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
Creating Robust Solutions by Means of Evolutionary Algorithms
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Faster convergence by means of fitness estimation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Design and Analysis of Experiments
Design and Analysis of Experiments
Trade-off between performance and robustness: an evolutionary multiobjective approach
EMO'03 Proceedings of the 2nd 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
Genetic algorithms with a robust solution searching scheme
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Multiobjective robustness for portfolio optimization in volatile environments
Proceedings of the 10th annual conference on Genetic and evolutionary computation
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
Avoiding the pitfalls of noisy fitness functions with genetic algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Yield enhancement by robust application-specific mapping on Network-on-Chips
Proceedings of the 2nd International Workshop on Network on Chip Architectures
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Optimization of temporal processes: a model predictive control approach
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Optimal strategies of the iterated prisoner's dilemma problem for multiple conflicting objectives
IEEE Transactions on Evolutionary Computation
Reliability-based optimization using evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Closed-loop evolutionary multiobjective optimization
IEEE Computational Intelligence Magazine
Multiobjective optimization of temporal processes
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Robust task scheduling for volunteer computing systems
The Journal of Supercomputing
A multiobjective metaheuristic for a mean-risk static stochastic knapsack problem
Computational Optimization and Applications
Robust design of embedded systems
Proceedings of the Conference on Design, Automation and Test in Europe
Design for Six Sigma through collaborative multiobjective optimization
Computers and Industrial Engineering
Hierarchical stochastic metamodels based on moving least squares and polynomial chaos expansion
Structural and Multidisciplinary Optimization
Multi-objective reliability-based optimization with stochastic metamodels
Evolutionary Computation
Many-objective de Novo water supply portfolio planning under deep uncertainty
Environmental Modelling & Software
ACM Transactions on Embedded Computing Systems (TECS)
Quantum control experiments as a testbed for evolutionary multi-objective algorithms
Genetic Programming and Evolvable Machines
On optimizing a bi-objective flowshop scheduling problem in an uncertain environment
Computers & Mathematics with Applications
Advances in evolutionary multi-objective optimization
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
Many objective robust decision making for complex environmental systems undergoing change
Environmental Modelling & Software
Design optimization for robustness in multiple performance functions
Structural and Multidisciplinary Optimization
A variational approach to define robustness for parametric multiobjective optimization problems
Journal of Global Optimization
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In optimization studies including multi-objective optimization, the main focus is placed on finding the global optimum or global Pareto-optimal solutions, representing the best possible objective values. However, in practice, users may not always be interested in finding the so-called global best solutions, particularly when these solutions are quite sensitive to the variable perturbations which cannot be avoided in practice. In such cases, practitioners are interested in finding the robust solutions which are less sensitive to small perturbations in variables. Although robust optimization is dealt with in detail in single-objective evolutionary optimization studies, in this paper, we present two different robust multi-objective optimization procedures, where the emphasis is to find a robust frontier, instead of the global Pareto-optimal frontier in a problem. The first procedure is a straightforward extension of a technique used for single-objective optimization and the second procedure is a more practical approach enabling a user to set the extent of robustness desired in a problem. To demonstrate the differences between global and robust multi-objective optimization principles and the differences between the two robust optimization procedures suggested here, we develop a number of constrained and unconstrained test problems having two and three objectives and show simulation results using an evolutionary multi-objective optimization (EM0) algorithm. Finally, we also apply both robust optimization methodologies to an engineering design problem.