Azad, Saeıd Kazemzadeh

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Azad, S. Kazemzadeh
Saeid Kazemzadeh, Azad
S.K.Azad
Azad, Saeid Kazemzadeh
S., Azad
Azad, Saeıd Kazemzadeh
A., Saeid Kazemzadeh
S.,Azad
Azad,S.K.
A.,Saeid Kazemzadeh
Saeıd Kazemzadeh, Azad
A.,Saeıd Kazemzadeh
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saeid.azad@atilim.edu.tr
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Scholarly Output

21

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19

Citation Count

390

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2

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Now showing 1 - 10 of 21
  • Thumbnail Image
    Article
    Citation Count: 1
    Metaheuristic Optimization of Rotating Multilayer Composite Tubes Under Internal Heating and Pressure
    (Springer, 2022) Akış, Tolga; Akis, Tolga; Azad, Saeıd Kazemzadeh; Civil Engineering; Department of Civil Engineering
    Although analysis/design of multilayer assemblies has been always an active field of research, works on the optimal design of rotating multilayer composite tubes are very limited. This paper addresses the design optimization of rotating multilayer composite tubes under internal heating and pressure. For determining the structural responses, analytical solutions are provided based on different boundary conditions. The automated selection of optimal material as well as thickness optimization of pressurized multilayer assemblies is carried out under different angular speed and internal heating conditions using a metaheuristic algorithm. The corresponding optimum design for each angular speed as well as internal heating condition is sought, and the numerical results are discussed. The study provides general guidelines for conceptual design of rotating multilayer composite tubes subjected to internal heating and pressure.
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    Article
    Citation Count: 13
    Discrete Sizing of Steel Frames Using Adaptive Dimensional Search Algorithm
    (Budapest Univ Technology Economics, 2019) Azad, Saeıd Kazemzadeh; Azad, Saeid Kazemzadeh; Department of Civil Engineering
    Adaptive dimensional search (ADS) algorithm is a recently proposed metaheuristic optimization technique for discrete structural optimization problems. In this study, discrete sizing optimization problem of steel frames is tackled using the ADS algorithm. An important feature of the algorithm is that it does not use any metaphor as an underlying principle for its implementation. Instead, the algorithm employs an efficient performance-oriented methodology at each iteration for convergence to the optimum or a near optimum solution. The performance of the ADS is investigated through optimum design of five real-size steel frame structures and the results are compared versus several contemporary metaheuristic techniques. The comparison of the obtained numerical results with those of available designs in the literature reveals the reliability and efficiency of the ADS in optimum design of steel frames.
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    Article
    Citation Count: 5
    Improving Computational Efficiency of Bat-Inspired Algorithm in Optimal Structural Design
    (Sage Publications inc, 2015) Azad, Saeıd Kazemzadeh; Azad, S. Kazemzadeh; Department of Civil Engineering
    Bat-inspired (BI) algorithm is a recent metaheuristic optimization technique that simulates echolocation behavior of bats in seeking a design space. Along the same line with almost all metaheuristics, this algorithm also entails a large number of time-consuming structural analyses in structural design optimization applications. This study is focused on improving computational efficiency of the BI algorithm in optimum structural design. The number of structural analyses required by BI algorithm in the course of design optimization is reduced considerably by incorporating an upper bound strategy (UBS) into the solution procedure. The performance of the resulting algorithm, i.e. UBS integrated BI algorithm (UBI), is evaluated in discrete sizing optimization of large-scale steel skeletal structures designed for minimum weight according to American Institute of Steel Construction-Allowable Stress Design provisions. The numerical results verify that the UBI results in a significant gain in the computational efficiency of the standard algorithm.
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    Article
    Citation Count: 1
    A Standard Benchmarking Suite for Structural Optimization Algorithms: Iscso 2016-2022
    (Elsevier Science inc, 2023) Azad, Saeid Kazemzadeh; Azad, Saeıd Kazemzadeh; Azad, Sina Kazemzadeh; Azad, Saeıd Kazemzadeh; Azad, Saeıd Kazemzadeh; Department of Civil Engineering; Department of Civil Engineering
