{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T02:31:54Z","timestamp":1775269914524,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2016,8,19]],"date-time":"2016-08-19T00:00:00Z","timestamp":1471564800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2018,3]]},"DOI":"10.1007\/s00521-016-2528-9","type":"journal-article","created":{"date-parts":[[2016,8,19]],"date-time":"2016-08-19T07:05:58Z","timestamp":1471590358000},"page":"111-121","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Memory-enriched big bang\u2013big crunch optimization algorithm for data clustering"],"prefix":"10.1007","volume":"29","author":[{"given":"Kayvan","family":"Bijari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hadi","family":"Zare","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hadi","family":"Veisi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hossein","family":"Bobarshad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,8,19]]},"reference":[{"key":"2528_CR1","volume-title":"Machine learning: a probabilistic perspective, mass","author":"K Murphy","year":"2012","unstructured":"Murphy K (2012) Machine learning: a probabilistic perspective, mass, 1st edn. The MIT Press, Cambridge","edition":"1"},{"issue":"8","key":"2528_CR2","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","volume":"31","author":"AK Jain","year":"2010","unstructured":"Jain AK (2010) Data clustering: 50 years beyond k-means. Pattern Recognit Lett 31(8):651\u2013666","journal-title":"Pattern Recognit Lett"},{"key":"2528_CR3","doi-asserted-by":"crossref","unstructured":"Gan G, Ma C, Wu J (2007) Data clustering: theory, algorithms, and applications. SIAM, Society for Industrial and Applied Mathematics, Philadelphia, Alexandria","DOI":"10.1137\/1.9780898718348"},{"issue":"4","key":"2528_CR4","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1007\/s00521-009-0300-0","volume":"19","author":"N Ahmad","year":"2010","unstructured":"Ahmad N, Alahakoon D, Chau R (2010) Cluster identification and separation in the growing self-organizing map: application in protein sequence classification. Neural Comput Appl 19(4):531\u2013542","journal-title":"Neural Comput Appl"},{"issue":"7\u20138","key":"2528_CR5","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1007\/s00521-013-1437-4","volume":"24","author":"HM Moftah","year":"2014","unstructured":"Moftah HM, Azar AT, Al-Shammari ET, Ghali NI, Hassanien AE, Shoman M (2014) Adaptive k-means clustering algorithm for MR breast image segmentation. Neural Comput Appl 24(7\u20138):1917\u20131928","journal-title":"Neural Comput Appl"},{"key":"2528_CR6","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.asoc.2016.01.044","volume":"43","author":"P Moradi","year":"2016","unstructured":"Moradi P, Gholampour M (2016) A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy. Appl Soft Comput 43:117\u2013130","journal-title":"Appl Soft Comput"},{"issue":"7\u20138","key":"2528_CR7","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1007\/s00521-012-0959-5","volume":"22","author":"Y-K Lam","year":"2013","unstructured":"Lam Y-K, Tsang PW-M, Leung C-S (2013) Pso-based k-means clustering with enhanced cluster matching for gene expression data. Neural Comput Appl 22(7\u20138):1349\u20131355","journal-title":"Neural Comput Appl"},{"issue":"1","key":"2528_CR8","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s00521-013-1468-x","volume":"24","author":"LP Dinu","year":"2014","unstructured":"Dinu LP, Ionescu RT (2014) Clustering based on median and closest string via rank distance with applications on dna. Neural Comput Appl 24(1):77\u201384","journal-title":"Neural Comput Appl"},{"issue":"1","key":"2528_CR9","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1080\/00949658208810560","volume":"15","author":"WJ Welch","year":"1982","unstructured":"Welch WJ (1982) Algorithmic complexity: three NP-hard problems in computational statistics. J Stat Comput Simul 15(1):17\u201325","journal-title":"J Stat Comput Simul"},{"issue":"9","key":"2528_CR10","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1016\/0031-3203(95)00022-R","volume":"28","author":"KS Al-Sultan","year":"1995","unstructured":"Al-Sultan KS (1995) A tabu search approach to the clustering problem. Pattern Recognit 28(9):1443\u20131451","journal-title":"Pattern