Fuzzy Structural Analysis Using Improved Jaya-based Optimization Approach

Abstract

A new approach to performing the α-level optimization in the fuzzy analysis of structural systems is developed in this study. The method uses a simple global optimizer, the Jaya algorithm, together with an innovative dimension reduction technique. The dimension reduction technique aims to transform the original large α-level optimization problem into a low-dimension one by making use of the monotonic behavior of the system output with respect to the input variables. Then, the Jaya algorithm is applied to solve the reduced max/min α-level optimization problems to determine the bounds of the fuzzy output. Two numerical examples, including a 2D truss and a 3D truss, with a relatively large number of fuzzy input variables are analyzed and the fuzzy displacements under static loads are predicted. It is demonstrated that the proposed approach can save a significant computational amount and also estimate the fuzzy displacement with high accuracy.