Structural Optimization Using Genetic Algorithms with Fuzzy Rules
Date Issued
2006
Date
2006
Author(s)
Shih, Chia-Sheng
DOI
zh-TW
Abstract
This thesis presents two fuzzy rules employed to adapt both parameters of genetic operators and penalty factor in genetic algorithms for optimum design of structures. Namely, one system adjusts the crossover rate as well as the mutation rate dynamically according to the information of current population, and the other adjusts penalty factor according to the amount and level of constraint violations by individuals. Two studies are conducted for the research. In the first one (as for the core study), an improved dynamic penalty method is applied to transform the constrained structural optimization problem into an unconstrained problem for the optimization procedure using genetic algorithms. With the developed program, several constrained structural optimization problems are thus investigated. The results demonstrate that the developed algorithm can be applied successfully to solve general structural optimization problems. In the second study, a further attempt is made to seek solutions to the multi-objective optimization problems. Hence a simple aggregating function method is raised to assign the fitness of individuals with fuzzy rules according to the original values of each objective function. To maintain the diversity of the population and reduce the computational complexity, the mutation rate was tested and chosen before the optimization process. Elitism was also adopted by using an external population to keep the elite individuals of the population. Through the use of the method, several nonconvex, disconnected Pareto-optimal solutions to multi-objective optimization problems are tested. Numerical experimental results demonstrate that the proposed method is simple and efficient in achieving optimum solutions to multi-objective problems.
Subjects
遺傳演算法
模糊法則
結構最佳化
多目標最佳化
Genetic algorithm
Fuzzy rules
Structural optimization
Multi-objective optimization
Type
thesis