{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:00:19Z","timestamp":1777705219518,"version":"3.51.4"},"reference-count":24,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,1,5]]},"abstract":"<jats:p>Multi-Pursuers Multi-Evader Game (MPMEG) is considered as a multi-agent complex problem in which the pursuers must perform the capture of the detected evaders according to the temporal constraints. In this paper, we propose a metaheuristic approach based on a Discrete Particle Swarm Optimization in order to allow a dynamic coalition formation of the pursuers during the pursuit game. A pursuit coalition can be considered as the role definition of each pursuer during the game. In this work, each possible coalition is represented by a feasible particle\u2019s position, which changes the concerned coalition according to its velocity during the pursuit game. With the aim of showcasing the performance of the new approach, we propose a comparison study in relation to recent approaches processing the MPMEG in term of capturing time and payoff acquisition. Moreover, we have studied the pursuit capturing time according to the number of used particles as well as the dynamism of the pursuit coalitions formed during the game. The obtained results note that the proposed approach outperforms the compared approaches in relation to the capturing time by only using eight particles. Moreover, this approach improves the pursuers\u2019 payoff acquisition, which represents the pursuers\u2019 learning rate during the task execution.<\/jats:p>","DOI":"10.3233\/jifs-221767","type":"journal-article","created":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T12:25:08Z","timestamp":1664540708000},"page":"757-773","source":"Crossref","is-referenced-by-count":2,"title":["A discrete particle swarm optimization coalition formation algorithm for multi-pursuer multi-evader game"],"prefix":"10.1177","volume":"44","author":[{"given":"Mohammed El Habib","family":"Souidi","sequence":"first","affiliation":[{"name":"Department of Computer Science, ICOSI Lab, University of Khenchela, Khenchela, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hichem","family":"Haouassi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, ICOSI Lab, University of Khenchela, Khenchela, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Makhlouf","family":"Ledmi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, ICOSI Lab, University of Khenchela, Khenchela, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toufik Messaoud","family":"Maarouk","sequence":"additional","affiliation":[{"name":"Department of Computer Science, ICOSI Lab, University of Khenchela, Khenchela, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdeldjalil","family":"Ledmi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, ICOSI Lab, University of Khenchela, Khenchela, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JIFS-221767_ref1","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s10846-018-0841-5","article-title":"Multiagent pursuit-evasion problem with the pursuers moving at uncertain speeds","volume":"95","author":"Yan","year":"2019","journal-title":"Journal of Intelligent & Robotic Systems"},{"issue":"1","key":"10.3233\/JIFS-221767_ref2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3356467","article-title":"Market clearing\u2013based dynamic multi-agent task allocation","volume":"11","author":"Nelke","year":"2020","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"10.3233\/JIFS-221767_ref3","doi-asserted-by":"crossref","unstructured":"Afzalov A. , He J. , Lotfi A. et al. Multi-Agent Path Planning Approach Using Assignment Strategy Variations in Pursuit of Moving Targets. In: Agents and Multi-Agent Systems: Technologies and Applications 2021. Springer, Singapore (2021), 451\u2013463.","DOI":"10.1007\/978-981-16-2994-5_38"},{"key":"10.3233\/JIFS-221767_ref4","doi-asserted-by":"crossref","unstructured":"Souidi M.E.H. , Maarouk T.M. , Ledmi. A. Multiagent Ludo Game Collaborative Path Planning based on Markov Decision Process. In: Inventive Systems and Control. Springer, Singapore (2021), 37\u201351.","DOI":"10.1007\/978-981-16-1395-1_4"},{"issue":"2","key":"10.3233\/JIFS-221767_ref7","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s10846-018-0963-9","article-title":"The multi-pursuer single-evader game","volume":"96","author":"Von Moll","year":"2019","journal-title":"Journal of Intelligent & Robotic Systems"},{"issue":"6","key":"10.3233\/JIFS-221767_ref9","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1007\/s10732-009-9121-7","article-title":"Coalition-based metaheuristic: a self-adaptive metaheuristic using reinforcement learning and mimetism","volume":"16","author":"Meignan","year":"2010","journal-title":"Journal of Heuristics"},{"issue":"1","key":"10.3233\/JIFS-221767_ref10","doi-asserted-by":"crossref","first-page":"157","DOI":"10.3390\/make1010010","article-title":"Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives","volume":"1","author":"Sengupta","year":"2019","journal-title":"Machine Learning and Knowledge Extraction"},{"issue":"11","key":"10.3233\/JIFS-221767_ref11","doi-asserted-by":"crossref","first-page":"131139","DOI":"10.5539\/mas.v10n11p131","article-title":"A survey on evolutionary computation: Methods and their applications in engineering","volume":"10","author":"Yar","year":"2016","journal-title":"Mod. Appl. Sci"},{"issue":"5","key":"10.3233\/JIFS-221767_ref12","doi-asserted-by":"crossref","first-page":"8091","DOI":"10.1007\/s11042-020-10139-6","article-title":"A