Over the past decades, a large number of complex optimization problems have been widely addressed through multiobjective evolutionary algorithms (MOEAs), and the knee solutions of the Pareto front (PF) are most likely to be fitting for the decision maker (DM) without any user preferences. This work investigates the ontology matching problem, which is a challenge in the semantic web (SW) domain. Due to the complex heterogeneity between two different ontologies, it is arduous to get an excellent alignment that meets all DMs’ demands. To this end, a popular MOEA, i.e., nondominated sorting genetic algorithm (NSGA-II), is investigated to address the ontology matching problem, which outputs the knee solutions in the PF to meet diverse DMs’ requirements. In this study, for further enhancing the performance of NSGA-II, we propose to incorporate into NSGA-II’s evolutionary process the monkey king evolution algorithm (MKE) as the local search algorithm. The improved NSGA-II (iNSGA-II) is able to better converge to the real Pareto optimum region and ameliorate the quality of the solution. The experiment uses the famous benchmark given by the ontology alignment evaluation initiative (OAEI) to assess the performance of iNSGA-II, and the experiment results present that iNSGA-II is able to seek out preferable alignments than OAEI’s participators and NSGA-II-based ontology matching technique.

中文翻译：

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通过改进的NSGA-II优化本体排列

在过去的几十年中，许多复杂的优化问题已通过多目标进化算法（MOEA）得到了广泛解决，帕累托前沿（PF）的拐点解最有可能适合决策者（DM）用户首选项。这项工作研究了本体匹配问题，这是语义网（SW）领域中的一个挑战。由于两种不同本体之间的复杂异质性，要满足所有DM的需求，要实现出色的一致性是一项艰巨的任务。为此，研究了一种流行的MOEA，即非支配排序遗传算法（NSGA-II），以解决本体匹配问题，该问题在PF中输出拐点解以满足各种DM的要求。在这项研究中，为了进一步提高NSGA-II的性能，我们建议将Monkey King进化算法（MKE）纳入NSGA-II的进化过程中，作为本地搜索算法。改进的NSGA-II（iNSGA-II）可以更好地收敛到实际的帕累托最优区域，并改善解决方案的质量。实验使用了本体一致性评估倡议（OAEI）给出的著名基准来评估iNSGA-II的性能，并且实验结果表明，iNSGA-II能够找到比OAEI参与者和基于NSGA-II的更好的一致性本体匹配技术。