• English
    • العربية
  • العربية
  • Login
  • QU
  • QU Library
  •  Home
  • Communities & Collections
  • Help
    • Item Submission
    • Publisher policies
    • User guides
    • FAQs
  • About QSpace
    • Vision & Mission
View Item 
  •   Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Civil and Environmental Engineering
  • View Item
  • Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Civil and Environmental Engineering
  • View Item
  •      
  •  
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    The Mosaic of Metaheuristic Algorithms in Structural Optimization

    View/Open
    s11831-022-09773-0.pdf (3.261Mb)
    Date
    2022
    Author
    Lagaros, Nikos D.
    Plevris, Vagelis
    Kallioras, Nikos Ath.
    Metadata
    Show full item record
    Abstract
    Metaheuristic optimization algorithms (MOAs) represent powerful tools for dealing with multi-modal nonlinear optimization problems. The considerable attention that MOAs have received over the last decade and especially when adopted for dealing with several types of structural optimization problems can be mainly credited to the advances achieved in computer science and computer technology rendering possible, among others, the solution of real-world structural design optimization cases in reasonable computational time. The primal scope of the study is to present a state-of-the-art review of past and current developments achieved so far in structural optimization problems dealt with MOAs, accompanied by a set of tests aiming to examine the efficiency of various MOAs in several benchmark structural optimization problems. For this purpose, 24 population-based state-of-the-art MOAs belonging in four classes, (i) swarm-based; (ii) physics-based; (iii) evolutionary-based; and (iv) human-based, are used for solving 11 single objective benchmark structural optimization test problems of different levels of complexity. The size of the problems employed varies, with the number of unknowns ranging from 3 to 328 and the number of constraint functions ranging from 2 to 264, related to the structural performance of the design with reference to deformation and stress limits.
    DOI/handle
    http://dx.doi.org/10.1007/s11831-022-09773-0
    http://hdl.handle.net/10576/59681
    Collections
    • Civil and Environmental Engineering [‎867‎ items ]

    entitlement

    Related items

    Showing items related by title, author, creator and subject.

    • Thumbnail

      An optimized algorithm for optimal power flow based on deep learning 

      Qinggang, Su; Khan, Habib Ullah; Khan, Imran; Choi, Bong Jun; Wu, Falin; Aly, Ayman A.... more authors ... less authors ( Elsevier , 2021 , Article)
      With the increasing requirements for power system transient stability assessment, the research on power system transient stability assessment theory and methods requires not only qualitative conclusions about system transient ...
    • Thumbnail

      Identifying Optimal Design of Office Buildings Using Harmony Search Optimization Algorithm 

      Asadi, Somayeh; Mostavi, Ehsan; Boussaa, Djamel ( Hamad bin Khalifa University Press (HBKU Press) , 2016 , Conference)
      Energy is an expensive and scare resource and the world faces an energy crisis given our dependence on the limited supply of fossil fuels. Similar to other countries, in Qatar, energy consumption and the subsequent production ...
    • Thumbnail

      Looking Ahead: Restructuring Chemical Engineering Undergraduate Curriculum for Optimal Impact of Process Simulation: Student Benefits in Optimization and Sustainability 

      Al-Sobhi, Saad; Al-Muhtaseb, Shaheen; Elfaki, Hesan ( American Society for Engineering Education , 2021 , Article)
      Proper chemical process simulation knowledge is valuable for senior chemical engineering students to deliver optimal design outcomes and achieve many learning objectives. This study aims to highlight the benefits of ...

    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Home

    Submit your QU affiliated work

    Browse

    All of Digital Hub
      Communities & Collections Publication Date Author Title Subject Type Language Publisher
    This Collection
      Publication Date Author Title Subject Type Language Publisher

    My Account

    Login

    Statistics

    View Usage Statistics

    About QSpace

    Vision & Mission

    Help

    Item Submission Publisher policiesUser guides FAQs

    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Video