Non-Linear Finite Element Analysis of Solids and Structures: Essentials
Non-Linear Finite Element Analysis of Solids and Structures: Essentials
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
Pareto-Front Exploration with Uncertain Objectives
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Nonlinear positional formulation for space truss analysis
Finite Elements in Analysis and Design
On using deterministic FEA software to solve problems in stochastic structural mechanics
Computers and Structures
Introducing robustness in multi-objective optimization
Evolutionary Computation
Reliability-based optimization using evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Reliability-based multi-objective optimization using evolutionary algorithms
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Searching for robust pareto-optimal solutions in multi-objective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Fuzzy-Pareto-Dominance and its application in evolutionary multi-objective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multi-objective optimization of problems with epistemic uncertainty
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
Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation
IEEE Transactions on Evolutionary Computation
Multiobjective topology optimization of truss structures with kinematic stability repair
Structural and Multidisciplinary Optimization
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While surrogate-based optimization has encountered a growing success in engineering design, the development of stochastic metamodels, i.e. capable of representing the complete random responses with respect to random inputs, is still an open issue, although they could be fruitfully used in optimization under uncertainty, both with single and multiple objectives. Therefore, the contribution of the paper is twofold. First, hierarchical stochastic metamodels based on moving least squares and spectral decomposition (by polynomial chaos expansion) are proposed in order to obtain a complete description of the random responses with respect to the deterministic and random input parameters. Then, these metamodels are incorporated into a novel multiobjective reliability-based formulation leaning on the concept of probabilistic nondominance. The whole procedure is applied to an analytical test case as well as to the design optimization of space truss structures, demonstrating the ability of the proposed method to provide accurate solutions at an affordable computational time.