Multi-Objective Optimization of Functionally Graded Beams Using a Genetic Algorithm with Non-Dominated Sorting

Abstract

A mixed layer-wise (LW) higher-order shear deformation theory (HSDT) is developed for the thermal buckling analysis of simply-supported, functionally graded (FG) beams subjected to a uniform temperature change. The material properties of the FG beam are assumed to be dependent on the thickness and temperature variables, and the effective material properties are estimated using either the rule of mixtures or the Mori–Tanaka scheme. The results shown in the numerical examples indicate the mixed LW HSDT solutions for critical temperature change parameters are in excellent agreement with the accurate solutions available in the literature. A multi-objective optimization of FG beams is presented to maximize the critical temperature change parameters and to minimize their total mass using a non-dominated sorting-based genetic algorithm. Some specific forms for the volume fractions of the constituents of the FG beam are assumed in advance, such as the one- and three-parameter power-law functions. The former is used in the thermal buckling analysis of the FG beams for comparison purposes, and the latter is used in their optimal design.