{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T11:34:44Z","timestamp":1768563284610,"version":"3.49.0"},"reference-count":73,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:00:00Z","timestamp":1687910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002848","name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo","doi-asserted-by":"publisher","award":["11231016"],"award-info":[{"award-number":["11231016"]}],"id":[{"id":"10.13039\/501100002848","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002848","name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo","doi-asserted-by":"publisher","award":["1210810"],"award-info":[{"award-number":["1210810"]}],"id":[{"id":"10.13039\/501100002848","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>This paper presents a set of adaptive parameter control methods through reinforcement learning for the particle swarm algorithm. The aim is to adjust the algorithm\u2019s parameters during the run, to provide the metaheuristics with the ability to learn and adapt dynamically to the problem and its context. The proposal integrates Q\u2013Learning into the optimization algorithm for parameter control. The applied strategies include a shared Q\u2013table, separate tables per parameter, and flexible state representation. The study was evaluated through various instances of the multidimensional knapsack problem belonging to the NP-hard class. It can be formulated as a mathematical combinatorial problem involving a set of items with multiple attributes or dimensions, aiming to maximize the total value or utility while respecting constraints on the total capacity or available resources. Experimental and statistical tests were carried out to compare the results obtained by each of these hybridizations, concluding that they can significantly improve the quality of the solutions found compared to the native version of the algorithm.<\/jats:p>","DOI":"10.3390\/axioms12070643","type":"journal-article","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T01:43:13Z","timestamp":1688002993000},"page":"643","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Learning\u2014Based Particle Swarm Optimizer for Solving Mathematical Combinatorial Problems"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0582-954X","authenticated-orcid":false,"given":"Rodrigo","family":"Olivares","sequence":"first","affiliation":[{"name":"Escuela de Ingenier\u00eda Inform\u00e1tica, Universidad de Valpara\u00edso, Valpara\u00edso 2362905, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5755-6929","authenticated-orcid":false,"given":"Ricardo","family":"Soto","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda Inform\u00e1tica, Pontificia Universidad Cat\u00f3lica de Valpara\u00edso, Valpara\u00edso 2362807, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5500-0188","authenticated-orcid":false,"given":"Broderick","family":"Crawford","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda Inform\u00e1tica, Pontificia Universidad Cat\u00f3lica de Valpara\u00edso, Valpara\u00edso 2362807, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5578-272X","authenticated-orcid":false,"given":"V\u00edctor","family":"R\u00edos","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda Inform\u00e1tica, Universidad de Valpara\u00edso, Valpara\u00edso 2362905, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5200-3289","authenticated-orcid":false,"given":"Pablo","family":"Olivares","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda Inform\u00e1tica, Universidad de Valpara\u00edso, Valpara\u00edso 2362905, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2228-5786","authenticated-orcid":false,"given":"Camilo","family":"Ravelo","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda Inform\u00e1tica, Universidad de Valpara\u00edso, Valpara\u00edso 2362905, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5334-1473","authenticated-orcid":false,"given":"Sebastian","family":"Medina","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda Inform\u00e1tica, Universidad de Valpara\u00edso, Valpara\u00edso 2362905, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6222-5460","authenticated-orcid":false,"given":"Diego","family":"Nauduan","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda Inform\u00e1tica, Universidad de Valpara\u00edso, Valpara\u00edso 2362905, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Du, K.L., Swamy, M., Du, K.L., and Swamy, M. (2016). Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature, Springer.","DOI":"10.1007\/978-3-319-41192-7"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Talbi, E.G. (2009). Metaheuristics: From Design to Implementation, John Wiley & Sons.","DOI":"10.1002\/9780470496916"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.ins.2013.02.041","article-title":"A survey on optimization metaheuristics","volume":"237","author":"Lepagnot","year":"2013","journal-title":"Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Panigrahi, B.K., Shi, Y., and Lim, M.H. (2011). Handbook of Swarm Intelligence: Concepts, Principles and Applications, Springer Science & Business Media.","DOI":"10.1007\/978-3-642-17390-5"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"10031","DOI":"10.1109\/ACCESS.2022.3142859","article-title":"Particle Swarm Optimization: A Comprehensive Survey","volume":"10","author":"Shami","year":"2022","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bansal, J.C. (2019). Evolutionary and Swarm Intelligence Algorithms, Springer.","DOI":"10.1007\/978-3-319-91341-4"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","article-title":"No free lunch theorems for optimization","volume":"1","author":"Wolpert","year":"1997","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_8","unstructured":"Hoos, H.H. (2012). Autonomous Search, Springer."