{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:21:49Z","timestamp":1781108509687,"version":"3.54.1"},"reference-count":47,"publisher":"IGI Global Scientific Publishing","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,4]]},"abstract":"<jats:p>This article proposes a new optimal data clustering method for finding optimal clusters of data by incorporating chaotic maps into the standard NOA. NOA, a newly developed optimization technique, has been shown to be efficient in generating optimal results with lowest solution cost. The incorporation of chaotic maps into metaheuristics enables algorithms to diversify the solution space into two phases: explore and exploit more. To make the NOA more efficient and avoid premature convergence, chaotic maps are incorporated in this work, termed as CNOAs. Ten different chaotic maps are incorporated individually into standard NOA for testing the optimization performance. The CNOA is first benchmarked on 23 standard functions. Secondly, testing was done on the numerical complexity of the new clustering method which utilizes CNOA, by solving 10 UCI data cluster problems and 4 web document cluster problems. The comparisons have been made with the help of obtaining statistical and graphical results. The superiority of the proposed optimal clustering algorithm is evident from the simulations and comparisons.<\/jats:p>","DOI":"10.4018\/ijssci.2019040101","type":"journal-article","created":{"date-parts":[[2019,7,10]],"date-time":"2019-07-10T11:32:51Z","timestamp":1562758371000},"page":"1-25","source":"Crossref","is-referenced-by-count":3,"title":["A Novel Chaotic Northern Bald Ibis Optimization Algorithm for Solving Different Cluster Problems [ICCICC18 #155]"],"prefix":"10.4018","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3658-6651","authenticated-orcid":true,"given":"Ravi Kumar","family":"Saidala","sequence":"first","affiliation":[{"name":"Acharya Nagarjuna University, Guntur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4864-8482","authenticated-orcid":true,"given":"Nagaraju","family":"Devarakonda","sequence":"additional","affiliation":[{"name":"Lakireddy Bali Reddy College of Engineering, Mylavaram, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJSSCI.2019040101-0","unstructured":"Aarts, E., & Korst, J. (1988). Simulated annealing and Boltzmann machines. U.S. Dept. of Energy."},{"key":"IJSSCI.2019040101-1","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2014.02.001"},{"key":"IJSSCI.2019040101-2","doi-asserted-by":"publisher","DOI":"10.4018\/IJCINI.2015100105"},{"key":"IJSSCI.2019040101-3","author":"J.Brownlee","year":"2011","journal-title":"Clever algorithms: nature-inspired programming recipes"},{"key":"IJSSCI.2019040101-4","unstructured":"Brownlee, J. (2016). Supervised and unsupervised machine learning algorithms. Machine Learning Mastery, 16(3)."},{"key":"IJSSCI.2019040101-5","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2005.04.005"},{"key":"IJSSCI.2019040101-6","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-013-9400-4"},{"key":"IJSSCI.2019040101-7","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2016.2586973"},{"key":"IJSSCI.2019040101-8","doi-asserted-by":"publisher","DOI":"10.4018\/IJSSCI.2015040105"},{"key":"IJSSCI.2019040101-9","author":"J.Han","year":"2011","journal-title":"Data mining: concepts and techniques"},{"key":"IJSSCI.2019040101-10","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2014.10.062"},{"key":"IJSSCI.2019040101-11","first-page":"1","article-title":"Metaheuristic research: A comprehensive survey.","author":"K.Hussain","year":"2018","journal-title":"Artificial Intelligence Review"},{"key":"IJSSCI.2019040101-12","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2009.09.011"},{"key":"IJSSCI.2019040101-13","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.10.010"},{"key":"IJSSCI.2019040101-14","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2015.12.001"},{"key":"IJSSCI.2019040101-15","doi-asserted-by":"publisher","DOI":"10.1002\/9781118029145"},{"key":"IJSSCI.2019040101-16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcde.2017.02.005"},{"key":"IJSSCI.2019040101-17","doi-asserted-by":"crossref","DOI":"10.1002\/9781118874059","author":"D. T.Larose","year":"2014","journal-title":"Discovering knowledge in data: an introduction to data