{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:28:25Z","timestamp":1775143705708,"version":"3.50.1"},"reference-count":70,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2020,12,31]],"date-time":"2020-12-31T00:00:00Z","timestamp":1609372800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This paper proposes a Multi-Agent based Particle Swarm Optimization (PSO) Framework for the Traveling salesman problem (MAPSOFT). The framework is a deployment of the recently proposed intelligent multi-agent based PSO model by the authors. MAPSOFT is made up of groups of agents that interact with one another in a coordinated search effort within their environment and the solution space. A discrete version of the original multi-agent model is presented and applied to the Travelling Salesman Problem. Based on the simulation results obtained, it was observed that agents retrospectively decide on their next moves based on consistent better fitness values obtained from present and prospective neighborhoods, and by reflecting back to previous behaviors and sticking to historically better results. These overall attributes help enhance the conventional PSO by providing more intelligence and autonomy within the swarm and thus contributed to the emergence of good results for the studied problem.<\/jats:p>","DOI":"10.1515\/jisys-2020-0042","type":"journal-article","created":{"date-parts":[[2021,1,14]],"date-time":"2021-01-14T03:06:04Z","timestamp":1610593564000},"page":"413-428","source":"Crossref","is-referenced-by-count":5,"title":["MAPSOFT: A Multi-Agent based Particle Swarm Optimization Framework for Travelling Salesman Problem"],"prefix":"10.1515","volume":"30","author":[{"given":"Nachamada Vachaku","family":"Blamah","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Jos , Jos , Nigeria"},{"name":"School of Computer Science, University of KwaZulu-Natal , Durban , South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aderemi Adewumi","family":"Oluyinka","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of KwaZulu-Natal , Durban , South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gregory","family":"Wajiga","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Moddibo Adama University of Technology , Yola , Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yusuf Benson","family":"Baha","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Moddibo Adama University of Technology , Yola , Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2020,12,31]]},"reference":[{"key":"2025120523322316178_j_jisys-2020-0042_ref_001","unstructured":"Ahmad, R., Lee, Y., Rahimi, S., and Gupta, B. (2007). A Multi-Agent Based Approach for Particle Swarm Optimization, In IEEE Publications 1-4244-0945-4\/07, Paper 17."},{"key":"2025120523322316178_j_jisys-2020-0042_ref_002","unstructured":"Bell, W.J., Roth, L., and Nalepa, C. (2007). Cockroaches: Ecology, Behavior, and Natural History The Johns Hopkins University Press."},{"key":"2025120523322316178_j_jisys-2020-0042_ref_003","doi-asserted-by":"crossref","unstructured":"Biswas, S., Anavatti, S. G., and Garratt, M. A. (2017). Particle Swarm Optimization Based Co-operative Task Assignment and Path Planning for Multi-Agent System. IEEE, 978-1-5386-2726-6\/17","DOI":"10.1109\/SSCI.2017.8280872"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_004","doi-asserted-by":"crossref","unstructured":"Blamah, N. V. and Adewumi, A. O. (2012). A multi-agent-based model for distributed system processing, International Journal of the Physical Sciences 7(34), pp. 5297-5303.","DOI":"10.5897\/IJPS12.246"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_005","doi-asserted-by":"crossref","unstructured":"Blamah, N. V., Adewumi, A.O. and Olusanya,M.O. (2013). A Secured Agent-Based Framework for Data Warehouse Management. Proceedings of IEEE International Conference on Industrial Technology (ICIT) pp1840-1845, Cape Town.","DOI":"10.1109\/ICIT.2013.6505956"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_006","unstructured":"Blamah, N. V., Adewumi, A. O., Wajiga, G. M. and