Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Trade-off between performance and robustness: an evolutionary multiobjective approach
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Multiobjective robustness for portfolio optimization in volatile environments
Proceedings of the 10th annual conference on Genetic and evolutionary computation
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
Reliability-based optimization using evolutionary algorithms
IEEE Transactions on Evolutionary Computation
An investigation on noise-induced features in robust evolutionary multi-objective optimization
Expert Systems with Applications: An International Journal
Handling uncertainties in evolutionary multi-objective optimization
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
Robust design of embedded systems
Proceedings of the Conference on Design, Automation and Test in Europe
Structural and Multidisciplinary Optimization
Design optimization for robustness in multiple performance functions
Structural and Multidisciplinary Optimization
Structural and Multidisciplinary Optimization
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Real-world multi-objective engineering design optimization problems often have parameters with uncontrollable variations. The aim of solving such problems is to obtain solutions that in terms of objectives and feasibility are as good as possible and at the same time are least sensitive to the parameter variations. Such solutions are said to be robust optimum solutions. In order to investigate the trade-off between the performance and robustness of optimum solutions, we present a new Robust Multi-Objective Genetic Algorithm (RMOGA) that optimizes two objectives: a fitness value and a robustness index. The fitness value serves as a measure of performance of design solutions with respect to multiple objectives and feasibility of the original optimization problem. The robustness index, which is based on a non-gradient based parameter sensitivity estimation approach, is a measure that quantitatively evaluates the robustness of design solutions. RMOGA does not require a presumed probability distribution of uncontrollable parameters and also does not utilize the gradient information of these parameters. Three distance metrics are used to obtain the robustness index and robust solutions. To illustrate its application, RMOGA is applied to two well-studied engineering design problems from the literature.