{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T06:13:22Z","timestamp":1777011202154,"version":"3.51.4"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,10,3]],"date-time":"2020-10-03T00:00:00Z","timestamp":1601683200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,10,3]],"date-time":"2020-10-03T00:00:00Z","timestamp":1601683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s10489-020-01872-4","type":"journal-article","created":{"date-parts":[[2020,10,3]],"date-time":"2020-10-03T01:02:36Z","timestamp":1601686956000},"page":"1645-1668","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An enhanced particle swarm optimization algorithm to solve probabilistic load flow problem in a micro-grid"],"prefix":"10.1007","volume":"51","author":[{"given":"Hajar","family":"Bagheri Tolabi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Afshin","family":"Lashkar Ara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rahil","family":"Hosseini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,3]]},"reference":[{"key":"1872_CR1","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1016\/j.apenergy.2019.03.059","volume":"242","author":"M Breen","year":"2019","unstructured":"Breen M, Murphy MD, Upton J (2019) Development of a dairy multi-objective optimization (DAIRYMOO) method for economic and environmental optimization of dairy farms. Appl Energy 242:1697\u20131711","journal-title":"Appl Energy"},{"key":"1872_CR2","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.ins.2013.02.041","volume":"237","author":"I Boussa\u00efd","year":"2013","unstructured":"Boussa\u00efd I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Inf Sci 237:82\u2013117","journal-title":"Inf Sci"},{"key":"1872_CR3","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.knosys.2015.12.022","volume":"96","author":"SA Mirjalili","year":"2016","unstructured":"Mirjalili SA (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120\u2013133","journal-title":"Knowl-Based Syst"},{"key":"1872_CR4","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1061\/(ASCE)0733-9496(1994)120:4(423)","volume":"120","author":"AR Simpson","year":"1994","unstructured":"Simpson AR, Dandy GC, Murphy LJ (1994) Genetic algorithms compared to other techniques for pipe optimization. J Water Resour Plan Manag 120:423\u2013443","journal-title":"J Water Resour Plan Manag"},{"key":"1872_CR5","unstructured":"James C (2003) \"introduction to Stochastics search and optimization,\" ed: Wiley-Interscience, New Jersey"},{"key":"1872_CR6","unstructured":"H. R. Louren\u00e7o, O. Martin, T. St\u00fctzle (2010) Iterated local search: framework and applications, Handbook of Metaheuristics, 2nd. Edition. Kluwer Academic Publishers, International Series in Operations Research & Management Science Vol. 146, pp. 363\u2013397"},{"key":"1872_CR7","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/BF00940812","volume":"45","author":"V Cerny","year":"1985","unstructured":"Cerny V (1985) A thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J Optim Theory Appl 45:41\u201351","journal-title":"J Optim Theory Appl"},{"issue":"11","key":"1872_CR8","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1016\/S0305-0548(97)00031-2","volume":"24","author":"N Mladenovi\u2019c","year":"1997","unstructured":"Mladenovi\u2019c N, Hansen P (1997) Variable neighborhood search. Comput Oper Res 24(11):1097\u20131100","journal-title":"Comput Oper Res"},{"key":"1872_CR9","doi-asserted-by":"crossref","unstructured":"Feo TA, Resende MGC Greedy randomized adaptive search procedures. Journal of Global Optimization 6(109):1995","DOI":"10.1007\/BF01096763"},{"issue":"5","key":"1872_CR10","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/0305-0548(86)90048-1","volume":"13","author":"F Glover","year":"1986","unstructured":"Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13(5):533\u2013549","journal-title":"Comput Oper Res"},{"issue":"2","key":"1872_CR11","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/S0377-2217(98)00099-X","volume":"113","author":"C Voudouris","year":"1999","unstructured":"Voudouris C, Tsang E (1999) Guided local search and its application to the traveling salesman problem. Eur J Oper Res 113(2):469\u2013499","journal-title":"Eur J Oper Res"},{"key":"1872_CR12","unstructured":"Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press"},{"key":"1872_CR13","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks:1942\u20131948","DOI":"10.1109\/ICNN.1995.488968"},{"issue":"1","key":"1872_CR14","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/4235.585892","volume":"1","author":"M Dorigo","year":"1997","unstructured":"Dorigo M, Gambardella LM (Apr. 1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53\u201366","journal-title":"IEEE Trans Evol Comput"},{"key":"1872_CR15","unstructured":"D. T. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim M. and Zaidi, \"The Bees Algorithm, Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005"},{"key":"1872_CR16","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s10898-007-9149-x","volume":"39","author":"D Karaboga","year":"2007","unstructured":"Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459\u2013471","journal-title":"J Glob Optim"},{"key":"1872_CR17","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1177\/003754970107600201","volume":"76","author":"z w Geem","year":"2001","unstructured":"Geem z w, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60\u201368","journal-title":"Simulation"},{"key":"1872_CR18","doi-asserted-by":"crossref","unstructured":"X.-S. Yang (2009) Firefly algorithms for multimodal optimization. In Proceedings of the 5th international conference on stochastic algorithms: foundations and applications. Sapporo, Japan: Springer-Verlag, pp. 169\u201378","DOI":"10.1007\/978-3-642-04944-6_14"},{"key":"1872_CR19","doi-asserted-by":"crossref","unstructured":"Yang X-S and Deb S (2009) Cuckoo search via levy flights\", In Proceedings of the world congress on nature & biologically inspired computing (NaBIC-2009), Coimbatore, India, pp. 210\u2013214","DOI":"10.1109\/NABIC.2009.5393690"},{"issue":"1\/2","key":"1872_CR20","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1504\/IJBIC.2009.022775","volume":"1","author":"SH Hosseini","year":"2009","unstructured":"Hosseini SH (2009) The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. International Journal of Bio-Inspired Computing 1(1\/2):71\u201379","journal-title":"International Journal of Bio-Inspired Computing"},{"key":"1872_CR21","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.compstruc.2014.03.007","volume":"139","author":"MY Cheng","year":"2014","unstructured":"Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98\u2013112","journal-title":"Comput Struct"},{"key":"1872_CR22","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1007\/s10489-017-0903-6","volume":"47","author":"SJ Mousavirad","year":"2017","unstructured":"Mousavirad SJ, Ebrahimpour-Komleh H (2017) Human mental search: a new population-based metaheuristic optimization algorithm. Appl Intell 47:850\u2013887. https:\/\/doi.org\/10.1007\/s10489-017-0903-6","journal-title":"Appl Intell"},{"key":"1872_CR23","doi-asserted-by":"crossref","unstructured":"Sheng Xin Zhang, Wing Shing Chan, Zi Kang Peng, Shao Yong Zheng, Kit Sang Tang (2020) Selective-candidate framework with similarity selection rule for evolutionary optimization, Swarm and Evolutionary Computation, Volume 56","DOI":"10.1016\/j.swevo.2020.100696"},{"key":"1872_CR24","first-page":"4661","volume":"7","author":"E Atashpaz-Gargari","year":"2007","unstructured":"Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. IEEE Congress on Evolutionary Computation 7:4661\u20134666","journal-title":"IEEE Congress on Evolutionary Computation"},{"key":"1872_CR25","doi-asserted-by":"publisher","first-page":"1172","DOI":"10.1007\/s10489-019-01592-4","volume":"50","author":"H Wilde","year":"2020","unstructured":"Wilde H, Knight V, Gillard J (2020) Evolutionary dataset optimisation: learning algorithm quality through evolution. Appl Intell 50:1172\u20131191. https:\/\/doi.org\/10.1007\/s10489-019-01592-4","journal-title":"Appl Intell"},{"key":"1872_CR26","doi-asserted-by":"publisher","first-page":"12363","DOI":"10.1007\/s00521-020-04832-8","volume":"32","author":"A Slowik","year":"2020","unstructured":"Slowik A, Kwasnicka H (2020) Evolutionary algorithms and their applications to engineering problems. Neural Comput & Applic 32:12363\u201312379. https:\/\/doi.org\/10.1007\/s00521-020-04832-8","journal-title":"Neural Comput & Applic"},{"key":"1872_CR27","doi-asserted-by":"publisher","first-page":"2434","DOI":"10.1007\/s10489-018-1365-1","volume":"49","author":"G Dhiman","year":"2019","unstructured":"Dhiman G, Kumar V (2019) KnRVEA: A hybrid