Baha, B. Y. (2013). An Intelligent Particle Swarm Optimization Model based on Multi-Agent System, IEEE, The African Journal of Computing and ICTs, 6(2), pp1-8. www.ajocict.net"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_007","doi-asserted-by":"crossref","unstructured":"Blamah, N. V. (2013). A Reflective Swarm Intelligence Algorithm, Journal Of Computer Engineering, 14(4), pp. 44-48","DOI":"10.9790\/0661-1444448"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_008","doi-asserted-by":"crossref","unstructured":"Bratman, M.E. (1990). What is Intention? In Cohen, P.R., Morgan, J.L., and Pollack, M.E., editors, Intentions in Communication, The MIT Press: Cambridge, MA.","DOI":"10.7551\/mitpress\/3839.003.0004"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_009","unstructured":"Brezina, I and \u010ci\u010dkov\u00e1, Z. (2011). Solving the Travelling Salesman Problem Using the Ant Colony Optimization. Management Information Systems, 6 (4), pp. 010-014"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_010","unstructured":"Brownlee, J. (2011). Clever Algorithms: Nature-Inspired Programming Recipes 1st ed., Lulu. http:\/\/www.CleverAlgorithms.com"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_011","doi-asserted-by":"crossref","unstructured":"Cakir, M. and Yilmaz, G. (2015). Traveling salesman problem optimization with parallel genetic algorithm, IEEE 23nd Signal Processing and Communications Applications Conference (SIU) Malatya 2015.","DOI":"10.1109\/SIU.2015.7130406"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_012","doi-asserted-by":"crossref","unstructured":"Confort, M and Meng, Y. (2008). Reinforcing Learning for Neural Networks using Swarm Intelligence, IEEE Swarm Intelligence Symposium St. Louis MO, USA, Sept., 21-23, 2008.","DOI":"10.1109\/SIS.2008.4668289"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_013","doi-asserted-by":"crossref","unstructured":"Davendra, D. (2010). \u201cTraveling Salesman Problem, Theory and Applications,\u201d InTech, ISBN 978-953-307-426-9.","DOI":"10.5772\/547"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_014","doi-asserted-by":"crossref","unstructured":"Davidson, H. (1992). Alfarabi, Avicenna, and Averroes, on Intellect Oxford University Press,","DOI":"10.1093\/oso\/9780195074239.001.0001"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_015","doi-asserted-by":"crossref","unstructured":"Deng, Y., Liu, Y. and Zhou, D. (2015). An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP, Mathematical Problems in Engineering 2015, Hindawi Publishing Corporation.","DOI":"10.1155\/2015\/212794"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_016","doi-asserted-by":"crossref","unstructured":"Dorigo, M. and Gambardella, L. (1997). \u201cAnt Colonies for the Travelling Salesman Problem,\u201d Bio Systems, Vol. 43, pp. 73-81.","DOI":"10.1016\/S0303-2647(97)01708-5"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_017","doi-asserted-by":"crossref","unstructured":"Gao, D., Li, X. and Chen, H. (2019). Application of Improved Particle Swarm Optimization in Vehicle Crashworthiness, Hindawi, Mathematical Problems in Engineering Volume 2019.","DOI":"10.1155\/2019\/8164609"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_018","doi-asserted-by":"crossref","unstructured":"Groba, C., Sartal, A. and Vazquez, X (2015). Solving the Dynamic Traveling Salesman Problem using a Genetic Algorithm with Trajectory Prediction. Computers and Operations Research 56(C): 22-32, Elsevier Science.","DOI":"10.1016\/j.cor.2014.10.012"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_019","doi-asserted-by":"crossref","unstructured":"Gulcu, S. D. and Ornek, H. K. (2019). Solution of Multiple Travelling Salesman Problem using Particle Swarm Optimization based Algorithms, International Journal of Intelligent Systems and Applications in Engineering, 7(2), 72-82.","DOI":"10.18201\/ijisae.2019252784"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_020","unstructured":"Gupta, P., Sharma, K., and Singh, P. (2012). A Review of Object Tracking using Particle Swarm Optimizatio, VSRD, International Journal of Electrical Electronics & Comm. Engg. 2(7)."