典型文献
Improving the Efficiency of Multi-Objective Grasshopper Optimization Algorithm to Enhance Ontology Alignment
文献摘要：
Ontology alignment is an essential and complex task to integrate heterogeneous ontology.The meta-heuristic algorithm has proven to be an effective method for ontology alignment.However,it only applies the inherent advantages of meta-heuristics algorithm and rarely considers the execution efficiency,especially the multi-objective ontology alignment model.The per-formance of such multi-objective optimization models mostly de-pends on the well-distributed and the fast-converged set of solu-tions in real-world applications.In this paper,two multi-objective grasshopper optimization algorithms(MOGOA)are proposed to enhance ontology alignment.One is ε-dominance concept based GOA(EMO-GOA)and the other is fast Non-dominated Sorting based GOA(NS-MOGOA).The performance of the two methods to align the ontology is evaluated by using the benchmark dataset.The results demonstrate that the proposed EMO-GOA and NS-MOGOA improve the quality of ontology alignment and reduce the running time compared with other well-known metaheuristic and the state-of-the-art ontology alignment methods.
文献关键词：
中图分类号：
作者姓名：
LV Zhaoming;PENG Rong
作者机构：
School of Computer Science,Wuhan University,Wuhan 430072,Hubei,China
文献出处：
引用格式：
[1]LV Zhaoming;PENG Rong-.Improving the Efficiency of Multi-Objective Grasshopper Optimization Algorithm to Enhance Ontology Alignment)[J].武汉大学自然科学学报（英文版）,2022(03):240-254
A类：
MOGOA
B类：
Improving,Efficiency,Multi,Objective,Grasshopper,Optimization,Algorithm,Enhance,Ontology,Alignment,alignment,essential,complex,task,integrate,heterogeneous,ontology,has,proven,effective,However,only,applies,inherent,advantages,heuristics,rarely,considers,execution,efficiency,especially,multi,objective,such,optimization,models,mostly,pends,well,distributed,fast,converged,solu,real,world,applications,In,this,paper,two,grasshopper,algorithms,proposed,enhance,One,dominance,concept,EMO,other,Non,dominated,Sorting,NS,performance,methods,evaluated,by,using,benchmark,dataset,results,demonstrate,that,improve,quality,reduce,running,compared,known,metaheuristic,state,art
AB值：
0.522359
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