A surrogate-assisted evolutionary algorithm with multi-perspective infill sampling for expensive super-many-objective optimization problems

Key Points

• The proposed algorithm enhances optimization in super-many-objective scenarios, targeting multiple complex objectives simultaneously.
• Notably, the method integrates multi-perspective infill sampling, improving the efficiency of locating optimal solutions.
• Assessment involves surrogate models to estimate outcomes, facilitating faster evaluations in optimization processes.
• The findings may significantly impact the way complicated optimization problems are approached across various fields.