Variable functioning and its application to large scale steel frame design optimization

Abstract

To solve complex real-world problems, heuristics and concept-based approaches can be used 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 subsets of variables are defined with functions using information prior to optimization; thus, the function variables are optimized instead of modifying the variables in the search process. By using the 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. Herein, the proposed approach was coupled with particle swarm optimization and differential evolution algorithms then applied 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.

A novel multi-hybrid differential evolution algorithm for optimization of frame structures

“…Also, the frame structure benchmark problems are highly challenging to design due to higher level of difficulty in their implementation 80 . The figures of the three frames are taken from 81 .…”

“…Overall, MHDE provides more reliable results than most of the well-known algorithms reported in literature.

Figure 8 Design of 1-bay 8-story frame 81 .

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“…This further proves the superiority of MHDE algorithm in comparison to others.

Figure 9 Design of 3-bay 15-story frame 81 .

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