Integrating Energy Smart Grid’s ontologies through multi-objective particle swarm optimization algorithm with competitive mechanism - Xue, Xingsi; Tsai, Pei-Wei (Sustainable energy technologies and assessments, 2022)
Smart city seeks safer, resource-oriented, environment friendly, and cost-effective energy solutions, and due to the distributed and independent nature of energy management systems, Energy Smart Grid (ESG) quickly evolves from a widely accepted concept to an industrial reality. To monitor the energy consumption across various domains and assist efficient energy distribution and storage, it is necessary to integrate various ESG agents’ ontologies. To this end, this work models ESG’s Ontology Matching Problem (ESG-OMP) as a discrete Multi-Objective Optimization Problem (MOOP), which simultaneously optimizes the alignment’s completeness and correctness through determining an optimal entity corresponding set. After that, a Multi-Objective Particle Swarm Optimization Algorithm With Competitive Mechanism (MOPSO-CM) is proposed to address this issue by trading off the alignment’s completeness and correctness, which introduces the CM to update the particles with the pairwise competition that performed in the swarm of current generation. Moreover, an instance-based hybrid similarity measure is used to distinguish the heterogeneous ontology entities. The experiment uses OAEI’s testing datasets, 13 smart grid’s ontology matching tasks and 5 energy smart grid’s ontology matching tasks to test MOPSO-CM’s performance. The experimental results show that MOPSO-CM can effectively address various heterogeneous ontology matching problems and determine high-quality ESG’s ontology alignments.