Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Maufacturing supply chain applications 1: supply chain multi-objective simulation optimization
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Fitness inheritance for noisy evolutionary multi-objective optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Inverse multi-objective robust evolutionary design
Genetic Programming and Evolvable Machines
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Multiobjective evolutionary algorithm for the optimization of noisy combustion processes
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
Accumulative sampling for noisy evolutionary multi-objective optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
On optimizing a bi-objective flowshop scheduling problem in an uncertain environment
Computers & Mathematics with Applications
Multi-objective optimization with estimation of distribution algorithm in a noisy environment
Evolutionary Computation
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This paper presents a multiobjective evolutionary algorithm (MOEA) capable of handling stochastic objective functions. We extend a previously developed approach to solve multiple objective optimization problems in deterministic environments by incorporating a stochastic nondomination-based solution ranking procedure. In this study, concepts of stochastic dominance and significant dominance are introduced in order to better discriminate among competing solutions. The MOEA is applied to a number of published test problems to assess its robustness and to evaluate its performance relative to NSGA-II. Moreover, a new stopping criterion is proposed, which is based on the convergence velocity of any MOEA to the true Pareto optimal front, even if the exact location of the true front is unknown. This stopping criterion is especially useful in real-world problems, where finding an appropriate point to terminate the search is crucial.