Trust-region methods
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Evolutionary Multiobjective Optimization: Theoretical Advances and Applications (Advanced Information and Knowledge Processing)
Mesh Adaptive Direct Search Algorithms for Constrained Optimization
SIAM Journal on Optimization
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Multiobjective Optimization Through a Series of Single-Objective Formulations
SIAM Journal on Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Computers and Industrial Engineering
A multicriteria simulation optimization method for injection molding
Proceedings of the Winter Simulation Conference
Zigzag Search for Continuous Multiobjective Optimization
INFORMS Journal on Computing
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This work proposes a new method for approximating the Pareto front of a multi-objective simulation optimization problem (MOP) where the explicit forms of the objective functions are not available. The method iteratively approximates each objective function using a metamodeling scheme and employs a weighted sum method to convert the MOP into a set of single objective optimization problems. The weight on each single objective function is adaptively determined by accessing newly introduced points at the current iteration and the non-dominated points so far. A trust region algorithm is applied to the single objective problems to search for the points on the Pareto front. The numerical results show that the proposed algorithm efficiently generates evenly distributed points for various types of Pareto fronts.