Multi-objective seismic design optimization of steel frames by a chaotic meta-heuristic algorithm

In this study, multi-objective optimization is applied to implement performance-based design of steel moment-resisting frame (SMRF) structures. In order to efficiently achieve this purpose, a chaotic multi-objective firefly algorithm (CMOFA) is proposed to find the Pareto optimal front for the multi...

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Vydáno v:Engineering with computers Ročník 33; číslo 4; s. 1045 - 1060
Hlavní autoři: Gholizadeh, Saeed, Baghchevan, Amir
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.10.2017
Springer Nature B.V
Témata:
Algorithms
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Classical Mechanics
Computer Science
Computer-Aided Engineering (CAD
Control
Design optimization
Heuristic
Heuristic methods
Math. Applications in Chemistry
Mathematical and Computational Engineering
Multiple objective analysis
Original Article
Seismic design
Steel frames
Steel structures
Structural weight
Systems Theory
Performance-based design
Steel moment-resisting frame
Multi-objective optimization
Chaotic map
Meta-heuristic
Firefly algorithm
ISSN:0177-0667, 1435-5663
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Shrnutí:In this study, multi-objective optimization is applied to implement performance-based design of steel moment-resisting frame (SMRF) structures. In order to efficiently achieve this purpose, a chaotic multi-objective firefly algorithm (CMOFA) is proposed to find the Pareto optimal front for the multi-objective performance-based optimum design (MO-PBOD) problem of SMRFs. The structural weight and the maximum inter-story drift at performance levels are taken as the conflicting objective functions of the MO-PBOD problem which should be optimized simultaneously subject to serviceability and ultimate limit-state constraints. In order to illustrate the efficiency of the proposed CMOFA meta-heuristic, two benchmark truss examples and three MO-PBOD examples of SMRFs are presented. The numerical results demonstrate the better computational performance of the proposed CMOFA meta-heuristic in comparison with some existing multi-objective algorithms.
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ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-017-0515-0