Variable Functioning and Its Application to Large Scale Steel Frame Design Optimization

TL;DR

This paper introduces the variable functioning Fx approach to improve large-scale steel frame design optimization by reducing variables and narrowing search space, enhancing convergence and final design quality.

Contribution

The study presents a novel concept-based method called variable functioning Fx that integrates problem structure analysis and expert knowledge into optimization algorithms.

Findings

Fx improves convergence rate of optimization algorithms.

Fx enhances the quality of final steel frame designs.

Fx reduces the number of optimization variables.

Abstract

To solve complex real-world problems, heuristics and concept-based approaches can be used in order to incorporate information into the problem. In this study, a concept-based approach called variable functioning Fx is introduced to reduce the optimization variables and narrow down the search space. In this method, the relationships among one or more subset of variables are defined with functions using information prior to optimization; thus, instead of modifying the variables in the search process, the function variables are optimized. By using problem structure analysis technique and engineering expert knowledge, the $Fx$ method is used to enhance the steel frame design optimization process as a complex real-world problem. The proposed approach is coupled with particle swarm optimization and differential evolution algorithms and used for three case studies. The algorithms are applied to optimize the case studies by considering the relationships among column cross-section areas. The results show that $Fx$ can significantly improve both the convergence rate and the final design of a frame structure, even if it is only used for seeding.