},{"key":"2025120523322316178_j_jisys-2020-0042_ref_021","doi-asserted-by":"crossref","unstructured":"Halloy, J., Sempo, G., Caprari, G., Rivault, C., Asadpour, M., Tache, F., Said, I., Durier, V., Canonge, S., Am\u00e9, J.M., Detrain, C., Correll, N., Martinoli, A., Mondada, F., Siegwart, R., and Deneubourg, J.L. (2007). Social integration of robots into groups of cockroaches to control self-organized choices, Science November, 318(5853), pp.1155\u20131158.","DOI":"10.1126\/science.1144259"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_022","unstructured":"Hjertenes, M. O. (2002). \u201cA Multilevel Scheme for the Travelling Salesman Problem,\u201d University of Bergen."},{"key":"2025120523322316178_j_jisys-2020-0042_ref_023","unstructured":"Hlaing, Z. C. S. S. and Khine, M. A. (2011) An Ant Colony Optimization Algorithm for Solving Travelling Salesman Problem. International Conference on Information Communication and Management IPCSIT vol.16, IACSIT Press, Singapore."},{"key":"2025120523322316178_j_jisys-2020-0042_ref_024","doi-asserted-by":"crossref","unstructured":"Houssein, E. H. (2017). Particle Swarm Optimization-Enhanced Twin Support Vector Regression for Wind Speed Forecasting. J. Intell. Syst; https:\/\/doi.org\/10.1515\/jisys-2017-0378 pp 1-10","DOI":"10.1515\/jisys-2017-0378"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_025","doi-asserted-by":"crossref","unstructured":"Huang, M., Zhao, Z. and Liang, X. (2019). Application of Improved Particle Swarm Optimization in Vehicle Depot Overhaul Shop Scheduling, 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), Dalian, China, pp. 103-106.","DOI":"10.1109\/ICCSNT47585.2019.8962463"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_026","doi-asserted-by":"crossref","unstructured":"Jana, G., Mitra, A., Pan, S., Sural, S. and Chattaraj, P.K. (2019). Modified Particle Swarm Optimization Algorithms for the Generation of Stable Structures of Carbon Clusters, Cn (n = 3\u20136, 10).","DOI":"10.3389\/fchem.2019.00485"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_027","doi-asserted-by":"crossref","unstructured":"Jiang, M, Luo, Y. P. and Yang, S. Y. (2007).Particle Swarm Optimization \u2013 Stochastic Trajectory Analysis and Parameter Selection. In Swarm Intelligence: Focus on Ant and Particle Swarm Optimization I-Tech Education and Publishing, Vienna, Austria.","DOI":"10.5772\/5104"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_028","doi-asserted-by":"crossref","unstructured":"Kefi, S., Rokbani, N., Kromer, P. and Alimi, A. M. (2016). A New Ant Supervised-PSO Variant Applied to Traveling Salesman Problem, Hybrid Intelligent Systems Advances in Intelligent Systems and Computing, 420, Springer Int. Pub.","DOI":"10.1007\/978-3-319-27221-4_8"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_029","doi-asserted-by":"crossref","unstructured":"Kennedy, J. (1999). Small worlds and mega-minds: Effects of Neighborhood Topology on Particle Swarm Performance. In Proceedings of the 1999 Congress on Evolutionary Computation.","DOI":"10.1109\/CEC.1999.785509"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_030","unstructured":"Kennedy, J., and Eberhart, R. (1995). Particle Swarm Optimisation. In: Proceedings of the IEEE Conf. on Neural Networks, Perth."