Robust Optimization and Sensitivity Analysis with Multi-objective Genetic Algorithms: Single- and Multi-disciplinary Applications

Robust Optimization and Sensitivity Analysis with Multi-objective Genetic Algorithms: Single- and Multi-disciplinary Applications

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The dissertation presents models and approaches under four research thrusts. In the first thrust, an approach is presented to obtain robustly optimal solutions which are as best as possible, in a multi-objective sense, and at the same time their sensitivity of objective and/or constraint functions is within an acceptable range. In the second thrust, the robust optimization approach in the first thrust is extended to design optimization problems which are decomposed into multiple subproblems, each with multiple objectives and constraints. In the third thrust, a new approach for multi-objective sensitivity analysis and uncertainty reduction is presented. And in the final research thrust, a metamodel embedded Multi-Objective Genetic Algorithm (MOGA) for solution of design optimization problems is presented.... percentage (e.g., 40%) of individuals in the population is generated by genetic operations so that the algorithm can keep searching for new optima. ... The choice of these percentage and probability values is suggested by Deb [2001] or by MATLAB GADS Usera#39;s Manual. ... Code designs Simulation Current population of designs Fitness evaluation Dominated designs Non-dominated designs Eliteanbsp;...


Title:Robust Optimization and Sensitivity Analysis with Multi-objective Genetic Algorithms: Single- and Multi-disciplinary Applications
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Publisher:ProQuest - 2007
ISBN-13:

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