review on genetic algorithm: past, present, and future","volume":"80","author":"Katoch","year":"2021","journal-title":"Multimedia Tools and Applications"},{"key":"10.3233\/JIFS-221767_ref13","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/B978-0-12-416743-8.00007-5","article-title":"Chapter 7-Particle Swarm Optimization","volume":"7","author":"Yang","year":"2014","journal-title":"Nature-Inspired Optimization Algorithms"},{"key":"10.3233\/JIFS-221767_ref15","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.future.2020.09.016","article-title":"A discrete PSO-based static load balancing algorithm for distributed simulations in a cloud environment","volume":"115","author":"Miao","year":"2021","journal-title":"Future Generation Computer Systems"},{"key":"10.3233\/JIFS-221767_ref18","doi-asserted-by":"crossref","first-page":"113292","DOI":"10.1016\/j.eswa.2020.113292","article-title":"A new hierarchical multi group particle swarm optimization with different task allocations inspired by holonic multi agent systems","volume":"149","author":"Roshanzamir","year":"2020","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"10.3233\/JIFS-221767_ref20","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1515\/jisys-2020-0042","article-title":"MAPSOFT: A Multi-Agent based Particle Swarm Optimization Framework for Travelling Salesman Problem","volume":"30","author":"Blamah","year":"2021","journal-title":"Journal of Intelligent Systems"},{"issue":"6","key":"10.3233\/JIFS-221767_ref21","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1080\/0952813X.2015.1056241","article-title":"Multi-agent cooperation pursuit based on an extension of AALAADIN organisational model","volume":"28","author":"Souidi","year":"2016","journal-title":"Journal of Experimental Theoretical & Artificial Intelligence"},{"issue":"1","key":"10.3233\/JIFS-221767_ref22","first-page":"1","article-title":"Multi-agent pursuit-evasion game based on organizational architecture","volume":"27","author":"Souidi","year":"2019","journal-title":"Journal of computing and information technology"},{"issue":"3","key":"10.3233\/JIFS-221767_ref26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.14257\/ijhit.2015.8.3.01","article-title":"Coalition formation in multi-agent systems based on improved particle swarm optimization algorithm","volume":"8","author":"Xu","year":"2015","journal-title":"International Journal of Hybrid Information Technology"},{"key":"10.3233\/JIFS-221767_ref27","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.ifacol.2016.09.054","article-title":"PSO-based Optimal taskallocation for cooperative timing missions","volume":"49","author":"Oh","year":"2016","journal-title":"IFAC-Papers On Line"},{"key":"10.3233\/JIFS-221767_ref28","doi-asserted-by":"crossref","first-page":"106603","DOI":"10.1016\/j.asoc.2020.106603","article-title":"Multi-task allocation with an optimized quantum particle swarm method","volume":"96","author":"Li","year":"2020","journal-title":"Applied Soft Computing"},{"issue":"2","key":"10.3233\/JIFS-221767_ref29","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s10489-014-0589-y","article-title":"Using binary particle swarm optimization to search for maximal successful coalition","volume":"42","author":"Zhang","year":"2015","journal-title":"Applied Intelligence"},{"key":"10.3233\/JIFS-221767_ref30","doi-asserted-by":"crossref","first-page":"100686","DOI":"10.1016\/j.swevo.2020.100686","article-title":"Multi-agent coalition formation by an efficient genetic algorithm with heuristic initialization and repair strategy","volume":"55","author":"Guo","year":"2020","journal-title":"Swarm and Evolutionary Computation"},{"key":"10.3233\/JIFS-221767_ref31","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.adhoc.2018.11.008","article-title":"Use of a quantum genetic algorithm for coalition formation in large-scale UAV networks","volume":"87","author":"Mousavi","year":"2019","journal-title":"Ad Hoc Networks"},{"issue":"11","key":"10.3233\/JIFS-221767_ref34","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.3390\/e23111433","article-title":"An Improved Approach towards Multi-Agent Pursuit\u2013Evasion Game Decision-Making Using Deep Reinforcement Learning","volume":"23","author":"Wan","year":"2021","journal-title":"Entropy"},{"issue":"2","key":"10.3233\/JIFS-221767_ref35","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1002\/rnc.1193","article-title":"Stochastic multi-player pursuit\u2013evasion differential games","volume":"18","author":"Li","year":"2008","journal-title":"International Journal of Robust and Nonlinear Control: IFAC-Affiliated Journal"},{"key":"10.3233\/JIFS-221767_ref41","doi-asserted-by":"crossref","first-page":"100801","DOI":"10.1016\/j.softx.2021.100801","article-title":"NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces","volume":"16","author":"Gunaratne","year":"2021","journal-title":"SoftwareX"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-221767","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:43:14Z","timestamp":1777455794000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-221767"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,5]]},"references-count":24,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/jifs-221767","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,5]]}}}