},{"key":"2025120523322316178_j_jisys-2020-0042_ref_031","doi-asserted-by":"crossref","unstructured":"Kompridis, N. (2000). So We Need Something Else for Reason to Mean, International Journal of Philosophical Studies 8(3), pp. 271-295.","DOI":"10.1080\/096725500750039282"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_032","doi-asserted-by":"crossref","unstructured":"Lee, K. C., Lee, N. and Li, H. (2009). A particle swarm optimization-driven cognitive map approach to analyzing information systems project risk, Journal of the American Society for Information Science and Technology, 60(6), 1208-1221","DOI":"10.1002\/asi.21019"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_033","doi-asserted-by":"crossref","unstructured":"Lin, S. (1965). \u201cComputer Solutions of the Traveling Salesman Problem,\u201d Bell System Technical Journal, Vol. 44, pp. 2245-2269.","DOI":"10.1002\/j.1538-7305.1965.tb04146.x"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_034","unstructured":"Lorion, Y., Bogon, T., Timm, I.J., and Drobnik, O. (2009). An Agent Based Parallel Particle Swarm Optimization \u2013 APPSO, In IEEE Publications 978-1-4244-2762-8\/09"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_035","doi-asserted-by":"crossref","unstructured":"Matai, R., Mittal, M. L., and Singh, S. (2010). Traveling salesman problem: An overview of applications, formulations, and solution approaches In Tech Open Access Publisher.","DOI":"10.5772\/12909"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_036","doi-asserted-by":"crossref","unstructured":"Mavrovouniotis, M., Muller, F. P. and Yang, S. (2016). Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics","DOI":"10.1109\/TCYB.2016.2556742"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_037","doi-asserted-by":"crossref","unstructured":"Mavrovouniotis, M., Li, C., and Yang, S. (2017). A survey of swarm intelligence for dynamic optimization: Algorithms and applications. Preprint submitted to Journal of Swarm and Evolutionary Computation.","DOI":"10.1016\/j.swevo.2016.12.005"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_038","doi-asserted-by":"crossref","unstructured":"Nouiri, M., Bekrar, A., Jemai, A., Niar, S., and Ammari, A. C. (2015). An effective and distributed particle swarm optimization algorithm for flexible jo-shop scheduling problem. J Intell Manuf, Springer Science and Business Media, New York, DOI 10.1007\/s10845-015-1039-3,","DOI":"10.1007\/s10845-015-1039-3"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_039","unstructured":"Obagduwa, I.C. (2012). Cockroaches Optimization Algorithms, Seminar paper presented at the Progress Seminar, March 3rd UKZN, Durban."},{"key":"2025120523322316178_j_jisys-2020-0042_ref_040","doi-asserted-by":"crossref","unstructured":"Odili, J. B. and Kahar, M. N. M. (2016). Solving the Traveling Salesman\u2019s Problem Using the African Buffalo Optimization, Computational Intelligence and Neuroscience 2016, Hindawi Publishing Corporation.","DOI":"10.1155\/2016\/1510256"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_041","doi-asserted-by":"crossref","unstructured":"Osaba, E., Yang, X., Diaz, F., Lopez-Garcia, P. and Carballedo, R. (2016). An Improved Discrete Bat Algorithm for Symmetric and Asymmetric Traveling Salesman Problems, Engineering Applications of Artificial Intelligence 48 (1), 59-71.","DOI":"10.1016\/j.engappai.2015.10.006"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_042","doi-asserted-by":"crossref","unstructured":"Parpinelli, R.S., and Lopes, H.S. (2011). New inspirations in swarm intelligence: a survey, International Journal of Bio-Inspired Computation 3(1), pp.1\u201316.","DOI":"10.1504\/IJBIC.2011.038700"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_043","doi-asserted-by":"crossref","unstructured":"Parsopoulos, K.E., and Vrahatis, M.N. (2010). Particle Swarm Optimization and Intelligence: Advances and Applications Information Science Reference, Hershey, (NY).","DOI":"10.4018\/978-1-61520-666-7"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_044","unstructured":"Petrie, H.G. (1971). Philosophy & Rhetoric \u00a9 1971, Penn State University Press."