    Benchmarking is an essential part of developing efficient structural optimization techniques. Despite the advent of numerous metaheuristic techniques for solving truss optimization problems, benchmarking new algorithms is often carried out using a selection of classic test examples which are indeed unchallenging for contemporary sophisticated optimization algorithms. Furthermore, the limited optimization results available in the literature on new test examples are usually not accurately comparable. This is typically due to the lack of infromation about the performance of the investigated algorithms and the inconsistencies between the studies in terms of adopted test examples for benchmarking, optimization problem formulation, maximum number of objective function evaluations and other similar issues. Accordingly, there exists a need for developing new standard test suites composed of easily reproducible challenging test examples with rigorous and comparable performance evaluation results of algorithms on these test suites. To this end, the present work aims to propose a new baseline for benchmarking structural optimization algorithms, using a set of challenging sizing and shape optimization problems of truss structures selected from the international student competition in structural optimization (ISCSO) instances. The most recent six structural optimization examples from the ISCSO are tackled using a representative metaheuristic structural optimization algorithm. The statistical results of all the optimization runs using the proposed benchmarking suite are provided to pave the way for more rigorous benchmarking of structural optimization algorithms.
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    Article
    Citation Count: 16
    Design Optimization of Real-Size Steel Frames Using Monitored Convergence Curve
    (Springer, 2021) Azad, Saeid Kazemzadeh; Azad, Saeıd Kazemzadeh; Azad, Saeıd Kazemzadeh; Azad, Saeıd Kazemzadeh; Department of Civil Engineering; Department of Civil Engineering
    It is an undeniable fact that there are main challenges in the use of metaheuristics for optimal design of real-size steel frames in practice. In general, steel frame optimization problems usually require an inordinate amount of processing time where the main portion of computational effort is devoted to myriad structural response computations during the optimization iterations. Moreover, the inherent complexity of steel frame optimization problems may result in poor performance of even contemporary or advanced metaheuristics. Beside the challenging nature of such problems, significant difference in geometrical properties of two adjacent steel sections in a list of available profiles can also mislead the optimization algorithm and may result in trapping the algorithm in a poor local optimum. Consequently, akin to other challenging engineering optimization instances, significant fluctuations could be observed in the final results of steel frame optimization problems over multiple runs even using contemporary metaheuristics. Accordingly, the main focus of this study is to improve the solution quality as well as the stability of results in metaheuristic optimization of real-size steel frames using a recently developed framework so-called monitored convergence curve (MCC). Two enhanced variants of the well-known big bang-big crunch algorithm are adopted as typical contemporary metaheuristic algorithms to evaluate the usefulness of the MCC framework in steel frame optimization problems. The numerical experiments using challenging test examples of real-size steel frames confirm the efficiency of the MCC integrated metaheuristics versus their standard counterparts.
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    Article
    Citation Count: 92
    Adaptive Dimensional Search: a New Metaheuristic Algorithm for Discrete Truss Sizing Optimization
    (Pergamon-elsevier Science Ltd, 2015) Hasancebi, Oguzhan; Azad, Saeıd Kazemzadeh; Azad, Saeid Kazemzadeh; Azad, Saeıd Kazemzadeh; Azad, Saeıd Kazemzadeh; Department of Civil Engineering; Department of Civil Engineering
    In the present study a new metaheuristic algorithm called adaptive dimensional search (ADS) is proposed for discrete truss sizing optimization problems. The robustness of the ADS lies in the idea of updating search dimensionality ratio (SDR) parameter online during the search for a rapid and reliable convergence towards the optimum. In addition, several alternative stagnation-control strategies are integrated with the algorithm to escape from local optima, in which a limited uphill (non-improving) move is permitted when a stagnation state is detected in the course of optimization. Besides a remarkable computational efficiency, the ease of implementation and capability of locating promising solutions for challenging instances of practical design optimization are amongst the remarkable features of the proposed algorithm. The efficiency of the ADS is investigated and verified using two benchmark examples as well as three real-world problems of discrete sizing truss optimization. A comparison of the numerical results obtained using the ADS with those of other metaheuristic techniques indicates that the proposed algorithm is capable of locating improved solutions using much lesser computational effort. (C) 2015 Elsevier Ltd. All rights reserved.
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    Master Thesis
    Serbest Biçimli Çelık Çift Katmanlı Uzay Kafeslerin Optimizasyonu ve Standardizasyonu
    (2021) Shaban, Samer S S; Azad, Saeıd Kazemzadeh; Azad, Saeıd Kazemzadeh; Department of Civil Engineering
