Improving the Efficiency of Multi-Objective Grasshopper Optimization Algorithm to Enhance Ontology Alignment

Ontology alignment is an essential and complex task to integrate heterogeneous ontology. The meta-heuristic algorithm has proven be effective method for ontology alignment. However, it only applies the inherent advantages of meta-heuristics rarely considers execution efficiency, especially multi-objective model. performance such optimization models mostly depends on well-distributed fast-converged set solutions in real-world applications. In this paper, two grasshopper algorithms (MOGOA) are proposed enhance One ε -dominance concept based GOA (EMO-GOA) other fast Non-dominated Sorting (NS-MOGOA). methods align evaluated by using benchmark dataset. results demonstrate that EMO-GOA NS-MOGOA improve quality reduce running time compared with well-known metaheuristic state-of-the-art methods.