Structural Optimization Using Genetic Algorithms with Fuzzy Rule-Based Systems

Abstract

This paper presents two fuzzy rule-based systems to adapt parameters of genetic operators and a penalty factor in genetic algorithms for optimum design of structures. 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. The first fuzzy rule-based system adjusts the crossover rate and the mutation rate dynamically according to the information of current population. The other fuzzy rule-based system adjusts the penalty factor according to the amount and level of constraint violations of all individuals. With the developed program, several constrained structural optimization problems are investigated. The results demonstrate that the developed algorithm can be applied successfully to general structural optimization problems.