Fuzzy Set Theory with Genetic Algorithm in Constrained Structural Optimization
Abstract
This paper presents a new design optimization approach which combines concepts of artificial intelligence and fuzzy logic. A genetic algorithm (GA) is the search tool. Design space is redefined through fuzzy set theory. Linear and discrete membership functions are studied to transform crisp constraints into fuzzy ones. Infeasible points are redefined as incomplete solutions by fuzzy set theory, and regarded as lower grade members of a fuzzy feasible set. Fuzzy control and adjustment are introduced to the GA's evolutionary process. An eight-bar truss optimal design (single-objective and multiobjective) is provided to explain analysis procedures of the proposed optimal method.
Recommended Citation
F. Y. Cheng and D. Li, "Fuzzy Set Theory with Genetic Algorithm in Constrained Structural Optimization," Proceedings of the US-Japan Joint Seminar on Structural Optimization, pp. 55 - 66, Jan 1997.
Department(s)
Civil, Architectural and Environmental Engineering
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Publication Date
01 Jan 1997