Seeding the initial population with feasible solutions in metaheuristic optimization of steel trusses
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Date
2018
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Taylor & Francis Ltd
Abstract
In spite of considerable research work on the development of efficient algorithms for discrete sizing optimization of steel truss structures, only a few studies have addressed non-algorithmic issues affecting the general performance of algorithms. For instance, an important question is whether starting the design optimization from a feasible solution is fruitful or not. This study is an attempt to investigate the effect of seeding the initial population with feasible solutions on the general performance of metaheuristic techniques. To this end, the sensitivity of recently proposed metaheuristic algorithms to the feasibility of initial candidate designs is evaluated through practical discrete sizing of real-size steel truss structures. The numerical experiments indicate that seeding the initial population with feasible solutions can improve the computational efficiency of metaheuristic structural optimization algorithms, especially in the early stages of the optimization. This paves the way for efficient metaheuristic optimization of large-scale structural systems.
Description
Kazemzadeh Azad, Saeid/0000-0001-9309-607X
Keywords
Discrete optimization, steel trusses, metaheuristic algorithms, big bang-big crunch algorithm, AISC-LRFD
Turkish CoHE Thesis Center URL
Citation
53
WoS Q
Q2
Scopus Q
Q2
Source
Volume
50
Issue
1
Start Page
89
End Page
105