{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:49:08Z","timestamp":1742957348580,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031741821"},{"type":"electronic","value":"9783031741838"}],"license":[{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-74183-8_13","type":"book-chapter","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T07:04:34Z","timestamp":1728371074000},"page":"153-164","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Comparison Procedure for\u00a0the\u00a0Evaluation of\u00a0Metaheuristics"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7125-9421","authenticated-orcid":false,"given":"Enol","family":"Garc\u00eda Gonz\u00e1lez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6024-9527","authenticated-orcid":false,"given":"Jos\u00e9 R.","family":"Villar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4191-8438","authenticated-orcid":false,"given":"Javier","family":"Sedano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,9]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.swevo.2015.07.002","volume":"26","author":"H Abedinpourshotorban","year":"2016","unstructured":"Abedinpourshotorban, H., Mariyam Shamsuddin, S., Beheshti, Z., Jawawi, D.N.: Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evol. Comput. 26, 8\u201322 (2016). https:\/\/doi.org\/10.1016\/j.swevo.2015.07.002","journal-title":"Swarm Evol. Comput."},{"issue":"10","key":"13_CR2","doi-asserted-by":"publisher","first-page":"13170","DOI":"10.1016\/j.eswa.2011.04.126","volume":"38","author":"B Alatas","year":"2011","unstructured":"Alatas, B.: ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst. Appl. 38(10), 13170\u201313180 (2011). https:\/\/doi.org\/10.1016\/j.eswa.2011.04.126","journal-title":"Expert Syst. Appl."},{"key":"13_CR3","doi-asserted-by":"publisher","first-page":"106410","DOI":"10.1016\/j.cor.2023.106410","volume":"161","author":"JF Camacho-Vallejo","year":"2024","unstructured":"Camacho-Vallejo, J.F., Corpus, C., Villegas, J.G.: Metaheuristics for bilevel optimization: a comprehensive review. Comput. Oper. Res. 161, 106410 (2024). https:\/\/doi.org\/10.1016\/j.cor.2023.106410","journal-title":"Comput. Oper. Res."},{"issue":"10","key":"13_CR4","doi-asserted-by":"publisher","first-page":"12939","DOI":"10.1016\/j.eswa.2011.04.090","volume":"38","author":"N Chen","year":"2011","unstructured":"Chen, N., Ribeiro, B., Vieira, A.S., Duarte, J., Neves, J.C.: A genetic algorithm-based approach to cost-sensitive bankruptcy prediction. Expert Syst. Appl. 38(10), 12939\u201312945 (2011). https:\/\/doi.org\/10.1016\/j.eswa.2011.04.090","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"13_CR5","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/s10614-013-9407-6","volume":"45","author":"S Deng","year":"2015","unstructured":"Deng, S., Yoshiyama, K., Mitsubuchi, T., Sakurai, A.: Hybrid method of multiple kernel learning and genetic algorithm for forecasting short-term Foreign exchange rates. Comput. Econ. 45(1), 49\u201389 (2015). https:\/\/doi.org\/10.1007\/s10614-013-9407-6","journal-title":"Comput. Econ."},{"key":"13_CR6","doi-asserted-by":"publisher","unstructured":"Du, H., Wu, X., Zhuang, J.: Small-world optimization algorithm for function optimization. In: Advances in Natural Computation, pp. 264\u2013273 (2006). https:\/\/doi.org\/10.1007\/11881223_33","DOI":"10.1007\/11881223_33"},{"issue":"2","key":"13_CR7","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.advengsoft.2005.04.005","volume":"37","author":"OK Erol","year":"2006","unstructured":"Erol, O.K., Eksin, I.: A new optimization method: big bang-big crunch. Adv. Eng. Softw. 37(2), 106\u2013111 (2006). https:\/\/doi.org\/10.1016\/j.advengsoft.2005.04.005","journal-title":"Adv. Eng. Softw."