Recognit"},{"issue":"3","key":"2528_CR11","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/3477.764879","volume":"29","author":"K Krishna","year":"1999","unstructured":"Krishna K, Murty MN (1999) Genetic k-means algorithm. IEEE Trans Syst Man Cybern B Cybern 29(3):433\u2013439","journal-title":"IEEE Trans Syst Man Cybern B Cybern"},{"issue":"2","key":"2528_CR12","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.aca.2003.12.032","volume":"509","author":"P Shelokar","year":"2004","unstructured":"Shelokar P, Jayaraman VK, Kulkarni BD (2004) An ant colony approach for clustering. Anal Chim Acta 509(2):187\u2013195","journal-title":"Anal Chim Acta"},{"issue":"1","key":"2528_CR13","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1016\/j.eswa.2011.07.123","volume":"39","author":"T Cura","year":"2012","unstructured":"Cura T (2012) A particle swarm optimization approach to clustering. Expert Syst Appl 39(1):1582\u20131588","journal-title":"Expert Syst Appl"},{"issue":"7","key":"2528_CR14","doi-asserted-by":"crossref","first-page":"4761","DOI":"10.1016\/j.eswa.2009.11.003","volume":"37","author":"C Zhang","year":"2010","unstructured":"Zhang C, Ouyang D, Ning J (2010) An artificial bee colony approach for clustering. Expert Syst Appl 37(7):4761\u20134767","journal-title":"Expert Syst Appl"},{"issue":"3","key":"2528_CR15","doi-asserted-by":"crossref","first-page":"1754","DOI":"10.1016\/j.eswa.2007.01.028","volume":"34","author":"Y-T Kao","year":"2008","unstructured":"Kao Y-T, Zahara E, Kao I-W (2008) A hybridized approach to data clustering. Expert Syst Appl 34(3):1754\u20131762","journal-title":"Expert Syst Appl"},{"key":"2528_CR16","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.asoc.2014.10.026","volume":"26","author":"AR Jordehi","year":"2015","unstructured":"Jordehi AR (2015) Enhanced leader PSO (ELPSO): a new PSO variant for solving global optimisation problems. Appl Soft Comput 26:401\u2013417","journal-title":"Appl Soft Comput"},{"key":"2528_CR17","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1016\/j.ijepes.2014.07.058","volume":"64","author":"AR Jordehi","year":"2015","unstructured":"Jordehi AR, Jasni J, Wahab NA, Kadir M, Javadi M (2015) Enhanced leader PSO (ELPSO): a new algorithm for allocating distributed TCSCs in power systems. Int J Electr Power Energy Syst 64:771\u2013784","journal-title":"Int J Electr Power Energy Syst"},{"key":"2528_CR18","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.asoc.2014.11.018","volume":"28","author":"L Zhang","year":"2015","unstructured":"Zhang L, Tang Y, Hua C, Guan X (2015) A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques. Appl Soft Comput 28:138\u2013149","journal-title":"Appl Soft Comput"},{"key":"2528_CR19","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.engappai.2014.03.007","volume":"32","author":"S Tabakhi","year":"2014","unstructured":"Tabakhi S, Moradi P, Akhlaghian F (2014) An unsupervised feature selection algorithm based on ant colony optimization. Eng Appl Artif Intell 32:112\u2013123","journal-title":"Eng Appl Artif Intell"},{"key":"2528_CR20","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.ijepes.2014.12.083","volume":"69","author":"AR Jordehi","year":"2015","unstructured":"Jordehi AR (2015) Brainstorm optimisation algorithm (BSOA): an efficient algorithm for finding optimal location and setting of FACTS devices in electric power systems. Int J Electr Power Energy Syst 69:48\u201357","journal-title":"Int J Electr Power Energy Syst"},{"issue":"6","key":"2528_CR21","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1080\/0952813X.2015.1020568","volume":"27","author":"AR Jordehi","year":"2015","unstructured":"Jordehi AR (2015) Seeker optimisation (human group optimisation) algorithm with chaos. J Exp Theor Artif Intell 27(6):753\u2013762","journal-title":"J Exp Theor Artif Intell"},{"key":"2528_CR22","doi-asserted-by":"publisher","unstructured":"Heidari AA, Abbaspour RA, Jordehi AR (2015) An efficient chaotic water cycle algorithm for optimization tasks. Neural Comput Appl 1\u201329. doi: 10.1007\/s00521-015-2037-2","DOI":"10.1007\/s00521-015-2037-2"},{"issue":"4","key":"2528_CR23","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1007\/s00521-014-1751-5","volume":"26","author":"AR Jordehi","year":"2015","unstructured":"Jordehi AR (2015) A chaotic artificial immune system optimisation algorithm for solving global continuous optimisation problems. Neural Comput Appl 26(4):827\u2013833","journal-title":"Neural Comput Appl"},{"issue":"2","key":"2528_CR24","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.advengsoft.2005.04.005","volume":"37","author":"OK Erol","year":"2006","unstructured":"Erol OK, Eksin I (2006) A new optimization method: big bang\u2013big crunch. Adv Eng Softw 37(2):106\u2013111","journal-title":"Adv Eng Softw"},{"issue":"3","key":"2528_CR25","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s40031-013-0053-0","volume":"94","author":"H Verma","year":"2013","unstructured":"Verma H, Mafidar P (2013) Tlbo based voltage stable environment friendly economic dispatch considering real and reactive power constraints. J Inst Eng (India) B 94(3):193\u2013206","journal-title":"J Inst Eng (India) B"},{"key":"2528_CR26","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.ijepes.2014.11.022","volume":"67","author":"CF Kucuktezcan","year":"2015","unstructured":"Kucuktezcan CF, Genc VI (2015) Preventive and corrective control applications in power systems via big bang\u2013big crunch optimization. Int J Electr Power Energy Syst 67:114\u2013124","journal-title":"Int J Electr Power Energy Syst"},{"issue":"8","key":"2528_CR27","doi-asserted-by":"crossref","first-page":"2888","DOI":"10.1016\/j.ymssp.2010.03.012","volume":"24","author":"H Tang","year":"2010","unstructured":"Tang H, Zhou J, Xue S, Xie L (2010) Big bang\u2013big crunch optimization for parameter estimation in structural systems. Mech Syst Signal Process 24(8):2888\u20132897","journal-title":"Mech Syst Signal Process"},{"issue":"6","key":"2528_CR28","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1007\/s00521-014-1613-1","volume":"25","author":"AR Jordehi","year":"2014","unstructured":"Jordehi AR (2014) A chaotic-based big bang\u2013big crunch algorithm for solving global optimisation problems. Neural Comput Appl 25(6):1329\u20131335","journal-title":"Neural Comput Appl"},{"key":"2528_CR29","doi-asserted-by":"crossref","DOI":"10.1002\/9780470382776","volume-title":"Clustering","author":"R Xu","year":"2008","unstructured":"Xu R, Wunsch D (2008) Clustering. Wiley, Hoboken"},{"key":"2528_CR30","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2007","unstructured":"Bishop CM (2007) Pattern recognition and machine learning, 1st edn. Springer, New York","edition":"1"},{"key":"2528_CR31","volume-title":"Innovative computational intelligence: a rough guide to 134 clever algorithms","author":"B Xing","year":"2013","unstructured":"Xing B, Gao WJ (2013) Innovative computational intelligence: a rough guide to 134 clever algorithms, 2014th edn. Springer, New York","edition":"2014"},{"key":"2528_CR32","doi-asserted-by":"crossref","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","journal-title":"Appl Math Comput"},{"key":"2528_CR33","doi-asserted-by":"crossref","unstructured":"Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the 1999 congress on evolutionary computation, 1999. CEC 99, vol.\u00a03, p.\u00a01950","DOI":"10.1109\/CEC.1999.785511"},{"key":"2528_CR34","doi-asserted-by":"crossref","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","journal-title":"Adv Eng Softw"},{"key":"2528_CR35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2013.11.003","volume":"16","author":"SJ Nanda","year":"2014","unstructured":"Nanda SJ, Panda G (2014) A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evolut Comput 16:1\u201318","journal-title":"Swarm Evolut Comput"},{"key":"2528_CR36","unstructured":"Lichman M (2013) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-016-2528-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-016-2528-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-016-2528-9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-016-2528-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T08:05:23Z","timestamp":1601021123000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-016-2528-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,8,19]]},"references-count":36,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2018,3]]}},"alternative-id":["2528"],"URL":"https:\/\/doi.org\/10.1007\/s00521-016-2528-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,8,19]]}}}