{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:12:13Z","timestamp":1742911933633,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031629211"},{"type":"electronic","value":"9783031629228"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-62922-8_20","type":"book-chapter","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T13:02:58Z","timestamp":1718629378000},"page":"292-305","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Population of\u00a0Hyperparametric Solutions for\u00a0the\u00a0Design of\u00a0Metaheuristic Algorithms: An Empirical Analysis of\u00a0Performance in\u00a0Particle Swarm Optimization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8231-7041","authenticated-orcid":false,"given":"Mario A.","family":"Navarro","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7711-7551","authenticated-orcid":false,"given":"Angel","family":"Casas-Ordaz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2573-7722","authenticated-orcid":false,"given":"Beatriz A.","family":"Rivera-Aguilar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2145-1233","authenticated-orcid":false,"given":"Bernardo","family":"Morales-Casta\u00f1eda","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8781-7993","authenticated-orcid":false,"given":"Diego","family":"Oliva","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,18]]},"reference":[{"key":"20_CR1","unstructured":"Awad, N., Ali, M., Suganthan, P., Liang, J., Qu, B.: Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization. School of EEE, Nanyang Technological University, Singapore (2016)"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Bagdonavi\u010dius, V., Kruopis, J., Nikulin, M.S.: Non-parametric tests for complete data. ISTE\/Wiley (2011)","DOI":"10.1002\/9781118558072"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Bao, G., Mao, K.: Particle swarm optimization algorithm with asymmetric time varying acceleration coefficients. In: 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 2134\u20132139. IEEE (2009)","DOI":"10.1109\/ROBIO.2009.5420504"},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern search technology. In: Handbook of Metaheuristics, pp. 457\u2013474 (2003)","DOI":"10.1007\/0-306-48056-5_16"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Burke, E.K., Hyde, M.R., Kendall, G., Ochoa, G., \u00d6zcan, E., Woodward, J.R.: A classification of hyper-heuristic approaches: revisited. In: Handbook of Metaheuristics, pp. 453\u2013477 (2019)","DOI":"10.1007\/978-3-319-91086-4_14"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Cui, Z., Zeng, J., Yin, Y.: An improved PSO with time-varying accelerator coefficients. In: 2008 Eighth International Conference on Intelligent Systems Design and Applications, vol.\u00a02, pp. 638\u2013643. IEEE (2008)","DOI":"10.1109\/ISDA.2008.86"},{"issue":"4\/5","key":"20_CR7","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1147\/JRD.2017.2709578","volume":"61","author":"GI Diaz","year":"2017","unstructured":"Diaz, G.I., Fokoue-Nkoutche, A., Nannicini, G., Samulowitz, H.: An effective algorithm for hyperparameter optimization of neural networks. IBM J. Res. Dev. 61(4\/5), 9\u20131 (2017)","journal-title":"IBM J. Res. Dev."},{"key":"20_CR8","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/978-3-031-09753-9_24","volume-title":"Artificial Intelligence and Applied Mathematics in Engineering","author":"S Duman","year":"2021","unstructured":"Duman, S., Kahraman, H.T., Korkmaz, B., Bakir, H., Guvenc, U., Yilmaz, C.: Improved phasor particle swarm optimization with fitness distance balance for optimal power flow problem of hybrid AC\/DC power grids. In: Jude Hemanth, D., Kose, U., Watada, J., Patrut, B. (eds.) Artificial Intelligence and Applied Mathematics in Engineering, pp. 307\u2013336. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-031-09753-9_24"},{"issue":"200","key":"20_CR9","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","volume":"32","author":"M Friedman","year":"1937","unstructured":"Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32(200), 675\u2013701 (1937)","journal-title":"J. Am. Stat. Assoc."