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  <doi_batch_id>aspg-41-3490-1792223526</doi_batch_id>
  <timestamp>20261017075206</timestamp>
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  <journal>
   <journal_metadata language="en">
    <full_title>Metaheuristic Optimization Review</full_title>
    <abbrev_title>MOR</abbrev_title>
    <issn media_type="electronic">3066-280X</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <journal_volume>
     <volume>3</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Metaheuristic Algorithms in Optimizing Structural Design of Bridges: A Review</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Sekar Kidambi</given_name>
      <surname>Raju</surname>
      <affiliations>
       <institution>
        <institution_name>School of Computing, SASTRA Deemed University, Thanjavur 613401, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Metaheuristic optimization algorithms become essential to solving structural design problems because they can handle nonlinear, multiple-mode, large-scale, and other difficulties. This review focuses on how MOAs have been developed and utilized and how they have compared efficiency in structural engineering design optimization. It describes some of the main milestones, such as hybrid and ensemble algorithms, as well as quantum annealing and finite elements, to improve the accuracy of the results. The study organizes and assesses modern approaches scientifically and accentuates their benefits and pitfalls in practical applications. Hypotheses derived from benchmarking and statistical exercises show that enhanced MOAs are reliable and fast in yielding almost ideal structures within a manageable computational frontier. Finally, the review outlines the limitations of the current research and suggests research foci for the future advancement of metaheuristic methods and their use in structural engineering optimization.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <pages>
     <first_page>06</first_page>
     <last_page>10</last_page>
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     <item_number item_number_type="article-number">3490</item_number>
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     <doi>10.54216/MOR.030202</doi>
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