Comprehensive Review of Population Based Metaheuristic Algorithms & Ontology Integration

Nowadays, it is important to work to enhance the integration of heterogeneous ontologies for the betterment of knowledge representation used by expert systems. Recently evolutionary types of metaheuristic algorithms gained more popularity in the field of heterogeneous ontology integration. The population based metaheuristic algorithms are not used as that much as other evolutionary types of algorithms. The ontology integration process will work efficiently if ontology alignments are optimized. To optimize the ontology alignments, it is observed that a single objective function is most preferred as compared to the multi-objective function. In this paper, a comprehensive analysis of existing methodologies is performed to investigate novelty in ontology integration models that applied population based metaheuristic algorithms. This helped to identify an efficient approach to integrate the heterogeneous ontologies.