Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective optimization on a budget of 250 evaluations
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
IEEE Transactions on Evolutionary Computation
Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels
IEEE Transactions on Evolutionary Computation
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
On expected-improvement criteria for model-based multi-objective optimization
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
An evolution strategy assisted by an ensemble of local Gaussian process models
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A benchmark of kriging-based infill criteria for noisy optimization
Structural and Multidisciplinary Optimization
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Real-world optimization problems often require the consideration of multiple contradicting objectives. These multiobjective problems are even more challenging when facing a limited budget of evaluations due to expensive experiments or simulations. In these cases, a specific class of multiobjective optimization algorithms (MOOA) has to be applied. This paper provides a review of contemporary multiobjective approaches based on the singleobjective meta-model-assisted 'Efficient Global Optimization' (EGO) procedure and describes their main concepts. Additionally, a new EGO-based MOOA is introduced, which utilizes the $\mathcal{S}$-metric or hypervolume contribution to decide which solution is evaluated next. A benchmark on recently proposed test functions is performed allowing a budget of 130 evaluations. The results point out that the maximization of the hypervolume contribution within a real multiobjective optimization is superior to straightforward adaptations of EGO making our new approach capable of approximating the Pareto front of common problems within the allowed budget of evaluations.