},{"key":"2025120523322316178_j_jisys-2020-0042_ref_045","doi-asserted-by":"crossref","unstructured":"Poli, R., Kennedy, J., and Blackwell, T. (2007). Particle Swarm Optimization, An Overview. Swarm Intelligence 1, pp.33\u201357.","DOI":"10.1007\/s11721-007-0002-0"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_046","unstructured":"Reza G, Amin MF, Hamid T, Mahdi S (2008). Evolutionary Query Optimization for Heterogeneous Distributed Database Systems, World Academy of Science, Engineering and Technology, 43, pp.43-49"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_047","unstructured":"Sabet, S., Shokouhifar, M., and Farokhi, F. (2016). A comparison Between Swarm Intelligence Algorithms for Routing Problems. Electrical & Computer Engineering: An International Journal (ECIJ), Vol. 5, No. 1, pp. 17-33"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_048","unstructured":"Shangxiong, S. (2008). A Particle Swarm Optimization (PSO) Algorithm Based on Multi-Agent System, In IEEE International Conference on Intelligent Computation Technology and Automation, 978-0-7695-3357-5\/08"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_049","doi-asserted-by":"crossref","unstructured":"Shi, Y., and Eberhart, R.C. (1998). A Modified Particle Swarm Optimizers. In Proceedings of the IEEE International Conference on Evolutionary Computation, pp.69\u201373.","DOI":"10.1109\/ICEC.1998.699146"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_050","doi-asserted-by":"crossref","unstructured":"Shi, X.H., Liang, Y. C. Lee, H.P. Lu, C. and Wang, Q.X. (2007). Particle swarm optimization-based algorithms for TSP and generalized TSP, Elsevier, Information Processing Letters 103 (2007) 169\u2013176","DOI":"10.1016\/j.ipl.2007.03.010"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_051","doi-asserted-by":"crossref","unstructured":"Shu-Kai, S. F. and Chih-Hung, J. (2019). An Enhanced Partial Search to Particle Swarm Optimization for Unconstrained Optimization, Mathematics, 2019, 7, 357.","DOI":"10.3390\/math7040357"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_052","unstructured":"Sivanandam, S.N., and Deepa, S.N. (2008). Introduction to Genetic Algorithms Springer-Verlag Berlin Heidelberg"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_053","doi-asserted-by":"crossref","unstructured":"Sun, L., Song, X. and Chen, T. (2019). An Improved Convergence Particle Swarm Optimization Algorithmwith Random Sampling of Control Parameters, Hindawi, Journal of Control Science and Engineering Volume 2019.","DOI":"10.1155\/2019\/7478498"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_054","unstructured":"Suwannarongsri, S. and Puangdownreong, D. (2019). Optimal Solving Large Scale Traveling Transportation Problems by Flower Pollination Algorithm, WSEAS Transactions on Systems and Control, vol. 14."},{"key":"2025120523322316178_j_jisys-2020-0042_ref_055","unstructured":"Taghezout N, Adla A, Zarate P (2009). A Multi-agent Framework for Group Decision Support System: Application to a Boiler Combustion Management System (GLZ). International Journal of Software Engineering and Its Applications, 3(2), pp.9-20"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_056","doi-asserted-by":"crossref","unstructured":"Tian, Z.D., Li, S.J. and Wang, Y.H. (2017). Generalized predictive PID control for main steam temperature based on improved PSO algorithm. Journal of Advanced Computational Intelligence and Intelligent Informatics, 21(3):507\u2013517.","DOI":"10.20965\/jaciii.2017.p0507"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_057","doi-asserted-by":"crossref","unstructured":"Tian, Z.D., Li, S.J. and Wang, Y.H. (2018a). SVM Predictive Control for Calcination Zone Temperature in Lime Rotary Kiln with Improved PSO Algorithm. Transactions of the Institute of Measurement and Control, 40(10): 3134-3146.","DOI":"10.1177\/0142331217716983"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_058","doi-asserted-by":"crossref","unstructured":"Tian, Z.D., Li, S.J. and Wang, Y.H. (2018b). The multi-objective optimization model of flue aimed temperature of coke oven. Journal of Chemical Engineering of Japan, 51(8): 683-694.","DOI":"10.1252\/jcej.17we159"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_059","doi-asserted-by":"crossref","unstructured":"Tian, Z.D., Ren, Y. and Wang, G. (2019). Short-term Wind Speed Prediction Based on Improved PSO Algorithm Optimized EM-ELM. