{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:54:55Z","timestamp":1781110495432,"version":"3.54.1"},"reference-count":28,"publisher":"IGI Global Scientific Publishing","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,4,1]]},"abstract":"<p>This paper addresses an allocation problem and proposes a solution using a swarm intelligence method. The application of swarm intelligence has to be discrete. This allocation problem can be modelled as a multi-objective optimization problem where the authors minimize the time and the distance of the total travel in a logistic context. This study uses a hybrid Discrete Particle Swarm Optimization (DPSO) method combined to Evolutionary Game Theory (EGT). One of the main implementation issues of DPSO is the choice of inertial, individual, and social coefficients. In order to resolve this problem, those coefficients are optimised by using a dynamical approach based on EGT. The strategies are either to keep going with only inertia, only with individual, or only with social coefficients. Since the optimal strategy is usually a mixture of the three, the fitness of the swarm can be maximized when an optimal rate for each coefficient is obtained. Evolutionary game theory studies the behaviour of large populations of agents who repeatedly engage in strategic interactions. Changes in behaviour in these populations are driven by natural selection via differences in birth and death rates. To test this algorithm, the authors create a problem whose solution is already known. This study checks whether this adapted DPSO method succeeds in providing an optimal solution for general allocation problems.<\/p>","DOI":"10.4018\/jsir.2012040102","type":"journal-article","created":{"date-parts":[[2012,8,15]],"date-time":"2012-08-15T16:00:49Z","timestamp":1345046449000},"page":"20-38","source":"Crossref","is-referenced-by-count":19,"title":["A Swarm Intelligence Method Combined to Evolutionary Game Theory Applied to the Resources Allocation Problem"],"prefix":"10.4018","volume":"3","author":[{"given":"C\u00e9dric","family":"Leboucher","sequence":"first","affiliation":[{"name":"MBDA Missile Systems-France, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachid","family":"Chelouah","sequence":"additional","affiliation":[{"name":"\u00c9cole Internationale des Sciences du Traitement de l\u2019Information (EISTI), France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick","family":"Siarry","sequence":"additional","affiliation":[{"name":"University Paris-Est Cr\u00e9teil (UPEC), France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"St\u00e9phane","family":"Le M\u00e9nec","sequence":"additional","affiliation":[{"name":"MBDA Missile Systems\u00a0-France, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"jsir.2012040102-0","unstructured":"Al-kazemy, B., & Mohan, C. K. (2000). Multi-phase discrete particle swarm optimization. In Proceedings of the Fourth International Workshop on Frontiers in Evolutionary."},{"key":"jsir.2012040102-1","doi-asserted-by":"crossref","unstructured":"Badamchizadeh, M., & Madani, K. (2010). Applying modified discrete particle swarm optimization algorithm and genetic algorithm for system identification. In Proceedings of the International Conference on Computer and Automation Engineering (Vol. 5, pp. 354-358).","DOI":"10.1109\/ICCAE.2010.5451412"},{"key":"jsir.2012040102-2","doi-asserted-by":"crossref","unstructured":"Chio, C. D., Chio, P. D., & Giacobini, M. (2008). An evolutionary game-theoretical approach to particle swarm optimisation. In Proceedings of the Conference on Applications of Evolutionary Computing (pp. 575-584).","DOI":"10.1007\/978-3-540-78761-7_63"},{"key":"jsir.2012040102-3","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/978-3-540-39930-8_8","article-title":"Discrete particle swarm optimization, illustrated by the travelling salesman problem.","volume":"1","author":"M.Clerc","year":"2004","journal-title":"New Optimization Techniques in Engineering"},{"key":"jsir.2012040102-4","doi-asserted-by":"crossref","unstructured":"Clerc, M., & Siarry, P. (2004). Une nouvelle m\u00e9taheuristique pour l'optimisation difficile: la m\u00e9thode des essaims particulaires. Journal sur l'enseignement des sciences et technologies de l'information et des syst\u00e8mes, 3-7, 1-13.","DOI":"10.1051\/bib-j3ea:2004007"},{"key":"jsir.2012040102-5","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/2884.001.0001","author":"R.Cressman","year":"2003","journal-title":"Evolutionary dynamics and extensive form games"},{"key":"jsir.2012040102-6","doi-asserted-by":"crossref","unstructured":"Eberhart, R., & Shi, Y. (1998). Comparison between genetic algorithms and particle swarm optimization. In Proceedings of the 7th International Conference on Evolutionary Programming (pp. 611-616).","DOI":"10.1007\/BFb0040812"},{"key":"jsir.2012040102-7","doi-asserted-by":"crossref","unstructured":"Ho, T.-F., Jian, S. J., & Lin, Y.