Constrained Test Problems for Multi-objective Evolutionary Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
A multi-objective genetic algorithm for robust design optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Robust engineering design with genetic algorithms
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Interval robust multi-objective evolutionary algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Searching for robust pareto-optimal solutions in multi-objective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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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.