evolutionary algorithm based on knee points and reference vector adaptation strategies for many-objective optimization. Appl Intell 49:2434\u20132460. https:\/\/doi.org\/10.1007\/s10489-018-1365-1","journal-title":"Appl Intell"},{"key":"1872_CR28","doi-asserted-by":"publisher","first-page":"2954","DOI":"10.1007\/s10489-017-1122-x","volume":"48","author":"H Moradi","year":"2018","unstructured":"Moradi H, Ebrahimpour-Komleh H (2018) Development of a multi-objective optimization evolutionary algorithm based on educational systems. Appl Intell 48:2954\u20132966. https:\/\/doi.org\/10.1007\/s10489-017-1122-x","journal-title":"Appl Intell"},{"key":"1872_CR29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2015.05.001","volume":"24","author":"H Garg","year":"2015","unstructured":"Garg H (2015) An efficient biogeography based optimization algorithm for solving reliability optimization problems. Swarm and Evolutionary Computation 24:1\u201310","journal-title":"Swarm and Evolutionary Computation"},{"key":"1872_CR30","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.ins.2018.11.041","volume":"478","author":"H Garg","year":"2019","unstructured":"Garg H (2019) A hybrid GSA-GA algorithm for constrained optimization problems. Information Sciences, Volume 478:499\u2013523","journal-title":"Information Sciences, Volume"},{"key":"1872_CR31","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1016\/j.energy.2017.10.052","volume":"142","author":"RS Patwal","year":"2018","unstructured":"Patwal RS, Narang N, Garg H (2018) A novel TVAC-PSO based mutation strategies algorithm for generation scheduling of pumped storage hydrothermal system incorporating solar units. Energy 142:822\u2013837","journal-title":"Energy"},{"key":"1872_CR32","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.amc.2015.11.001","volume":"274","author":"H Garg","year":"2016","unstructured":"Garg H (2016) A hybrid PSO-GA algorithm for constrained optimization problems. Applied Mathematics and Computation 274:292\u2013305","journal-title":"Applied Mathematics and Computation"},{"key":"1872_CR33","doi-asserted-by":"crossref","unstructured":"Rezaee Jordehi A, Jasni J Parameter selection in particle swarm optimisation: a survey. J Exp Theor Artif Intell 25:527\u2013542","DOI":"10.1080\/0952813X.2013.782348"},{"issue":"5","key":"1872_CR34","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1016\/S1874-1029(11)60205-X","volume":"37","author":"A Alfi","year":"2011","unstructured":"Alfi A (2011) PSO with adaptive mutation and inertia weight and its application in parameter estimation of dynamic systems. Acta Automat Sin 37(5):541\u2013549","journal-title":"Acta Automat Sin"},{"key":"1872_CR35","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.asoc.2014.11.018","volume":"v","author":"L Zhang","year":"2015","unstructured":"Zhang L, Tang Y, Hua C et al (2015) A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques. Applied Soft Computing v:138\u2013149","journal-title":"Applied Soft Computing"},{"issue":"3","key":"1872_CR36","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1109\/TEVC.2004.826071","volume":"8","author":"A Ratnaweera","year":"2004","unstructured":"Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240\u2013255","journal-title":"IEEE Trans Evol Comput"},{"key":"1872_CR37","doi-asserted-by":"crossref","unstructured":"Yamaguchi T, Yasuda K (2006) Adaptive particle swarm optimization: self-coordinating mechanism with updating information, Proceedings of the 2006 IEEE International Conference on Systems, Man, and Cybernetics. 3:2303\u20132308","DOI":"10.1109\/ICSMC.2006.385206"},{"issue":"3","key":"1872_CR38","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/TEVC.2005.857610","volume":"10","author":"JJ Liang","year":"2006","unstructured":"Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281\u2013295","journal-title":"IEEE Trans Evol Comput"},{"key":"1872_CR39","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.camwa.2013.01.016","volume":"66","author":"M Pluhacek","year":"2013","unstructured":"Pluhacek M, Senkerik R, Davendra D, Oplatkova ZK, Zelinka I (2013) On the behavior and performance of chaos driven PSO algorithm with inertia weight. Comput Math Appl 66:122\u2013134","journal-title":"Comput Math Appl"},{"key":"1872_CR40","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.asoc.2014.10.026","volume":"26","author":"A Rezaee