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.
Bibliographical noteFunding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2011-0024829).
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Science Applications
- Computer Graphics and Computer-Aided Design
- Control and Optimization