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  <doi_batch_id>aspg-28-4514-1792155044</doi_batch_id>
  <timestamp>20261016125044</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
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  <journal>
   <journal_metadata language="en">
    <full_title>Journal of Artificial Intelligence and Metaheuristics</full_title>
    <abbrev_title>JAIM</abbrev_title>
    <issn media_type="electronic">2833-5597</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2026</year>
    </publication_date>
    <journal_volume>
     <volume>11</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Hybrid Grey Wolf and Al-Biruni Earth Radius Optimization for Sustainable Electric Vehicle Routing</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Mostafa</given_name>
      <surname>Abotaleb</surname>
      <affiliations>
       <institution>
        <institution_name>Engineering School of Digital Technologies, Yugra State University, Khanty-Mansiysk, Russia</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>In this chapter, a novel hybrid optimization algorithm, which integrates the Grey Wolf Optimizer (GWO) with the Al-Biruni Earth Radius (BER) method to address the problem of electric vehicle (EV) path planning, is introduced. By incorporating the simulation of wolf social hierarchies for efficient exploration alongside the spatial analysis capabilities of BER, the proposed approach effectively determines optimal, energy-efficient routes across diverse terrains. The hybrid approach systematically considers key factors such as distance, terrain characteristics, and energy consumption to guide vehicle movements, thereby minimizing energy usage, enhancing travel efficiency, and ensuring smooth traffic flow. Experimental results demonstrate the effectiveness of the proposed hybrid method compared to conventional metaheuristic approaches, achieving an average error of 0.530218. This chapter presents the technical framework of this hybrid approach, its applications in EV route optimization, and its broader implications for sustainable mobility and real-world transportation challenges.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2026</year>
    </publication_date>
    <pages>
     <first_page>149</first_page>
     <last_page>157</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">4514</item_number>
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     <doi>10.54216/JAIM.110209</doi>
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