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/TEVC.2019.2921598","article-title":"A Survey of Automatic Parameter Tuning Methods for Metaheuristics","volume":"24","author":"Huang","year":"2019","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1515\/math-2017-0029","article-title":"Learnheuristics: Hybridizing metaheuristics with machine learning for optimization with dynamic inputs","volume":"15","author":"Calvet","year":"2017","journal-title":"Open Math."},{"key":"ref_11","unstructured":"Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An introduction, MIT Press."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"200041","DOI":"10.1016\/j.sasc.2022.200041","article-title":"Dynamic Multidimensional Knapsack Problem benchmark datasets","volume":"4","author":"Skackauskas","year":"2022","journal-title":"Syst. Soft Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9788","DOI":"10.1016\/j.apm.2016.06.002","article-title":"A binary differential search algorithm for the 0\u20131 multidimensional knapsack problem","volume":"40","author":"Liu","year":"2016","journal-title":"Appl. Math. Model."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105693","DOI":"10.1016\/j.cor.2021.105693","article-title":"Knapsack problems-An overview of recent advances. Part II: Multiple, multidimensional, and quadratic knapsack problems","volume":"143","author":"Cacchiani","year":"2022","journal-title":"Comput. Oper. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1007\/s11063-021-10662-z","article-title":"Application of supervised machine learning methods on the multidimensional knapsack problem","volume":"54","author":"Rezoug","year":"2022","journal-title":"Neural Process. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1057\/jors.1990.166","article-title":"OR-Library: Distributing test problems by electronic mail","volume":"41","author":"Beasley","year":"1990","journal-title":"J. Oper. Res. Soc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"13147","DOI":"10.1007\/s00500-020-04730-0","article-title":"A self-adaptive virus optimization algorithm for continuous optimization problems","volume":"24","author":"Liang","year":"2020","journal-title":"Soft Comput."},{"key":"ref_18","unstructured":"Olamaei, J., Moradi, M., and Kaboodi, T. (May, January 30). A new adaptive modified firefly algorithm to solve optimal capacitor placement problem. Proceedings of the 18th Electric Power Distribution Conference, Kermanshah, Iran."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.ins.2014.11.042","article-title":"Modified cuckoo search algorithm with self adaptive parameter method","volume":"298","author":"Li","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1007\/s00521-012-1285-7","article-title":"Self-adaptive constrained artificial bee colony for constrained numerical optimization","volume":"24","author":"Li","year":"2014","journal-title":"Neural Comput. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6171","DOI":"10.1007\/s00500-017-2685-5","article-title":"A novel differential evolution algorithm with a self-adaptation parameter control method by differential evolution","volume":"22","author":"Cui","year":"2018","journal-title":"Soft Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"de Barros, J.B., Sampaio, R.C., and Llanos, C.H. (2019, January 26\u201330). An adaptive discrete particle swarm optimization for mapping real-time applications onto network-on-a-chip based MPSoCs. Proceedings of the 32nd Symposium on Integrated Circuits and Systems Design, Sao Paulo, Brazil.","DOI":"10.1145\/3338852.3339835"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cruz-Salinas, A.F., and Perdomo, J.G. (2017, January 15\u201319). Self-adaptation of genetic operators through genetic programming techniques. Proceedings of the Genetic and Evolutionary Computation Conference, Berlin, Germany.","DOI":"10.1145\/3071178.3071214"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100972","DOI":"10.1016\/j.aei.2019.100972","article-title":"An augmented self-adaptive parameter control in evolutionary computation: A case study for the berth scheduling problem","volume":"42","author":"Kavoosi","year":"2019","journal-title":"Adv. Eng. Inform."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nasser, A.B., and Zamli, K.Z. (2018, January 8\u201310). Parameter free flower algorithm based strategy for pairwise testing. Proceedings of the 2018 7th international conference on software and computer applications, Kuantan Malaysia.","DOI":"10.1145\/3185089.3185109"},{"key":"ref_26","unstructured":"Zhang, L., Chen, H., Wang, W., and Liu, S. (2018). FSDM, IOS Press."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Soto, R., Crawford, B., Olivares, R., Carrasco, C., Rodriguez-Tello, E., Castro, C., Paredes, F., and de la Fuente-Mella, H. (2020). A reactive population approach on the dolphin echolocation algorithm for solving cell manufacturing systems. Mathematics, 8.","DOI":"10.3390\/math8091389"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.ejor.2021.04.032","article-title":"Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art","volume":"296","author":"Mohammadi","year":"2022","journal-title":"Eur. J. Oper. Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Rubio, \u00c1., Soto, R., Crawford, B., Jaramillo, A., Mancilla, D., Castro, C., and Olivares, R. (2022). Applying Parallel and Distributed Models on Bio\u2013Inspired Algorithms via a Clustering Method. Mathematics, 10.","DOI":"10.3390\/math10020274"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Caselli, N., Soto, R., Crawford, B., Valdivia, S., and Olivares, R. (2021). A self\u2013adaptive cuckoo search algorithm using a machine learning technique. Mathematics, 9.","DOI":"10.3390\/math9161840"},{"key":"ref_31","first-page":"1","article-title":"Human behaviour based optimization supported with self\u2013organizing maps for solving the S\u2013box design Problem","volume":"2021","author":"Soto","year":"2021","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Valdivia, S., Soto, R., Crawford, B., Caselli, N., Paredes, F., Castro, C., and Olivares, R. (2020). Clustering\u2013based binarization methods applied to the crow search algorithm for 0\/1 combinatorial problems. Mathematics, 8.","DOI":"10.3390\/math8071070"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s10732-010-9125-3","article-title":"Autonomous operator management for evolutionary algorithms","volume":"16","author":"Maturana","year":"2010","journal-title":"J. Heuristics"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4939","DOI":"10.1016\/j.eswa.2014.01.040","article-title":"Reactive search strategies using reinforcement learning, local search algorithms and variable neighborhood search","volume":"41","author":"Neto","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.3923\/jas.2010.1991.2000","article-title":"A new machine learning based approach for tuning metaheuristics for the solution of hard combinatorial optimization problems","volume":"10","author":"Zennaki","year":"2010","journal-title":"J. Appl. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"12826","DOI":"10.1016\/j.eswa.2011.04.075","article-title":"Tuning metaheuristics: A data mining based approach for particle swarm optimization","volume":"38","author":"Lessmann","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1007\/s00500-014-1262-4","article-title":"An adaptive particle swarm optimization method based on clustering","volume":"19","author":"Liang","year":"2015","journal-title":"Soft Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.ins.2019.07.016","article-title":"A parameter-free particle swarm optimization algorithm using performance classifiers","volume":"503","author":"Harrison","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1109\/TSMC.2016.2560128","article-title":"A supervised learning and control method to improve particle swarm optimization algorithms","volume":"47","author":"Dong","year":"2016","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kurek, M., and Luk, W. (2012, January 10\u201312). Parametric reconfigurable designs with machine learning optimizer. Proceedings of the 2012 International Conference on Field-Programmable Technology, Seoul, Republic of Korea.","DOI":"10.1109\/FPT.2012.6412120"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.procs.2014.09.097","article-title":"Data mining based hybridization of meta-RaPS","volume":"36","author":"Rabadi","year":"2014","journal-title":"Procedia Comput. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1007\/s00521-015-1829-8","article-title":"Multiple parameter control for ant colony optimization applied to feature selection problem","volume":"26","author":"Wang","year":"2015","journal-title":"Neural Comput. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"107044","DOI":"10.1016\/j.knosys.2021.107044","article-title":"Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems","volume":"223","author":"Seyyedabbasi","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sadeg, S., Hamdad, L., Remache, A.R., Karech, M.N., Benatchba, K., and Habbas, Z. (2019, January 12\u201314). Qbso-fs: A reinforcement learning based bee swarm optimization metaheuristic for feature selection. Proceedings of the Advances in Computational Intelligence: 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Gran Canaria, Spain. Proceedings, Part II 15.","DOI":"10.1007\/978-3-030-20518-8_65"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"109","DOI":"10.19026\/rjaset.11.1682","article-title":"Nature-inspired parameter controllers for ACO-based reactive search","volume":"11","author":"Sagban","year":"2015","journal-title":"Res. J. Appl. Sci. Eng. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3223","DOI":"10.3934\/jimo.2020115","article-title":"Tabu search guided by reinforcement learning for the max-mean dispersion problem","volume":"17","author":"Nijimbere","year":"2020","journal-title":"J. Ind. Manag. Optim."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1016\/j.trpro.2020.03.095","article-title":"A biased-randomized learnheuristic for solving the team orienteering problem with dynamic rewards","volume":"47","author":"Juan","year":"2020","journal-title":"Transp. Res. Procedia"},{"key":"ref_48","unstructured":"Kusy, M., and Zajdel, R. (2014). Intelligent Systems in Technical and Medical Diagnostics, Springer."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1109\/4235.771166","article-title":"Parameter control in evolutionary algorithms","volume":"3","author":"Eiben","year":"1999","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1162\/EVCO_a_00022","article-title":"On the optimal convergence probability of univariate estimation of distribution algorithms","volume":"19","author":"Rastegar","year":"2011","journal-title":"Evol. Comput."