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dc.contributor.authorLalonde, Nicolas
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date2009-08-10 21:59:13.795en
dc.date.accessioned2009-08-13T15:10:33Z
dc.date.available2009-08-13T15:10:33Z
dc.date.issued2009-08-13T15:10:33Z
dc.identifier.urihttp://hdl.handle.net/1974/2586
dc.descriptionThesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2009-08-10 21:59:13.795en
dc.description.abstractThis research aims to improve our understanding of multiobjective optimization, by comparing the performance of five multiobjective optimization algorithms, and by proposing a new formulation to consider input uncertainty in multiobjective optimization problems. Four deterministic multiobjective optimization algorithms and one probabilistic algorithm were compared: the Weighted Sum, the Adaptive Weighted Sum, the Normal Constraint, the Normal Boundary Intersection methods, and the Nondominated Sorting Genetic Algorithm-II (NSGA-II). The algorithms were compared using six test problems, which included a wide range of optimization problem types (bounded vs. unbounded, constrained vs. unconstrained). Performance metrics used for quantitative comparison were the total run (CPU) time, number of function evaluations, variance in solution distribution, and numbers of dominated and non-optimal solutions. Graphical representations of the resulting Pareto fronts were also presented. No single method outperformed the others for all performance metrics, and the two different classes of algorithms were effective for different types of problems. NSGA-II did not effectively solve problems involving unbounded design variables or equality constraints. On the other hand, the deterministic algorithms could not solve a problem with a non-continuous objective function. In the second phase of this research, design under uncertainty was considered in multiobjective optimization. The effects of input uncertainty on a Pareto front were quantitatively investigated by developing a multiobjective robust optimization framework. Two possible effects on a Pareto front were identified: a shift away from the Utopia point, and a shrinking of the Pareto curve. A set of Pareto fronts were obtained in which the optimum solutions have different levels of insensitivity or robustness. Four test problems were used to examine the Pareto front change. Increasing the insensitivity requirement of the objective function with regard to input variations moved the Pareto front away from the Utopia point or reduced the length of the Pareto front. These changes were quantified, and the effects of changing robustness requirements were discussed. The approach would provide designers with not only the choice of optimal solutions on a Pareto front in traditional multiobjective optimization, but also an additional choice of a suitable Pareto front according to the acceptable level of performance variation.en
dc.format.extent5328989 bytes
dc.format.mimetypeapplication/pdf
dc.languageenen
dc.language.isoenen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectMultiobjective Optimizationen
dc.subjectBenchmarkingen
dc.subjectUncertaintyen
dc.subjectRobust Designen
dc.subjectPareto Fronten
dc.titleMultiobjective Optimization Algorithm Benchmarking and Design Under Parameter Uncertaintyen
dc.typethesisen
dc.description.degreeMasteren
dc.contributor.supervisorKim, Il-Yongen
dc.contributor.departmentMechanical and Materials Engineeringen


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