufacturing, using and disposing of computers, servers and associated subsystems with minimal environmental damage. These solutions should provide efficient mechanisms for maximizing the efficiency of use of computing resources. Evolutionary algorithm (EA) is a well-known global search algorithm, which has been successfully used to solve various complex optimization problems. However, a run of population-based EA often requires huge memory consumption, which limited their applications in the memory-limited hardware. To overcome this drawback, in this work, we propose a compact EA (CEA) for the sake of CGC, whose compact encoding and evolving mechanism is able to significantly reduce the memory consumption. After that, we use it to address the ternary compound ontology matching problem. Six testing cases that consist of nine ontologies are used to test CEA s performance, and the experimental results show its effectiveness.