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 41(1):26-46.","DOI":"10.1080\/15567036.2018.1495782"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_060","unstructured":"Tian, Z.D., Wang, G. and Ren, Y. (2019). AMOAIA: Adaptive Multi-objective Optimization Artificial Immune Algorithm. IAENG International Journal of Applied Mathematics, vol. 49, no.1, pp14-21."},{"key":"2025120523322316178_j_jisys-2020-0042_ref_061","unstructured":"Tian, Z.D. and Zhang, C. (2018). An Improved Harmony Search Algorithm and its Application in Function Optimization. Journal of Information Processing Systems, 14(5): 1237-1253."},{"key":"2025120523322316178_j_jisys-2020-0042_ref_062","doi-asserted-by":"crossref","unstructured":"Tiejun, Z., Yihong, T. and Lining, X. (2006). A multi-agent approach for solving travelling salesman problem, Wuhan University Journal of Natural Sciences, 11(5), 1104-1108","DOI":"10.1007\/BF02829218"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_063","doi-asserted-by":"crossref","unstructured":"Tsai, H. K., Yang, J. M., Tsai, Y. F. and Kao, C. Y. (2004). \u201cAn Evolutionary Algorithm for Large Traveling Salesman Problems,\u201d IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, Vol. 34(4), pp. 1718-1729.","DOI":"10.1109\/TSMCB.2004.828283"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_064","doi-asserted-by":"crossref","unstructured":"Tsai, C., Tseng, S., Chiang, M., Yang, C. and Hong, T. (2014). A High-Performance Genetic Algorithm: Using Traveling Salesman Problem as a Case, Scientific World Journal 2014, Hindawi Publishing Corporation.","DOI":"10.1155\/2014\/178621"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_065","doi-asserted-by":"crossref","unstructured":"Wei, X., Han, L. and Hong, L. (2014). A Modified Ant Colony Algorithm for Traveling Salesman Problem, Int. J. of Comp., Comm. & Control. 9(5): 633-643.","DOI":"10.15837\/ijccc.2014.5.1280"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_066","unstructured":"Wooldridge, M. (2009), An Introduction to MultiAgent Systems 2nd ed., John Wiley & Sons Ltd, Chichester."},{"key":"2025120523322316178_j_jisys-2020-0042_ref_067","doi-asserted-by":"crossref","unstructured":"Xie, X. and Liu, J. (2009). Multiagent Optimization System for Solving the Travelling Salesman Problem (TSP), IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics, 39(2), 489-502","DOI":"10.1109\/TSMCB.2008.2006910"},{"key":"2025120523322316178_j_jisys-2020-0042_ref_068","unstructured":"Yan, X., Zhang, C., Luo, W., Li, W., Chen, W. and Liu, H. (2012). \u201cSolve Traveling Salesman Problem Using Particle Swarm Optimization Algorithm,\u201d IJCSI International Journal of Computer Science Issues, Vol. 9(6), pp. 264-271."},{"key":"2025120523322316178_j_jisys-2020-0042_ref_069","unstructured":"Zhong,W., Zhang, J. and Chen, W. (2007). A Novel Discrete Particle Swarm Optimization to solve Travelling Salesman Problem. In Proceedings of IEEE Congress on Evolutionary Computing (CEC 2007)."},{"key":"2025120523322316178_j_jisys-2020-0042_ref_070","unstructured":"Zhang, X., Chen, X., Xiao, H. and Li, W. (2016). A new imperialist competitive algorithm for solving TSP problem. Control and Decision 31(04): 586-592."}],"container-title":["Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/view\/journals\/jisys\/30\/1\/article-p413.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2020-0042\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2020-0042\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T23:35:10Z","timestamp":1764977710000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2020-0042\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,31]]},"references-count":70,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,9,22]]},"published-print":{"date-parts":[[2021,9,22]]}},"alternative-id":["10.1515\/jisys-2020-0042"],"URL":"https:\/\/doi.org\/10.1515\/jisys-2020-0042","relation":{},"ISSN":["2191-026X"],"issn-type":[{"value":"2191-026X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,31]]}}}