Jordehi","year":"2015","unstructured":"Rezaee Jordehi A (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":"1872_CR41","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.ins.2013.08.015","volume":"258","author":"Z Beheshti","year":"2014","unstructured":"Beheshti Z, Shamsuddin SMH (2014) CAPSO: a centripetal accelerated particle swarm optimization. Inf Sci 258:54\u201379","journal-title":"Inf Sci"},{"key":"1872_CR42","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.ijepes.2013.01.002","volume":"49","author":"V Hosseinnezhad","year":"2013","unstructured":"Hosseinnezhad V, Babaei E (2013) Economic load dispatch using PSO. Int J Electr Power Energy Syst 49:160\u2013169","journal-title":"Int J Electr Power Energy Syst"},{"issue":"5","key":"1872_CR43","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1049\/iet-gtd.2009.0639","volume":"4","author":"JM Morales","year":"2010","unstructured":"Morales JM, Baringo L, Conejo AJ, Minguez R (2010) Probabilistic load flow with correlated wind sources. IET Generation, Transmission & Distribution 4(5):641\u2013651","journal-title":"IET Generation, Transmission & Distribution"},{"key":"1872_CR44","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.energy.2016.07.055","volume":"113","author":"C Gallego-Castillo","year":"2016","unstructured":"Gallego-Castillo C, Bessa R, Cavalcante L, Lopez-Garcia O (2016) On-line quantile regression in the RKHS (reproducing kernel Hilbert space) for operational probabilistic forecasting of wind power. Energy 113:355\u2013365","journal-title":"Energy"},{"key":"1872_CR45","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/j.energy.2018.01.080","volume":"147","author":"VAC Medellin","year":"2018","unstructured":"Medellin VAC, Hidalgo IG, Correia PB (2018) Probabilistic valuation for power generation projects from sugarcane in reserve energy auctions. Energy 147:603\u2013611","journal-title":"Energy"},{"key":"1872_CR46","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1016\/j.apenergy.2017.11.101","volume":"211","author":"MJ Morshed","year":"2018","unstructured":"Morshed MJ, Hmida JB, Fekih A (2018) A probabilistic multi-objective approach for load flow optimization in hybrid wind-PV-PEV systems. Appl Energy 211:1136\u20131149","journal-title":"Appl Energy"},{"key":"1872_CR47","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.energy.2019.01.021","volume":"171","author":"TT Nguyen","year":"2019","unstructured":"Nguyen TT (2019) A high performance social spider optimization algorithm for optimal load flow solution with single objective optimization. Energy 171:218\u2013240","journal-title":"Energy"},{"key":"1872_CR48","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.apenergy.2018.09.165","volume":"231","author":"Q Xiao","year":"2018","unstructured":"Xiao Q, Zhou S (2018) Probabilistic load flow computation considering correlated wind speeds. Appl Energy 231:677\u2013685","journal-title":"Appl Energy"},{"issue":"3","key":"1872_CR49","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1109\/TPAS.1974.293973","volume":"PAS-93","author":"B Borkowska","year":"1974","unstructured":"Borkowska B (1974) Probabilistic load flow. IEEE Transactions on Power Apparatus and Systems PAS-93(3):752\u2013759","journal-title":"IEEE Transactions on Power Apparatus and Systems"},{"key":"1872_CR50","unstructured":"G.K. Stefopoulos, A.P. Meliopoulos, G.J. Cokkinids (2004) Probabilistic load flow with non conforming electric loads\u2019. Proc. of the Eighth Int. Conf. on Probabilistic Methods Applied to Power Systems, pp. 525\u2013531"},{"key":"1872_CR51","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.renene.2014.11.028","volume":"76","author":"G Carpinelli","year":"2015","unstructured":"Carpinelli G, Caramia P, Varilone P (2015) Multi-linear Monte Carlo simulation method for probabilistic load flow of distribution systems with wind and photovoltaic generation systems. Renew Energy 76:283\u2013295","journal-title":"Renew Energy"},{"key":"1872_CR52","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/TPWRS.2009.2016589","volume":"24","author":"H Yu","year":"2009","unstructured":"Yu H, Chung CY, Wong KP, Lee HW, Zhang JH (2009) Probabilistic load flow evaluation with hybrid Latin hypercube sampling and Cholesky decomposition. IEEE Trans Power Syst 24:661\u2013667","journal-title":"IEEE Trans Power