TY - JOUR
T1 - A Study on Structural Optimum Design Based on Qualitative Sensitivities
AU - Arakawa, Masao
AU - Yamakawa, Hiroshi
PY - 1995
Y1 - 1995
N2 - An optimum design method is considered as a method whose results are the most reasonable from various standpoints in the prescribed mathematical formulation. However, its formulations present difficulties in choosing objective functions, setting allowances to constraints and so on, because they are greatly concerned with the results of optimization. In this study, we proposed concepts of qualitative sensitivitiesand qualitative optimality compared to quantitative concepts, as well as a general algorithm of the qualitative optimization method. We newly defined three kinds of fuzzy language, assigned fuzzy sets to eachof them and assigned them to each candidate for discrete design variable. We proposed fuzzy reasoning rules by using these two kinds of fuzzy sets. We also extended the proposed algorithm to multi-objective optimization. Through the numerical example of a single-objective problem, we obtained almost the same solution as in quantitative methods, and efficiencieswere confirmed for this case. Also through the example of a multiobjective problem, the new method was shown to have more flexibility in determining the preferred objective functions compared to the conventional method.
AB - An optimum design method is considered as a method whose results are the most reasonable from various standpoints in the prescribed mathematical formulation. However, its formulations present difficulties in choosing objective functions, setting allowances to constraints and so on, because they are greatly concerned with the results of optimization. In this study, we proposed concepts of qualitative sensitivitiesand qualitative optimality compared to quantitative concepts, as well as a general algorithm of the qualitative optimization method. We newly defined three kinds of fuzzy language, assigned fuzzy sets to eachof them and assigned them to each candidate for discrete design variable. We proposed fuzzy reasoning rules by using these two kinds of fuzzy sets. We also extended the proposed algorithm to multi-objective optimization. Through the numerical example of a single-objective problem, we obtained almost the same solution as in quantitative methods, and efficiencieswere confirmed for this case. Also through the example of a multiobjective problem, the new method was shown to have more flexibility in determining the preferred objective functions compared to the conventional method.
KW - Design Engineering
KW - Fuzzy Reasoning
KW - Fuzzy Set Theory
KW - Optimum Design
KW - Qualitative Sensitivity
KW - Sensitivity Analysis
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U2 - 10.1299/jsmec1993.38.190
DO - 10.1299/jsmec1993.38.190
M3 - Article
AN - SCOPUS:0029277155
SN - 1340-8062
VL - 38
SP - 190
EP - 198
JO - JSME International Journal, Series C: Dynamics, Control, Robotics, Design and Manufacturing
JF - JSME International Journal, Series C: Dynamics, Control, Robotics, Design and Manufacturing
IS - 1
ER -