Paper Info
Title
An Improved Multi-Objective Evolutionary Optimization Algorithm With Inverse Model For Matching Sensor Ontologies
Abstract
To address the heterogeneity problem of sensor data, it is necessary to conduct the Sensor Ontology Matching (SOM) process to find the mappings among diverse sensor data with the same semantics connotation. Currently, many Multi-Objective Evolutionary Algorithms (MOEAs) have been used to match the ontologies, which aim at finding a set of solutions called Pareto Set (PS) in the Pareto Front (PF) to represent a set of trade-off proposals for different Decision Makers (DMs). Being inspired by the success of MOEA with Inverse Model (IM-MOEA) in solving complicated optimization problems, in this work, an Improved IM-MOEA (I-IM-MOEA)-based matching technique is further proposed to enhance the algorithm's matching efficiency as well as the alignment's quality. To overcome the drawback of IM-MOEA that has poor performance on irregular PF, an adjusted selection mechanism is employed to avert the massive reduction in non-domination solutions on irregular PF, a dynamic Reference Vectors (RVs) is used to decrease the computational resources and boost the efficiency of the algorithm, and a local search strategy is introduced to promote the results' quality. The experiment employs the benchmark provided by Ontology Alignment Evaluation Initiative (OAEI) and three sensor ontologies to assess the performance of I-IM-MOEA, and the experimental results show that I-IM-MOEA is both effective and efficient.
Year
DOI
Venue
2021
10.1007/s00500-021-05895-y
SOFT COMPUTING
Keywords
DocType
Volume
Sensor ontology matching, Multi-objective evolutionary algorithm, Inverse modeling
Journal
25
Issue
ISSN
Citations 
18
1432-7643
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Xingsi Xue11816.08
Chao Jiang200.34
Haolin Wang300.34
Pei-Wei Tsai400.34
Guojun Mao501.35
Hai Zhu68722.69