{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T12:23:08Z","timestamp":1753878188454,"version":"3.41.2"},"reference-count":21,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100007446","name":"King Khalid University","doi-asserted-by":"publisher","award":["RGP2\/18\/43"],"award-info":[{"award-number":["RGP2\/18\/43"]}],"id":[{"id":"10.13039\/501100007446","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004242","name":"Princess Nourah Bint Abdulrahman University","doi-asserted-by":"publisher","award":["PNURSP2022R77"],"award-info":[{"award-number":["PNURSP2022R77"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006701","name":"Umm Al-Qura University","doi-asserted-by":"publisher","award":["22UQU4340237DSR23"],"award-info":[{"award-number":["22UQU4340237DSR23"]}],"id":[{"id":"10.13039\/501100006701","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Multi\u2010Agent System (MAS) gained significant interest amongst researchers since it provides multiple benefits through several application areas. MAS involves a network of socially\u2010cooperative smart agents that is conscious about the drastic modifications that occur in the platform at the time of task execution. On the other hand, energy efficiency is a major issue in real\u2010time IoT systems, since most of the sensor nodes experience energy constraints. Though several works have been conducted earlier, there is a need exists to design an effective solution for simultaneous processing in real\u2010time environments using multiple agents. The aim of Multi\u2010Agent Pathfinding (MAPF) process is to provide collision\u2010free routes so as to divert the agents from original path to the destination. In this view, the current study designs a Quasi\u2010Oppositional Wild Horse Optimization\u2010based Multi\u2010Agent Path Finding (QOWHO\u2010MAPF) scheme for real\u2010time IoT systems. The aim of the proposed QOWHO\u2010MAPF scheme is to determine the optimal set of paths to reach the destination in real\u2010time IoT networks. QOWHO algorithm is created by integrating the concepts of Quasi\u2010Oppositional Based Learning (QOBL) and conventional WHO algorithm. In addition, the proposed QOWHO\u2010MAPF model derives a fitness function that involves two input parameters such as residual energy and distance\u2010to\u2010destination. The proposed QOWHO\u2010MAPF model was experimentally analysed and the results were inspected under several aspects. The simulation results established that QOWHO\u2010MAPF model is a superior model compared to other state\u2010of\u2010the\u2010art models.<\/jats:p>","DOI":"10.1111\/exsy.13112","type":"journal-article","created":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T05:45:10Z","timestamp":1659419110000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Quasi\u2010oppositional wild horse optimization based multi\u2010agent path finding scheme for real time IoT systems"],"prefix":"10.1111","volume":"39","author":[{"given":"Radwa","family":"Marzouk","sequence":"first","affiliation":[{"name":"Department of Information Systems, College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University  Riyadh Saudi Arabia"}]},{"given":"Jaber S.","family":"Alzahrani","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, College of Engineering at Alqunfudah Umm Al\u2010Qura University  Mecca Saudi Arabia"}]},{"given":"Fadwa","family":"Alrowais","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University  Riyadh Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4389-4927","authenticated-orcid":false,"given":"Fahd N.","family":"Al\u2010Wesabi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Science &amp; Art at Mahayil King Khalid University  Abha Saudi Arabia"}]},{"given":"Manar Ahmed","family":"Hamza","sequence":"additional","affiliation":[{"name":"Department of Computer and Self Development, Preparatory Year Deanship Prince Sattam Bin Abdulaziz University  AlKharj Saudi Arabia"}]}],"member":"311","published-online":{"date-parts":[[2022,8]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2017.10.019"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10458-020-09478-3"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEM.2019.2918162"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-817636-8.00014-4"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.2478\/cait-2021-0042"},{"key":"e_1_2_10_7_1","doi-asserted-by":"crossref","unstructured":"Lam E. Le Bodic P. Harabor D. D. &Stuckey P. J.(2019).Branch\u2010and\u2010cut\u2010and\u2010price for multi\u2010agent pathfinding. InIJCAI(pp. 1289\u20131296).","DOI":"10.24963\/ijcai.2019\/179"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103574"},{"key":"e_1_2_10_9_1","doi-asserted-by":"crossref","unstructured":"Li J. Harabor D. Stuckey P. J. Ma H. &Koenig S.(2019).Symmetry\u2010breaking constraints for grid\u2010based multi\u2010agent path finding. InProceedings of the AAAI conference on artificial intelligence(Vol. 33 No. 01 pp. 6087\u20136095).","DOI":"10.1609\/aaai.v33i01.33016087"},{"key":"e_1_2_10_10_1","doi-asserted-by":"crossref","unstructured":"Li J. Surynek P. Felner A. Ma H. Kumar T. S. &Koenig S.(2019).Multi\u2010agent path finding for large agents. 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