Fuzzy Controlled Genetic Algorithm Search for Shape Optimization

Abstract

Shape design of a structure plays a significant part in deciding its least weight. In this paper, a fuzzy controlled genetic-based search technique for structural shape optimization is investigated. An automated optimal procedure based on the proposed approach is developed and used in the least-weight design of truss structures, which include their geometry as a design variable to be optimized. To increase the performance of the genetic-based approach for shape optimization problems, the design constraints related to member stress, joint displacement, and member buckling are described by using fuzzy set theory. A fuzzy rule-based system representing expert knowledge and experience is incorporated in the approach to control its optimal search process. Four examples for shape designs are presented to demonstrate the effectiveness and efficiency of the proposed hybrid approach in comparison with the use of pure genetic algorithms and other numerical methods. The examples show that the approach is flexible enough to deal with rigidly jointed structures.