1. An improved multi-objective evolutionary optimization algorithm with inverse model for matching sensor ontologies
- Author
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Xingsi Xue, Chao Jiang, Pei-Wei Tsai, Guojun Mao, Hai Zhu, and Haolin Wang
- Subjects
0209 industrial biotechnology ,Matching (statistics) ,Optimization problem ,business.industry ,Computer science ,Pareto principle ,Evolutionary algorithm ,ComputerApplications_COMPUTERSINOTHERSYSTEMS ,Computational intelligence ,02 engineering and technology ,computer.software_genre ,Multi-objective optimization ,Theoretical Computer Science ,020901 industrial engineering & automation ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Local search (optimization) ,Geometry and Topology ,Data mining ,business ,Ontology alignment ,computer ,Software - 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.
- Published
- 2021