TY - GEN
T1 - A multi-objective memetic algorithm for RBDO and robust design
AU - Zhuang, Xiaotian
AU - Pan, Rong
PY - 2010/6/1
Y1 - 2010/6/1
N2 - 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.
AB - 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.
KW - Memetic algorithm
KW - Multi-objective optimization
KW - Reliability-based design optimization
KW - Robust design
UR - http://www.scopus.com/inward/record.url?scp=77952760836&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77952760836&partnerID=8YFLogxK
U2 - 10.1109/RAMS.2010.5447967
DO - 10.1109/RAMS.2010.5447967
M3 - Conference contribution
AN - SCOPUS:77952760836
SN - 9781424451036
T3 - Proceedings - Annual Reliability and Maintainability Symposium
BT - Annual Reliability and Maintainability Symposium
T2 - Annual Reliability and Maintainability Symposium: The International Symposium on Product Quality and Integrity, RAMS 2010
Y2 - 25 January 2010 through 28 January 2010
ER -