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"012063","DOI":"10.1088\/1742-6596\/973\/1\/012063","article-title":"Parameter meta-optimization of metaheuristics of solving specific NP-hard facility location problem","volume":"973","author":"Skakov","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_52","first-page":"43","article-title":"The irace package: Iterated racing for automatic algorithm configuration","volume":"3","author":"Birattari","year":"2016","journal-title":"Oper. Res. Perspect."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.swevo.2016.04.003","article-title":"Using autonomous search for solving constraint satisfaction problems via new modern approaches","volume":"30","author":"Soto","year":"2016","journal-title":"Swarm Evol. Comput."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s11047-016-9576-z","article-title":"Online control of enumeration strategies via bat algorithm and black hole optimization","volume":"16","author":"Soto","year":"2017","journal-title":"Nat. Comput."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1613\/jair.301","article-title":"Reinforcement learning: A survey","volume":"4","author":"Kaelbling","year":"1996","journal-title":"J. Artif. Intell. Res."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Huotari, T., Savolainen, J., and Collan, M. (2020). Deep Reinforcement Learning Agent for S&P 500 Stock Selection. Axioms, 9.","DOI":"10.3390\/axioms9040130"},{"key":"ref_57","unstructured":"Van Otterlo, M., and Wiering, M. (2012). Reinforcement Learning: State-of-the-Art, Springer."},{"key":"ref_58","first-page":"e01890","article-title":"A hybrid data-driven and metaheuristic optimization approach for the compressive strength prediction of high-performance concrete","volume":"18","author":"Imran","year":"2023","journal-title":"Case Stud. Constr. Mater."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/BF00992698","article-title":"Q-learning","volume":"8","author":"Watkins","year":"1992","journal-title":"Mach. Learn."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zhang, L., Tang, L., Zhang, S., Wang, Z., Shen, X., and Zhang, Z. (2021). A Self-Adaptive Reinforcement-Exploration Q-Learning Algorithm. Symmetry, 13.","DOI":"10.3390\/sym13061057"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Melo, F.S., and Ribeiro, M.I. (2007, January 2\u20135). Convergence of Q-learning with linear function approximation. Proceedings of the 2007 European Control Conference (ECC), Kos, Greece.","DOI":"10.23919\/ECC.2007.7068926"},{"key":"ref_62","first-page":"2","article-title":"The dynamics of reinforcement learning in cooperative multiagent systems","volume":"1998","author":"Claus","year":"1998","journal-title":"AAAI\/IAAI"},{"key":"ref_63","first-page":"58","article-title":"Learning to cooperate in multi-agent systems by combining Q-learning and evolutionary strategy","volume":"1","author":"McGlohon","year":"2005","journal-title":"Int. J. Lateral Comput."},{"key":"ref_64","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995-International Conference on Neural Networks, Perth, Australia."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"100718","DOI":"10.1016\/j.swevo.2020.100718","article-title":"Population size in Particle Swarm Optimization","volume":"58","author":"Piotrowski","year":"2020","journal-title":"Swarm Evol. Comput."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/BF02022561","article-title":"Dynamic tabu list management using the reverse elimination method","volume":"41","author":"Dammeyer","year":"1993","journal-title":"Ann. Oper. Res."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/BF02242185","article-title":"A simulated annealing approach to the multiconstraint zero-one knapsack problem","volume":"40","author":"Drexl","year":"1988","journal-title":"Computing"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Khuri, S., B\u00e4ck, T., and Heitk\u00f6tter, J. (1994, January 6). The zero\/one multiple knapsack problem and genetic algorithms. Proceedings of the 1994 ACM Symposium on Applied Computing, New York, NY, USA.","DOI":"10.1145\/326619.326694"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"8404231","DOI":"10.1155\/2017\/8404231","article-title":"Putting Continuous Metaheuristics to Work in Binary Search Spaces","volume":"2017","author":"Crawford","year":"2017","journal-title":"Complexity"},{"key":"ref_70","unstructured":"Eberhart, R.C., and Shi, Y. (1998, January 25\u201327). Comparison between genetic algorithms and particle swarm optimization. Proceedings of the Evolutionary Programming VII: 7th International Conference, EP98, San Diego, CA, USA. Proceedings 7."},{"key":"ref_71","unstructured":"Universidad de Valpara\u00edso (2023, June 27). Implementations. Available online: https:\/\/figshare.com\/articles\/dataset\/PSOQLAV_Parameter_Test\/14999874."},{"key":"ref_72","unstructured":"Universidad de Valpara\u00edso (2023, June 27). Test Instances. Available online: https:\/\/figshare.com\/articles\/dataset\/Test_Instances\/14999907."},{"key":"ref_73","unstructured":"Universidad de Valpara\u00edso (2023, June 27). Data and Results. Available online: https:\/\/figshare.com\/articles\/dataset\/PSOQL_Test_Data\/14995374."}],"container-title":["Axioms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1680\/12\/7\/643\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:02:30Z","timestamp":1760126550000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1680\/12\/7\/643"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,28]]},"references-count":73,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["axioms12070643"],"URL":"https:\/\/doi.org\/10.3390\/axioms12070643","relation":{},"ISSN":["2075-1680"],"issn-type":[{"value":"2075-1680","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,28]]}}}