},{"key":"13_CR8","doi-asserted-by":"publisher","DOI":"10.6036\/11070","author":"EG Gonz\u00e1lez","year":"2024","unstructured":"Gonz\u00e1lez, E.G., Villar, J.R., Sedano, J., Chira, C.: Benchmarking analysis for biological-based metaheuristics. Dyna (2024). https:\/\/doi.org\/10.6036\/11070","journal-title":"Dyna"},{"issue":"10","key":"13_CR9","doi-asserted-by":"publisher","first-page":"1986","DOI":"10.5829\/ije.2020.33.10a.17","volume":"33","author":"F Goodarzian","year":"2020","unstructured":"Goodarzian, F., Hosseini-Nasab, H., Fakhrzad, M.B.: A multi-objective sustainable medicine supply chain network design using a novel hybrid multi-objective metaheuristic algorithm. Int. J. Eng. 33(10), 1986\u20131995 (2020). https:\/\/doi.org\/10.5829\/ije.2020.33.10a.17","journal-title":"Int. J. Eng."},{"key":"13_CR10","doi-asserted-by":"publisher","first-page":"107535","DOI":"10.1016\/j.cie.2021.107535","volume":"160","author":"F Goodarzian","year":"2021","unstructured":"Goodarzian, F., Wamba, S.F., Mathiyazhagan, K., Taghipour, A.: A new bi-objective green medicine supply chain network design under fuzzy environment: hybrid metaheuristic algorithms. Comput. Ind. Eng. 160, 107535 (2021). https:\/\/doi.org\/10.1016\/j.cie.2021.107535","journal-title":"Comput. Ind. Eng."},{"issue":"7","key":"13_CR11","doi-asserted-by":"publisher","first-page":"7873","DOI":"10.1007\/s12652-020-02514-w","volume":"12","author":"F Gul","year":"2021","unstructured":"Gul, F., Rahiman, W., Alhady, S.S.N., Ali, A., Mir, I., Jalil, A.: Meta-heuristic approach for solving multi-objective path planning for autonomous guided robot using PSO-GWO optimization algorithm with evolutionary programming. J. Ambient. Intell. Humaniz. Comput. 12(7), 7873\u20137890 (2021). https:\/\/doi.org\/10.1007\/s12652-020-02514-w","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"13_CR12","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.ins.2012.08.023","volume":"222","author":"A Hatamlou","year":"2013","unstructured":"Hatamlou, A.: Black hole: a new heuristic optimization approach for data clustering. Inf. Sci. 222, 175\u2013184 (2013). https:\/\/doi.org\/10.1016\/j.ins.2012.08.023","journal-title":"Inf. Sci."},{"key":"13_CR13","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.compstruc.2012.09.003","volume":"112\u2013113","author":"A Kaveh","year":"2012","unstructured":"Kaveh, A., Khayatazad, M.: rA new meta-heuristic method: ay optimization. Comput. Struct. 112\u2013113, 283\u2013294 (2012). https:\/\/doi.org\/10.1016\/j.compstruc.2012.09.003","journal-title":"Comput. Struct."},{"issue":"3","key":"13_CR14","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/s00707-009-0270-4","volume":"213","author":"A Kaveh","year":"2010","unstructured":"Kaveh, A., Talatahari, S.: A novel heuristic optimization method: charged system search. Acta Mech. 213(3), 267\u2013289 (2010). https:\/\/doi.org\/10.1007\/s00707-009-0270-4","journal-title":"Acta Mech."},{"key":"13_CR15","doi-asserted-by":"publisher","unstructured":"Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995 - International Conference on Neural Networks, vol.\u00a04, pp. 1942\u20131948 (1995). https:\/\/doi.org\/10.1109\/ICNN.1995.488968","DOI":"10.1109\/ICNN.1995.488968"},{"issue":"6","key":"13_CR16","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1111\/jcal.12299","volume":"34","author":"D Lambi","year":"2018","unstructured":"Lambi, D., Lazovi, B., Djeni, A., Mari, M.: A novel metaheuristic approach for collaborative learning group formation. J. Comput. Assist. Learn. 34(6), 907\u2013916 (2018). https:\/\/doi.org\/10.1111\/jcal.12299","journal-title":"J. Comput. Assist. Learn."