mining"},{"key":"IJSSCI.2019040101-18","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2017.07.002"},{"key":"IJSSCI.2019040101-19","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2016.01.008"},{"key":"IJSSCI.2019040101-20","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.08.010"},{"key":"IJSSCI.2019040101-21","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s00521-016-2686-9","article-title":"Social group optimization for global optimization of multimodal functions and data clustering problems.","author":"A.Naik","year":"2018","journal-title":"Neural Computing & Applications"},{"key":"IJSSCI.2019040101-22","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2013.11.003"},{"key":"IJSSCI.2019040101-23","doi-asserted-by":"publisher","DOI":"10.4018\/ijssci.2014070102"},{"key":"IJSSCI.2019040101-24","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2850220"},{"key":"IJSSCI.2019040101-25","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2018.03.019"},{"key":"IJSSCI.2019040101-26","doi-asserted-by":"crossref","unstructured":"Saidala, R. K., & Devarakonda, N. (2017c). Multi-Swarm Whale Optimization Algorithm for Data Clustering Problems using Multiple Cooperative Strategies. International Journal of Intelligent Systems and Applications.","DOI":"10.5815\/ijisa.2018.08.04"},{"key":"IJSSCI.2019040101-27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCI-CC.2017.8109777"},{"key":"IJSSCI.2019040101-28","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-3223-3_25"},{"key":"IJSSCI.2019040101-29","doi-asserted-by":"publisher","DOI":"10.4018\/IJSSCI.2018010104"},{"key":"IJSSCI.2019040101-30","doi-asserted-by":"crossref","unstructured":"Saidala, R. K., & Devarakonda, N. (2018d). Northern Bald Ibis Optimization Algorithm: Theory and Application. In Proceedings of the 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC) (pp. 541-551). IEEE.","DOI":"10.1109\/ICCI-CC.2018.8482088"},{"key":"IJSSCI.2019040101-31","doi-asserted-by":"crossref","unstructured":"Saidala, R. K., Devarakonda, N., & Edara, S. R. (2018). Enhanced Northern Bald Ibis Optimization Algorithm based Clustering Method for Web Document Clustering.","DOI":"10.1109\/ICCI-CC.2018.8482088"},{"key":"IJSSCI.2019040101-32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCI-CC.2018.8482030"},{"key":"IJSSCI.2019040101-33","doi-asserted-by":"publisher","DOI":"10.1109\/I2CT.2017.8226214"},{"key":"IJSSCI.2019040101-34","doi-asserted-by":"publisher","DOI":"10.1109\/I2CT.2017.8226205"},{"key":"IJSSCI.2019040101-35","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2017.01.004"},{"key":"IJSSCI.2019040101-36","doi-asserted-by":"publisher","DOI":"10.4018\/jcini.2011010102"},{"key":"IJSSCI.2019040101-37","doi-asserted-by":"publisher","DOI":"10.1016\/0375-9601(87)90796-1"},{"key":"IJSSCI.2019040101-38","doi-asserted-by":"publisher","DOI":"10.1201\/b16553"},{"key":"IJSSCI.2019040101-39","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-6889-7"},{"key":"IJSSCI.2019040101-40","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-013-1485-9"},{"key":"IJSSCI.2019040101-41","doi-asserted-by":"publisher","DOI":"10.1038\/35099670"},{"key":"IJSSCI.2019040101-42","author":"I. H.Witten","year":"2016","journal-title":"Data Mining: Practical machine learning tools and techniques"},{"key":"IJSSCI.2019040101-43","doi-asserted-by":"publisher","DOI":"10.1109\/4235.585893"},{"key":"IJSSCI.2019040101-44","author":"X.Yang","year":"2008","journal-title":"Introduction to mathematical optimization"},{"key":"IJSSCI.2019040101-45","author":"X. S.Yang","year":"2012","journal-title":"Metaheuristics in water, geotechnical and transport engineering"},{"key":"IJSSCI.2019040101-46","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2013.12.016"}],"container-title":["International Journal of Software Science and Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=233520","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T00:36:16Z","timestamp":1694997376000},"score":1,"resource":{"primary":{"URL":"http:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJSSCI.2019040101"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2019,4]]},"references-count":47,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.4018\/ijssci.2019040101","relation":{},"ISSN":["1942-9045","1942-9037"],"issn-type":[{"value":"1942-9045","type":"print"},{"value":"1942-9037","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4]]}}}