Conservative multi‐objective optimization considering design robustness and tolerance: a quality engineering design approach

Methods of multi‐objective optimization are proposed to account for tolerance of design variable and variation in problem parameter. The post‐optimization effort is initiated from deterministic Pareto‐optimal solutions that were obtained from NSGA‐II. The successive process to determine search directions and step sizes toward conservative multi‐objective solutions was conducted by design of experiments to determine the worst design that had the highest constraint violation. The signal‐to‐noise (S/N) ratio was also employed to represent the robustness of constrained objective functions under parameter variation. Structural optimization was explored to accommodate both design tolerance and parameter variation and further apply S/N ratio in conservative multi‐objective optimization.