},{"key":"20_CR10","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1016\/j.proeng.2013.02.063","volume":"53","author":"M Imran","year":"2013","unstructured":"Imran, M., Hashim, R., Abd Khalid, N.E.: An overview of particle swarm optimization variants. Procedia Eng. 53, 491\u2013496 (2013)","journal-title":"Procedia Eng."},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"Jabeen, H., Jalil, Z., Baig, A.R.: Opposition based initialization in particle swarm optimization (O-PSO). In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, pp. 2047\u20132052 (2009)","DOI":"10.1145\/1570256.1570274"},{"issue":"17","key":"20_CR12","doi-asserted-by":"publisher","first-page":"8392","DOI":"10.3390\/app12178392","volume":"12","author":"M Jain","year":"2022","unstructured":"Jain, M., Saihjpal, V., Singh, N., Singh, S.B.: An overview of variants and advancements of PSO algorithm. Appl. Sci. 12(17), 8392 (2022)","journal-title":"Appl. Sci."},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Johnson, D.S., et al.: A theoretician\u2019s guide to the experimental analysis of algorithms. In: Data Structures, Near Neighbor Searches, and Methodology, vol. 5, pp. 215\u2013250 (1999)","DOI":"10.1090\/dimacs\/059\/11"},{"key":"20_CR14","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.swevo.2019.05.010","volume":"49","author":"FEF Junior","year":"2019","unstructured":"Junior, F.E.F., Yen, G.G.: Particle swarm optimization of deep neural networks architectures for image classification. Swarm Evol. Comput. 49, 62\u201374 (2019)","journal-title":"Swarm Evol. Comput."},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995-International Conference on Neural Networks, vol.\u00a04, pp. 1942\u20131948. IEEE (1995)","DOI":"10.1109\/ICNN.1995.488968"},{"issue":"3","key":"20_CR16","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1080\/08839519508945477","volume":"9","author":"RD King","year":"1995","unstructured":"King, R.D., Feng, C., Sutherland, A.: Statlog: comparison of classification algorithms on large real-world problems. Appl. Artif. Intell. Int. J. 9(3), 289\u2013333 (1995)","journal-title":"Appl. Artif. Intell. Int. J."},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Li, H.R., Gao, Y.L.: Particle swarm optimization algorithm with exponent decreasing inertia weight and stochastic mutation. In: 2009 Second International Conference on Information and Computing Science, vol.\u00a01, pp. 66\u201369. IEEE (2009)","DOI":"10.1109\/ICIC.2009.24"},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Mirjalili, S., Hashim, S.Z.M.: A new hybrid psogsa algorithm for function optimization. In: 2010 International Conference on Computer and Information Application, pp. 374\u2013377. IEEE (2010)","DOI":"10.1109\/ICCIA.2010.6141614"},{"key":"20_CR19","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1007\/s10489-017-0903-6","volume":"47","author":"SJ Mousavirad","year":"2017","unstructured":"Mousavirad, S.J., Ebrahimpour-Komleh, H.: Human mental search: a new population-based metaheuristic optimization algorithm. Appl. Intell. 47, 850\u2013887 (2017)","journal-title":"Appl. Intell."},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"Pant, M., Radha, T., Singh, V.: Particle swarm optimization using gaussian inertia weight. In: International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007), vol.\u00a01, pp. 97\u2013102. IEEE (2007)","DOI":"10.1109\/ICCIMA.2007.96"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Pant, M., Thangaraj, R., Grosan, C., Abraham, A.: Improved particle swarm optimization with low-discrepancy sequences. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 3011\u20133018. IEEE (2008)","DOI":"10.1109\/CEC.2008.4631204"},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Ripley, B.D.: Statistical aspects of neural networks. In: Natworks and Chaos-Statistical and Probabilistic Aspects, pp. 40\u2013123 (1993)","DOI":"10.1007\/978-1-4899-3099-6_2"},{"key":"20_CR23","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/0-387-28356-0_17","volume-title":"Search Methodologies","author":"P Ross","year":"2005","unstructured":"Ross, P.: Hyper-heuristics. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies, pp. 529\u2013556. Springer, Boston (2005). https:\/\/doi.org\/10.1007\/0-387-28356-0_17"},{"key":"20_CR24","doi-asserted-by":"publisher","first-page":"10031","DOI":"10.1109\/ACCESS.2022.3142859","volume":"10","author":"TM Shami","year":"2022","unstructured":"Shami, T.M., El-Saleh, A.A., Alswaitti, M., Al-Tashi, Q., Summakieh, M.A., Mirjalili, S.: Particle swarm optimization: a comprehensive survey. IEEE Access 10, 10031\u201310061 (2022)","journal-title":"IEEE Access"},{"key":"20_CR25","unstructured":"Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360), pp. 69\u201373. IEEE (1998)"},{"key":"20_CR26","doi-asserted-by":"crossref","unstructured":"Song, M.P., Gu, G.C.: Research on particle swarm optimization: a review. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 04EX826), vol.\u00a04, pp. 2236\u20132241. IEEE (2004)","DOI":"10.1109\/ICMLC.2004.1382171"},{"issue":"1","key":"20_CR27","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1177\/1748301816665021","volume":"11","author":"I Sousa-Ferreira","year":"2017","unstructured":"Sousa-Ferreira, I., Sousa, D.: A review of velocity-type PSO variants. J. Algorithms Comput. Technol. 11(1), 23\u201330 (2017)","journal-title":"J. Algorithms Comput. Technol."},{"key":"20_CR28","doi-asserted-by":"crossref","unstructured":"Uy, N.Q., Hoai, N.X., McKay, R.I., Tuan, P.M.: Initialising PSO with randomised low-discrepancy sequences: the comparative results. In: 2007 IEEE Congress on Evolutionary Computation, pp. 1985\u20131992. IEEE (2007)","DOI":"10.1109\/CEC.2007.4424717"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Varna, F.T., Husbands, P.: HIDMS-PSO: a new heterogeneous improved dynamic multi-swarm PSO algorithm. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 473\u2013480. IEEE (2020)","DOI":"10.1109\/SSCI47803.2020.9308313"},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"Wang, H., Li, C., Liu, Y., Zeng, S.: A hybrid particle swarm algorithm with cauchy mutation. In: 2007 IEEE Swarm Intelligence Symposium, pp. 356\u2013360. IEEE (2007)","DOI":"10.1109\/SIS.2007.367959"},{"key":"20_CR31","doi-asserted-by":"crossref","unstructured":"Wei, J., Wang, Y.: A dynamical particle swarm algorithm with dimension mutation. In: 2006 International Conference on Computational Intelligence and Security, vol.\u00a01, pp. 254\u2013257. IEEE (2006)","DOI":"10.1109\/ICCIAS.2006.294131"},{"key":"20_CR32","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","volume":"415","author":"L Yang","year":"2020","unstructured":"Yang, L., Shami, A.: On hyperparameter optimization of machine learning algorithms: theory and practice. Neurocomputing 415, 295\u2013316 (2020)","journal-title":"Neurocomputing"},{"key":"20_CR33","doi-asserted-by":"crossref","unstructured":"Yang, W.P.: Vertical particle swarm optimization algorithm and its application in soft-sensor modeling. In: 2007 International Conference on Machine Learning and Cybernetics, vol.\u00a04, pp. 1985\u20131988. IEEE (2007)","DOI":"10.1109\/ICMLC.2007.4370472"},{"key":"20_CR34","unstructured":"Yu, T., Zhu, H.: Hyper-parameter optimization: a review of algorithms and applications. arXiv preprint arXiv:2003.05689 (2020)"},{"key":"20_CR35","doi-asserted-by":"crossref","unstructured":"Ziyu, T., Dingxue, Z.: A modified particle swarm optimization with an adaptive acceleration coefficients. In: 2009 Asia-Pacific Conference on Information Processing, vol.\u00a02, pp. 330\u2013332. IEEE (2009)","DOI":"10.1109\/APCIP.2009.217"}],"container-title":["Lecture Notes in Computer Science","Metaheuristics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-62922-8_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:43:31Z","timestamp":1732236211000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-62922-8_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031629211","9783031629228"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-62922-8_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"18 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Metaheuristics International Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lorient","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"metic2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mic2024.fr\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}