Real-World Applications of Multiobjective Optimization
Multiobjective Optimization
On Uncertainty and Robustness in Evolutionary Optimization-Based MCDM
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Evolutionary multiobjective optimization in noisy problem environments
Journal of Heuristics
Reliability-based optimization using evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A systems approach to evolutionary multiobjective structural optimization and beyond
IEEE Computational Intelligence Magazine
Research frontier: memetic computation-past, present & future
IEEE Computational Intelligence Magazine
Memetic compact differential evolution for cartesian robot control
IEEE Computational Intelligence Magazine
Multi-objective reliability-based optimization with stochastic metamodels
Evolutionary Computation
ACM Transactions on Embedded Computing Systems (TECS)
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In this paper, we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in families of nested sets using a multi-level optimization search. To reduce the high computational costs of the proposed methodology we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness performances efficiently.