An improved multi-objective evolutionary optimization algorithm with inverse model for matching sensor ontologies
An improved multi-objective evolutionary optimization algorithm with inverse model for matching sensor ontologies
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DOI：
10.1007/s00500-021-05895-y
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发表时间：
2021-05
期刊：
影响因子：
4.1
通讯作者：
Xingsi Xue;Chao Jiang;Haolin Wang;Pei-wei Tsai;Guojun Mao;Hai Zhu
中科院分区：
文献类型：
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作者：
Xingsi Xue;Chao Jiang;Haolin Wang;Pei-wei Tsai;Guojun Mao;Hai Zhu
为了解决传感器数据的异构性问题，需要进行传感器本体匹配（SOM）过程，以找到具有相同语义内涵的不同传感器数据之间的映射。目前，许多多目标进化算法（MOEA）已被用来匹配本体，其目的是在帕累托前沿（PF）中找到一组称为帕累托集（PS）的解决方案，以代表不同决策者（DM）的一组权衡建议。受到逆模型MOEA（IM-MOEA）在解决复杂优化问题方面成功的启发，在这项工作中，进一步提出了一种基于改进的IM-MOEA（I-IM-MOEA）的匹配技术，以提高算法的匹配效率和对齐质量。为了克服IM-MOEA在不规则PF上性能较差的缺点，采用调整选择机制来避免不规则PF上非支配解的大量减少，使用动态参考向量（RV）来减少计算资源并提高算法效率，并引入局部搜索策略来提高结果质量。实验采用本体对齐评估计划（OAEI）提供的基准和三个传感器本体来评估I-IM-MOEA的性能，实验结果表明I-IM-MOEA既有效又高效。
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.