    Son yıllarda düzensiz eğrili ancak estetik açıdan hoş görünen konfigürasyonlara sahip serbest biçimli yapıların kullanımına artan bir ilgi olmuştur. Normal çelik uzay kafeslerin tasarım optimizasyonu, yapısal optimizasyon literatüründe detaylı bir şekilde ele alınsa da, gerçek boyutlu serbest biçimli uzay kafes yapıların optimum tasarımında yapılan çalışmalar hala sınırlı düzeyde kalmıştır. Bir yandan, gerçek boyutlu serbest biçimli çelik kafeslerin optimizasyonunun önündeki ana engel, güncel evrimsel optimizasyon algoritmalarıyla ortaya çıkan aşırı hesaplama yüküdür. Öte yandan, algoritma tarafından nihai tasarımda farklı çelik kesit boyutlarının sayıca fazlalığını önlemek için gerekli kısıtlamalar uygulanmamışsa, geleneksel optimizasyon algoritmaları kullanılarak elde edilen nihai tasarımların uygulamada tercih edilmemesi muhtemeldir. Bu nedenle, tek bir optimum veya optimuma yakın tasarım sunmak yerine, tasarımcıya veya karar vericiye, hem minimum ağırlığı hem de mevcut ürün çeşitliliğini hesaba katan ve etkin tasarım alternatifleri setinden oluşan bir Pareto eğrisi sunmak daha arzu edilir olacaktır. Buna göre, bu çalışmada, gerçek boyutlu serbest biçimli çelik çift katmanlı uzay kafeslerin optimizasyonu ve standardizasyonu için hesaplama açısından verimli çok aşamalı rehberli stokastik arama algoritması önerilmiştir. İlk optimizasyon aşamasında, algoritmaya ticari olarak mevcut çelik profillerin eksiksiz bir setinin sunulduğu ve sonraki aşamalarda, kullanılmayan veya daha az kullanılan kesitleri eleyerek kesit listesinin kademeli olarak küçültüldüğü, bir tasarım odaklı kesit eleme yaklaşımı izlenmiştir. Serbest biçimli çelik çift katmanlı uzay kafeslerin sıra dışı kolay olmayan test örnekleri üzerinde önerilen tekniğin kullanışlılığını göstermek için algoritmanın iki farklı versiyonu kullanılmış ve elde edilen Pareto eğrisi, minimum ağırlık ve çelik kesit çeşitleri arasındaki dengeyi göstermek için çizilmiştir.
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    Article
    Citation Count: 10
    Automated Selection of Optimal Material for Pressurized Multi-Layer Composite Tubes Based on an Evolutionary Approach
    (Springer London Ltd, 2018) Azad, Saeid Kazemzadeh; Akış, Tolga; Akis, Tolga; Azad, Saeıd Kazemzadeh; Civil Engineering; Department of Civil Engineering
    Decision making on the configuration of material layers as well as thickness of each layer in composite assemblies has long been recognized as an optimization problem. Today, on the one hand, abundance of industrial alloys with different material properties and costs facilitates fabrication of more economical or light weight assemblies. On the other hand, in the design stage, availability of different alternative materials apparently increases the complexity of the design optimization problem and arises the need for efficient optimization techniques. In the present study, the well-known big bang-big crunch optimization algorithm is reformulated for optimum design of internally pressurized tightly fitted multi-layer composite tubes with axially constrained ends. An automated material selection and thickness optimization approach is employed for both weight and cost minimization of one-, two-, and three-layer tubes, and the obtained results are compared. The numerical results indicate the efficiency of the proposed approach in practical optimum design of multi-layer composite tubes under internal pressure and quantify the optimality of different composite assemblies compared to one-layer tubes.
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    Article
    Citation Count: 40
    Enhanced Hybrid Metaheuristic Algorithms for Optimal Sizing of Steel Truss Structures With Numerous Discrete Variables
    (Springer, 2017) Azad, Saeid Kazemzadeh; Azad, Saeıd Kazemzadeh; Department of Civil Engineering
    The advent of modern computing technologies paved the way for development of numerous efficient structural design optimization tools in the recent decades. In the present study sizing optimization problem of steel truss structures having numerous discrete variables is tackled using combined forms of recently proposed metaheuristic techniques. Three guided, and three guided hybrid metaheuristic algorithms are developed by integrating a design oriented strategy to the stochastic search properties of three recently proposed metaheuristic optimization techniques, namely adaptive dimensional search, modified big bang-big crunch, and exponential big bang-big crunch algorithms. The performances of the proposed guided, and guided hybrid metaheuristic algorithms are compared to those of standard variants through optimum design of real-size steel truss structures with up to 728 design variables according to AISC-LRFD specification. The numerical results reveal that the hybrid form of adaptive dimensional search and exponential big bang-big crunch algorithm is the most promising algorithm amongst the other investigated techniques.
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    Article
    Citation Count: 2
    Structural Design Optimization of Multi-Layer Spherical Pressure Vessels: a Metaheuristic Approach
    (Springer, 2019) Akis, Tolga; Akış, Tolga; Azad, Saeid Kazemzadeh; Azad, Saeıd Kazemzadeh; Civil Engineering; Department of Civil Engineering
    This study addresses the optimum design problem of multi-layer spherical pressure vessels based on von Mises yield criterion. In order to compute the structural responses under internal pressure, analytical solutions for one-, two-, and three-layer spherical pressure vessels are provided. A population-based metaheuristic algorithm is reformulated for optimum material selection as well as thickness optimization of multi-layer spherical pressure vessels. Furthermore, in order to enhance the computational efficiency of the optimization algorithm, upper bound strategy is also integrated with the algorithm for reducing the total number of structural response evaluations during the optimization iterations. The performance of the algorithm is investigated through weight and cost minimization of one-, two- and three-layer spherical pressure vessels and the results are presented in detail. The obtained numerical results, based on different internal pressures as well as vessel sizes, indicate the usefulness and efficiency of the employed methodology in optimum design of multi-layer spherical pressure vessels.