{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T19:08:06Z","timestamp":1793473686636,"version":"4.5.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001775","name":"University of Technology Sydney","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001775","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>A novel metaheuristic optimization algorithm, namely the Farthest better or Nearest worse Optimizer (FNO) algorithm, is proposed in this paper. The idea behind the FNO algorithm is derived from the qualities and distances between agents\u2019 positions in a search space. The process of searching in the FNO includes two phases. During the first phase of the FNO, it jumps over the nearest regions with lower potential to avoid local optima. In the second phase, the algorithm tries to explore the farthest positions with higher potential to reach or explore the global optimum. These operations aim to enhance population diversity and provide the FNO with opportunities to discover high-quality regions while avoiding low-quality regions. A structural component within FNO, called Dynamic Focus Strategy (DFS), is also presented for controlling the exploration ratio. The DFS applies a random vector as a coefficient to shrink the area around the farthest better positions throughout the search process. Several experimental studies have been conducted on well-known benchmark suites, comprising 45 benchmarks, to assess the efficacy of the FNO algorithm. Additionally, five engineering problems were used to evaluate the practical applicability of the proposed FNO algorithm. The Wilcoxon test, as a well-known non-parametric statistical test, is conducted to fairly compare results. The findings indicate that the FNO algorithm performs competitively against other state-of-the-art population-based metaheuristic algorithms on the tested problems.<\/jats:p>","DOI":"10.1007\/s10462-025-11443-z","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T14:01:36Z","timestamp":1767794496000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Farthest better or nearest worse optimizer: a novel metaheuristic algorithm"],"prefix":"10.1007","volume":"59","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8521-1074","authenticated-orcid":false,"given":"Ahmad","family":"Taheri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1763-8583","authenticated-orcid":false,"given":"Keyvan","family":"RahimiZadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0282-0462","authenticated-orcid":false,"given":"Jan","family":"Baumbach","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5988-5494","authenticated-orcid":false,"given":"Amin","family":"Beheshti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9424-8052","authenticated-orcid":false,"given":"Olga","family":"Zolotareva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1980-1791","authenticated-orcid":false,"given":"Mohammed Azmi","family":"Al-Betar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1443-9458","authenticated-orcid":false,"given":"Seyedali","family":"Mirjalili","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2798-0104","authenticated-orcid":false,"given":"Amir H.","family":"Gandomi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"key":"11443_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2020.113609","volume":"376","author":"L Abualigah","year":"2021","unstructured":"Abualigah L, Diabat A, Mirjalili S et al (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609. https:\/\/doi.org\/10.1016\/j.cma.2020.113609","journal-title":"Comput Methods Appl Mech Eng"},{"key":"11443_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116158","volume":"191","author":"L Abualigah","year":"2022","unstructured":"Abualigah L, Elaziz MA, Sumari P et al (2022) Reptile search algorithm (RSA): a nature-inspired meta-heuristic optimizer. Expert Syst Appl 191:116158. https:\/\/doi.org\/10.1016\/j.eswa.2021.116158","journal-title":"Expert Syst Appl"},{"key":"11443_CR3","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.asoc.2018.02.025","volume":"66","author":"\u0130B Aydilek","year":"2018","unstructured":"Aydilek \u0130B (2018) A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Appl Soft Comput 66:232\u2013249. https:\/\/doi.org\/10.1016\/j.asoc.2018.02.025","journal-title":"Appl Soft Comput"},{"key":"11443_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2019.01.006","volume":"50","author":"P Bujok","year":"2019","unstructured":"Bujok P, Tvrd\u00edk J, Pol\u00e1kov\u00e1 R (2019) Comparison of nature-inspired population-based algorithms on continuous optimisation problems. Swarm Evol Comput 50:100490. https:\/\/doi.org\/10.1016\/j.swevo.2019.01.006","journal-title":"Swarm Evol Comput"},{"key":"11443_CR5","doi-asserted-by":"publisher","DOI":"10.1002\/9781118165881","volume-title":"Nonparametric Statistics for Non-Statisticians","author":"GW Corder","year":"2009","unstructured":"Corder GW, Foreman DI (2009) Nonparametric Statistics for Non-Statisticians. John Wiley & Sons Inc, Hoboken"},{"key":"11443_CR6","doi-asserted-by":"publisher","first-page":"2013","DOI":"10.2514\/3.10834","volume":"29","author":"K Deb","year":"1991","unstructured":"Deb K (1991) Optimal design of a welded beam via genetic algorithms. AIAA J 29:2013\u20132015. https:\/\/doi.org\/10.2514\/3.10834","journal-title":"AIAA J"},{"key":"11443_CR7","doi-asserted-by":"crossref","unstructured":"Dorigo M, Di Caro G Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406). IEEE, pp 1470\u20131477","DOI":"10.1109\/CEC.1999.782657"},{"key":"11443_CR8","doi-asserted-by":"crossref","unstructured":"Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS\u201995. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. IEEE, pp 39\u201343","DOI":"10.1109\/MHS.1995.494215"},{"key":"11443_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113377","volume":"152","author":"A Faramarzi","year":"2020","unstructured":"Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020a) Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst Appl 152:113377. https:\/\/doi.org\/10.1016\/j.eswa.2020.113377","journal-title":"Expert Syst Appl"},{"key":"11443_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105190","volume":"191","author":"A Faramarzi","year":"2020","unstructured":"Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020b) Equilibrium optimizer: a novel optimization algorithm. Knowledge-Based Syst 191:105190. https:\/\/doi.org\/10.1016\/j.knosys.2019.105190","journal-title":"Knowledge-Based Syst"},{"key":"11443_CR11","first-page":"185","volume-title":"Mirjalili S","author":"H Faris","year":"2020","unstructured":"Faris H, Mirjalili S, Aljarah I et al (2020) Salp Swarm Algorithm: Theory, Literature Review, and Application in Extreme Learning Machines BT - Nature-Inspired Optimizers: Theories, Literature Reviews and Applications. In: Song Dong J, Lewis A (eds) Mirjalili S. Springer International Publishing, Cham, pp 185\u2013199"},{"key":"11443_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120886","volume":"232","author":"Y Gao","year":"2023","unstructured":"Gao Y (2023) PID-based search algorithm: a novel metaheuristic algorithm based on PID algorithm. Expert Syst Appl 232:120886. https:\/\/doi.org\/10.1016\/j.eswa.2023.120886","journal-title":"Expert Syst Appl"},{"key":"11443_CR13","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s10586-024-04761-4","volume":"28","author":"Y Gao","year":"2024","unstructured":"Gao Y, Wang J, Li C (2024a) Escape after love: Philoponella prominens optimizer and its application to 3D path planning. Cluster Comput 28:81. https:\/\/doi.org\/10.1007\/s10586-024-04761-4","journal-title":"Cluster Comput"},{"key":"11443_CR14","doi-asserted-by":"publisher","first-page":"12346","DOI":"10.1007\/s11227-024-05905-4","volume":"80","author":"Y Gao","year":"2024","unstructured":"Gao Y, Zhang J, Wang Y et al (2024b) Love evolution algorithm: a stimulus\u2013value\u2013role theory-inspired evolutionary algorithm for global optimization. J Supercomput 80:12346\u201312407. https:\/\/doi.org\/10.1007\/s11227-024-05905-4","journal-title":"J Supercomput"},{"key":"11443_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/106365603321828970","volume":"11","author":"N Hansen","year":"2003","unstructured":"Hansen N, M\u00fcller SD, Koumoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol Comput 11:1\u201318. https:\/\/doi.org\/10.1162\/106365603321828970","journal-title":"Evol Comput"},{"key":"11443_CR16","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1007\/s10489-012-0420-6","volume":"39","author":"M Hasanzadeh","year":"2013","unstructured":"Hasanzadeh M, Meybodi MR, Ebadzadeh MM (2013) Adaptive cooperative particle swarm optimizer. Appl Intell 39:397\u2013420. https:\/\/doi.org\/10.1007\/s10489-012-0420-6","journal-title":"Appl Intell"},{"key":"11443_CR17","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.1016\/j.amc.2006.07.134","volume":"186","author":"Q He","year":"2007","unstructured":"He Q, Wang L (2007) A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Appl Math Comput 186:1407\u20131422. https:\/\/doi.org\/10.1016\/j.amc.2006.07.134","journal-title":"Appl Math Comput"},{"key":"11443_CR18","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","volume":"97","author":"AA Heidari","year":"2019","unstructured":"Heidari AA, Mirjalili S, Faris H et al (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849\u2013872. https:\/\/doi.org\/10.1016\/j.future.2019.02.028","journal-title":"Future Gener Comput Syst"},{"key":"11443_CR19","doi-asserted-by":"crossref","unstructured":"Holland JH (1992) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence | MIT Press eBooks | IEEE Xplore. In: MIT Press. https:\/\/ieeexplore.ieee.org\/book\/6267401. Accessed 24 Oct 2022","DOI":"10.7551\/mitpress\/1090.001.0001"},{"key":"11443_CR20","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.amc.2006.07.105","volume":"186","author":"F