A multi-objective memetic algorithm for RBDO and robust design

Product design optimization under model or input variable uncertainty is commonly required, in which robustness and reliability are two important attributes of the design. In structure design, it is critical to maintain the design feasibility (or reliability); while at the same time, to counter manu...

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Vydáno v:2010 Proceedings - Annual Reliability and Maintainability Symposium (RAMS) s. 1 - 6
Hlavní autoři: Xiaotian Zhuang, Rong Pan
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.01.2010
Témata:
Algorithm design and analysis
Constraint optimization
Counting circuits
Design optimization
Input variables
Maintenance
Manufacturing
Memetic Algorithm
Multi-Objective Optimization
Product design
Reliability-Based Design Optimization
Robust Design
Robustness
Uncertainty
ISBN:9781424451029, 1424451027
ISSN:0149-144X
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Abstract Product design optimization under model or input variable uncertainty is commonly required, in which robustness and reliability are two important attributes of the design. In structure design, it is critical to maintain the design feasibility (or reliability); while at the same time, to counter manufacturing variations, robust design is employed in order to obtain high product quality. It is necessary, therefore, to establish a multi-objective optimization problem that combines both robustness and reliability considerations, where the product's performance variation and the performance function are simultaneously optimized, subject to probabilistic constraints for design feasibility. Based on the idea of the reliability index and the Most Probable Point (MPP), probabilistic constraints are converted into deterministic constraints. Then an efficient Multi-Objective Memetic Algorithm (MOMA) is presented to minimize robustness and performance value, subject to deterministic constraints. A classical I-beam example with probabilistic constraints illustrating the MOMA concept and applicability is proposed in the paper, the result is a Pareto frontier of robustness and performance value.
AbstractList Product design optimization under model or input variable uncertainty is commonly required, in which robustness and reliability are two important attributes of the design. In structure design, it is critical to maintain the design feasibility (or reliability); while at the same time, to counter manufacturing variations, robust design is employed in order to obtain high product quality. It is necessary, therefore, to establish a multi-objective optimization problem that combines both robustness and reliability considerations, where the product's performance variation and the performance function are simultaneously optimized, subject to probabilistic constraints for design feasibility. Based on the idea of the reliability index and the Most Probable Point (MPP), probabilistic constraints are converted into deterministic constraints. Then an efficient Multi-Objective Memetic Algorithm (MOMA) is presented to minimize robustness and performance value, subject to deterministic constraints. A classical I-beam example with probabilistic constraints illustrating the MOMA concept and applicability is proposed in the paper, the result is a Pareto frontier of robustness and performance value.
Author Xiaotian Zhuang
Rong Pan
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  surname: Rong Pan
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  organization: Arizona State Univ., Tempe, AZ, USA
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SubjectTerms Algorithm design and analysis
Constraint optimization
Counting circuits
Design optimization
Input variables
Maintenance
Manufacturing
Memetic Algorithm
Multi-Objective Optimization
Product design
Reliability-Based Design Optimization
Robust Design
Robustness
Uncertainty
Title A multi-objective memetic algorithm for RBDO and robust design
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