},{"key":"13_CR17","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.orp.2016.09.002","volume":"3","author":"M L\u00f3pez-Ib\u00e1\u00f1ez","year":"2016","unstructured":"L\u00f3pez-Ib\u00e1\u00f1ez, M., Dubois-Lacoste, J., P\u00e9rez C\u00e1ceres, L., St\u00fctzle, T., Birattari, M.: The irace package: iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43\u201358 (2016). https:\/\/doi.org\/10.1016\/j.orp.2016.09.002","journal-title":"Oper. Res. Perspect."},{"key":"13_CR18","doi-asserted-by":"publisher","first-page":"101248","DOI":"10.1016\/j.swevo.2023.101248","volume":"77","author":"Z Ma","year":"2023","unstructured":"Ma, Z., Wu, G., Suganthan, P.N., Song, A., Luo, Q.: Performance assessment and exhaustive listing of 500+ nature-inspired metaheuristic algorithms. Swarm Evol. Comput. 77, 101248 (2023). https:\/\/doi.org\/10.1016\/j.swevo.2023.101248","journal-title":"Swarm Evol. Comput."},{"key":"13_CR19","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.knosys.2015.07.006","volume":"89","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 89, 228\u2013249 (2015). https:\/\/doi.org\/10.1016\/j.knosys.2015.07.006","journal-title":"Knowl.-Based Syst."},{"key":"13_CR20","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51\u201367 (2016). https:\/\/doi.org\/10.1016\/j.advengsoft.2016.01.008","journal-title":"Adv. Eng. Softw."},{"key":"13_CR21","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46\u201361 (2014). https:\/\/doi.org\/10.1016\/j.advengsoft.2013.12.007","journal-title":"Adv. Eng. Softw."},{"key":"13_CR22","doi-asserted-by":"publisher","unstructured":"Serrano-P\u00e9rez, O., Villarreal-Cervantes, M.G., Gonz\u00e1lez-Robles, J.C., Rodr\u00edguez-Molina, A.: Meta-heuristic algorithms for the control tuning of omnidirectional mobile robots. Eng. Optim. 52(2), 325\u2013342 (2020). https:\/\/doi.org\/10.1080\/0305215X.2019.1585834","DOI":"10.1080\/0305215X.2019.1585834"},{"key":"13_CR23","doi-asserted-by":"publisher","unstructured":"Pierezan, J., Dos Santos\u00a0Coelho, L.: Coyote optimization algorithm: a new metaheuristic for global optimization problems. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp.\u00a01\u20138 (2018). https:\/\/doi.org\/10.1109\/CEC.2018.8477769","DOI":"10.1109\/CEC.2018.8477769"},{"key":"13_CR24","doi-asserted-by":"publisher","first-page":"83","DOI":"10.3233\/KES-180376","volume":"22","author":"A Sotoudeh-Anvari","year":"2018","unstructured":"Sotoudeh-Anvari, A., Hafezalkotob, A.: A bibliography of metaheuristics-review from 2009 to 2015. Int. J. Knowl.-Based Intell. Eng. Syst. 22, 83\u201395 (2018). https:\/\/doi.org\/10.3233\/KES-180376","journal-title":"Int. J. Knowl.-Based Intell. Eng. Syst."},{"issue":"4","key":"13_CR25","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341\u2013359 (1997). https:\/\/doi.org\/10.1023\/A:1008202821328","journal-title":"J. Global Optim."}],"container-title":["Lecture Notes in Computer Science","Hybrid Artificial Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-74183-8_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T07:10:40Z","timestamp":1728371440000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-74183-8_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,9]]},"ISBN":["9783031741821","9783031741838"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-74183-8_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,9]]},"assertion":[{"value":"9 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Hybrid Artificial Intelligence Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salamanca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"9 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hais2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/haisconference.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}