Huang","year":"2007","unstructured":"Huang F, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186:340\u2013356. https:\/\/doi.org\/10.1016\/j.amc.2006.07.105","journal-title":"Appl Math Comput"},{"key":"11443_CR21","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 (2010) A novel heuristic optimization method: charged system search. Acta Mech 213:267\u2013289. https:\/\/doi.org\/10.1007\/s00707-009-0270-4","journal-title":"Acta Mech"},{"issue":"2","key":"11443_CR22","first-page":"2014","volume":"635","author":"JJ Liang","year":"2013","unstructured":"Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore 635(2):2014","journal-title":"Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore"},{"key":"11443_CR23","doi-asserted-by":"crossref","unstructured":"Lu J, Zhou X, Ma Y, et al (2019) A Novel Artificial Bee Colony Algorithm with Division of Labor for Solving CEC 2019 100-Digit Challenge Benchmark Problems. In: 2019 IEEE Congress on Evolutionary Computation (CEC). pp 387\u2013394","DOI":"10.1109\/CEC.2019.8790252"},{"key":"11443_CR24","doi-asserted-by":"publisher","first-page":"1567","DOI":"10.1016\/j.amc.2006.11.033","volume":"188","author":"M Mahdavi","year":"2007","unstructured":"Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188:1567\u20131579. https:\/\/doi.org\/10.1016\/j.amc.2006.11.033","journal-title":"Appl Math Comput"},{"key":"11443_CR25","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1080\/03052150701364022","volume":"39","author":"E Mezura-Montes","year":"2007","unstructured":"Mezura-Montes E, Coello Coello CA, Vel\u00e1zquez-Reyes J, Mu\u00f1oz-D\u00e1vila L (2007) Multiple trial vectors in differential evolution for engineering design. Eng Optim 39:567\u2013589. https:\/\/doi.org\/10.1080\/03052150701364022","journal-title":"Eng Optim"},{"key":"11443_CR26","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.advengsoft.2015.01.010","volume":"83","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80\u201398. https:\/\/doi.org\/10.1016\/j.advengsoft.2015.01.010","journal-title":"Adv Eng Softw"},{"key":"11443_CR27","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.knosys.2015.12.022","volume":"96","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S (2016a) Sca: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120\u2013133. https:\/\/doi.org\/10.1016\/j.knosys.2015.12.022","journal-title":"Knowl-Based Syst"},{"key":"11443_CR28","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1007\/s00521-015-1920-1","volume":"27","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S (2016b) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27:1053\u20131073. https:\/\/doi.org\/10.1007\/s00521-015-1920-1","journal-title":"Neural Comput Appl"},{"key":"11443_CR29","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 (2016) The whale optimization algorithm. Adv Eng Softw 95:51\u201367. https:\/\/doi.org\/10.1016\/j.advengsoft.2016.01.008","journal-title":"Adv Eng Softw"},{"key":"11443_CR30","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 SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46\u201361. https:\/\/doi.org\/10.1016\/j.advengsoft.2013.12.007","journal-title":"Adv Eng Softw"},{"key":"11443_CR31","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.matcom.2022.06.007","volume":"202","author":"J-S Pan","year":"2022","unstructured":"Pan J-S, Zhang L-G, Wang R-B et al (2022) Gannet optimization algorithm\u202f: a new metaheuristic algorithm for solving engineering optimization problems. Math Comput Simul 202:343\u2013373. https:\/\/doi.org\/10.1016\/j.matcom.2022.06.007","journal-title":"Math Comput Simul"},{"key":"11443_CR32","unstructured":"Price, K. V., Awad, N. H., Ali, M. Z., & Suganthan, P. N. (2018). Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization. In\u00a0Technical report. Singapore: Nanyang Technological University."},{"key":"11443_CR33","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1115\/1.3438995","volume":"98","author":"KM Ragsdell","year":"1976","unstructured":"Ragsdell KM, Phillips DT (1976) Optimal design of a class of welded structures using geometric programming. J Eng Ind 98:1021\u20131025. https:\/\/doi.org\/10.1115\/1.3438995","journal-title":"J Eng Ind"},{"key":"11443_CR34","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.cad.2010.12.015","volume":"43","author":"RV Rao","year":"2011","unstructured":"Rao RV, Savsani VJ, Vakharia DP (2011) Teaching\u2013learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput des 43:303\u2013315. https:\/\/doi.org\/10.1016\/j.cad.2010.12.015","journal-title":"Comput des"},{"key":"11443_CR35","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1080\/03052150108940941","volume":"33","author":"T Ray","year":"2001","unstructured":"Ray T, Saini P (2001) Engineering design optimization using a swarm with an intelligent information sharing among individuals. Eng