-L. W. (2010). Discrete particle swarm optimization for materials budget allocation in academic libraries. In Proceedings of the International Conference on Computational Science and Engineering (pp. 196-203).","DOI":"10.1109\/CSE.2010.33"},{"key":"jsir.2012040102-8","doi-asserted-by":"crossref","unstructured":"Hu, W., Song, J., & Li, W. (2008). A new PSO scheduling simulation algorithm based on an intelligent compensation particle position rounding off. In Proceedings of the Fourth International Conference on Natural Computation (Vol. 1, pp. 145-149).","DOI":"10.1109\/ICNC.2008.605"},{"key":"jsir.2012040102-9","first-page":"1","article-title":"Air traffic flow management with heuristic repair.","volume":"0","author":"U.Junker","year":"2004","journal-title":"The Knowledge Engineering Review"},{"key":"jsir.2012040102-10","doi-asserted-by":"publisher","DOI":"10.1109\/ETFA.2006.355360"},{"key":"jsir.2012040102-11","doi-asserted-by":"crossref","unstructured":"Karimanzira, D., & Jacobi, M. (2008). A feasible and adaptive water-usage prediction and allocation based on a machine learning method. In Proceedings of the Tenth International Conference on Computer Modeling and Simulation (pp. 324-329).","DOI":"10.1109\/UKSIM.2008.55"},{"key":"jsir.2012040102-12","doi-asserted-by":"crossref","unstructured":"Kennedy, J., & Eberhart, R. (1997). A discrete binary version of the particle swarm algorithm. In Proceedings of the International Conference on Systems, Man, and Cybernetics (Vol. 5, pp. 4104-4108).","DOI":"10.1109\/ICSMC.1997.637339"},{"key":"jsir.2012040102-13","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2005.11.017"},{"key":"jsir.2012040102-14","unstructured":"Liu, P., Wang, L., Ding, X., & Gao, X. (2010). Scheduling of dispatching ready mixed concrete trucks through discrete particle swarm optimization. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (pp. 4086-4090)."},{"key":"jsir.2012040102-15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cam.2007.01.028"},{"key":"jsir.2012040102-16","doi-asserted-by":"crossref","unstructured":"Lu, B., & Ma, J. (2009). A strategy for resource allocation and pricing in grid environment based on economic model. In Proceedings of the International Conference on Computational Intelligence and Natural Computing (Vol. 1, pp. 221-224).","DOI":"10.1109\/CINC.2009.135"},{"key":"jsir.2012040102-17","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511806292"},{"key":"jsir.2012040102-18","doi-asserted-by":"crossref","unstructured":"Miranda, V., & Fonseca, N. (2002). EPSO - Evolutionary particle swarm optimization, a new algorithm with application in power systems. In Proceedings of the Transmission and Distribution Conference and Exhibition (Vol. 2, pp. 745-750).","DOI":"10.1109\/TDC.2002.1177567"},{"key":"jsir.2012040102-19","author":"W.Sandholm","year":"2010","journal-title":"Population games and evolutionary dynamics"},{"key":"jsir.2012040102-20","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.06.041"},{"key":"jsir.2012040102-21","unstructured":"Shaheen, F., & Bader-El-Den, M. B. (2010). Co-evolutionary hyper-heuristic method for auction based scheduling. In Proceedings of the IEEE Congress on Congress on Evolutionary Computation (pp. 1-8)."},{"key":"jsir.2012040102-22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipl.2007.03.010"},{"key":"jsir.2012040102-23","doi-asserted-by":"publisher","DOI":"10.1016\/0025-5564(78)90077-9"},{"issue":"3","key":"jsir.2012040102-24","first-page":"383","article-title":"Optimization of resource allocation in hard-real-time environment.","volume":"43","author":"V. V.Toporkov","year":"2004","journal-title":"Journal of Computer and System Sciences"},{"key":"jsir.2012040102-25","doi-asserted-by":"crossref","unstructured":"Wang, S., & Meng, B. (2007). Chaos particle swarm optimization for resource allocation problem. In Proceedings of the IEEE International Conference on Automation and Logistics (pp. 464-467).","DOI":"10.1109\/ICAL.2007.4338608"},{"key":"jsir.2012040102-26","doi-asserted-by":"crossref","unstructured":"Yare, Y., & Venayagamoorthy, G. K. (2007). Optimal scheduling of generator maintenance using modified discrete particle swarm optimization. In Proceedings of the iREP Symposium on Bulk Power System Dynamics and Control - VII. Revitalizing Operational Reliability (Vol. 7, pp. 1-7).","DOI":"10.1109\/IREP.2007.4410521"},{"key":"jsir.2012040102-27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2003.12.001"}],"container-title":["International Journal of Swarm Intelligence Research"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=69775","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T15:57:11Z","timestamp":1654099031000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/jsir.2012040102"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2012,4,1]]},"references-count":28,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2012,4]]}},"URL":"https:\/\/doi.org\/10.4018\/jsir.2012040102","relation":{},"ISSN":["1947-9263","1947-9271"],"issn-type":[{"value":"1947-9263","type":"print"},{"value":"1947-9271","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,4,1]]}}}