Multi-objective Greylag Goose Optimization
Ashish Sharma, Komal Gupta, Krishna Jangir, Priyanshu Jain, Preetam Malakar · 2024
This study introduces a powerful adaptation of the Greylag Goose Optimization algorithm tailored for solving complex multi-objective problems known as the Multi-objective Greylag Goose Optimization Algorithm (MOGGO). It features a dynamic archive for storing the best solutions and a unique leader selection process inspired by goose migration. In extensive testing against eight benchmarks, MOGGO consistently surpassed other algorithms, demonstrating its ability to find optimal solutions quickly and effectively. Comparative analysis against state-of-the-art multi-objective optimization algorithms demonstrates the superior performance of MOGGO in terms of solution quality and convergence. The results obtained in terms of IGD and DM metrices highlight the potential of MOGGO as a competitive and efficient approach for solving complex multi-objective optimization problems across various domains.