Optim 33:735\u2013748. https:\/\/doi.org\/10.1080\/03052150108940941","journal-title":"Eng Optim"},{"key":"11443_CR36","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.advengsoft.2017.01.004","volume":"105","author":"S Saremi","year":"2017","unstructured":"Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30\u201347. https:\/\/doi.org\/10.1016\/j.advengsoft.2017.01.004","journal-title":"Adv Eng Softw"},{"key":"11443_CR37","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn R, Price K (1997) Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341\u2013359. https:\/\/doi.org\/10.1023\/A:1008202821328","journal-title":"J Glob Optim"},{"key":"11443_CR38","unstructured":"Suganthan PN, Hansen N, Liang JJ, et al (2005) Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization"},{"key":"11443_CR39","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.ins.2021.06.064","volume":"576","author":"A Taheri","year":"2021","unstructured":"Taheri A, RahimiZadeh K, Rao RV (2021) An efficient Balanced Teaching-Learning-Based optimization algorithm with Individual restarting strategy for solving global optimization problems. Inf Sci (Ny) 576:68\u2013104. https:\/\/doi.org\/10.1016\/j.ins.2021.06.064","journal-title":"Inf Sci (Ny)"},{"key":"11443_CR40","doi-asserted-by":"publisher","DOI":"10.5267\/j.ijiec.2015.8.004","author":"R Venkata Rao","year":"2016","unstructured":"Venkata Rao R (2016) Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput. https:\/\/doi.org\/10.5267\/j.ijiec.2015.8.004","journal-title":"Int J Ind Eng Comput"},{"key":"11443_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.apm.2025.116052","volume":"143","author":"J Wang","year":"2025","unstructured":"Wang J, Gao Y, Qin L, Li Y (2025) Logistic-Gauss circle optimizer: theory and applications. Appl Math Model 143:116052. https:\/\/doi.org\/10.1016\/j.apm.2025.116052","journal-title":"Appl Math Model"},{"key":"11443_CR42","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67\u201382. https:\/\/doi.org\/10.1109\/4235.585893","journal-title":"IEEE Trans Evol Comput"},{"key":"11443_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2024.102464","volume":"61","author":"Y Xiao","year":"2024","unstructured":"Xiao Y, Cui H, Hussien AG, Hashim FA (2024) MSAO: a multi-strategy boosted snow ablation optimizer for global optimization and real-world engineering applications. Adv Eng Inform 61:102464. https:\/\/doi.org\/10.1016\/j.aei.2024.102464","journal-title":"Adv Eng Inform"},{"key":"11443_CR44","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/s10462-024-11023-7","volume":"58","author":"Y Xiao","year":"2025","unstructured":"Xiao Y, Cui H, Khurma RA, Castillo PA (2025) Artificial lemming algorithm: a novel bionic meta-heuristic technique for solving real-world engineering optimization problems. Artif Intell Rev 58:84. https:\/\/doi.org\/10.1007\/s10462-024-11023-7","journal-title":"Artif Intell Rev"},{"key":"11443_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.104966","volume":"188","author":"Y Xu","year":"2020","unstructured":"Xu Y, Yang Z, Li X et al (2020) Dynamic opposite learning enhanced teaching\u2013learning-based optimization. Knowl-Based Syst 188:104966. https:\/\/doi.org\/10.1016\/j.knosys.2019.104966","journal-title":"Knowl-Based Syst"},{"key":"11443_CR46","first-page":"65","volume-title":"A New Metaheuristic Bat-Inspired Algorithm","author":"X-S Yang","year":"2010","unstructured":"Yang X-S (2010) A New Metaheuristic Bat-Inspired Algorithm. Springer, Berlin, pp 65\u201374"},{"key":"11443_CR47","doi-asserted-by":"publisher","DOI":"10.1504\/IJBIC.2020.10036562","volume":"1","author":"XT Zeng","year":"2020","unstructured":"Zeng XT, Li H, He F et al (2020) A novel whale optimization algorithm with filtering disturbance and non-linear step. Int J Bio-Inspir Comput 1:1. https:\/\/doi.org\/10.1504\/IJBIC.2020.10036562","journal-title":"Int J Bio-Inspir Comput"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-025-11443-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-025-11443-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-025-11443-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T05:48:09Z","timestamp":1771480089000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-025-11443-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,7]]},"references-count":47,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["11443"],"URL":"https:\/\/doi.org\/10.1007\/s10462-025-11443-z","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,7]]},"assertion":[{"value":"15 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}},{"value":"This article is licensed under a Creative Commons Attribution-NonCommercialNoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third-party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit\n                      \n                      .","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Open Access"}}],"article-number":"79"}}