Syst"},{"key":"1872_CR53","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1049\/piee.1977.0027","volume":"124","author":"RN Allan","year":"1977","unstructured":"Allan RN, Al-Shakarchi MRG (1977) Probabilistic techniques in A.C. load-flow analysis. Proceedings of the Institution of Electrical Engineers 124:154\u2013160","journal-title":"Proceedings of the Institution of Electrical Engineers"},{"key":"1872_CR54","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1109\/TPWRS.2003.818743","volume":"19","author":"P Zhang","year":"2004","unstructured":"Zhang P, Lee ST (2004) Probabilistic load flow computation using the method of combined cumulants and gram-Charlier expansion. IEEE Trans Power Syst 19:676\u2013682","journal-title":"IEEE Trans Power Syst"},{"key":"1872_CR55","doi-asserted-by":"crossref","first-page":"2251","DOI":"10.1109\/TPWRS.2012.2190533","volume":"27","author":"M Fan","year":"2012","unstructured":"Fan M, Vittal V, Heydt GT, Ayyanar R (2012) Probabilistic load flow studies for transmission systems with photovoltaic generation using Cumulants. IEEE Trans Power Syst 27:2251\u20132261","journal-title":"IEEE Trans Power Syst"},{"key":"1872_CR56","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1109\/TPWRS.2005.857921","volume":"20","author":"CL Su","year":"2005","unstructured":"Su CL (2005) Probabilistic load-flow computation using point estimate method. IEEE Trans Power Syst 20:1843\u20131851","journal-title":"IEEE Trans Power Syst"},{"issue":"4","key":"1872_CR57","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.1109\/TPWRS.2006.881146","volume":"21","author":"G Verbic","year":"2006","unstructured":"G. Verbic, , C.A. Canizares, \u2018Probabilistic optimal load flow in electricity markets based on a two-point estimate method\u2019, IEEE Trans Power Syst, vol. 21, no. 4, pp. 1883\u20131893, 2006","journal-title":"IEEE Trans Power Syst"},{"key":"1872_CR58","doi-asserted-by":"crossref","unstructured":"I. S. Arneja, B. Venkatesh (2012) Probabilistic OPF using linear fuzzy relation. Conf on Power & Energy, Ho Chi Minh City, pp. 601\u2013605","DOI":"10.1109\/ASSCC.2012.6523336"},{"key":"1872_CR59","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/j.apenergy.2017.02.002","volume":"191","author":"X Fu","year":"2017","unstructured":"Fu X, Sun H, Guo Q, Pan Z, Zhang X, Zeng S (2017) Probabilistic load flow analysis considering the dependence between power and heat. Applied Energy 191:582\u2013592","journal-title":"Applied Energy"},{"issue":"11","key":"1872_CR60","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1049\/iet-gtd.2014.0570","volume":"9","author":"N Nikmehr","year":"2015","unstructured":"Nikmehr N, Najafi Ravadanegh S (2015) Heuristic probabilistic load flow algorithm for microgrids operation and planning. IET Generation, Transmission & Distribution 9(11):985\u2013995","journal-title":"IET Generation, Transmission & Distribution"},{"key":"1872_CR61","unstructured":"Liang J, Qu B, Suganthan P (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"},{"key":"1872_CR62","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1061\/(ASCE)CP.1943-5487.0000163","volume":"26","author":"M-Y Cheng","year":"2012","unstructured":"Cheng M-Y, Lien L-C (2012) Hybrid artificial intelligence-based PBA for benchmark functions and facility layout design optimization. J Comput Civ Eng 26:612\u2013624","journal-title":"J Comput Civ Eng"},{"key":"1872_CR63","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1007\/s10732-008-9080-4","volume":"15","author":"S Garc\u00eda","year":"2009","unstructured":"Garc\u00eda S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms\u2019 behavior: a case study on the CEC\u20192005 special session on real parameter optimization. J Heuristics 15:617\u2013644","journal-title":"J Heuristics"},{"key":"1872_CR64","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.apm.2018.06.036","volume":"63","author":"J Zhang","year":"2018","unstructured":"Zhang J, Xiao M, Gao L, Pan Q (2018) Queuing search algorithm: a novel metaheuristic algorithm for solving engineering optimization problems. Appl Math Model 63:464\u2013490","journal-title":"Appl Math Model"},{"key":"1872_CR65","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2014.07.025","volume":"75","author":"H Salimi","year":"2015","unstructured":"Salimi H (2015) Stochastic fractal search: a powerful metaheuristic algorithm. Knowl Based Syst 75:1\u201318","journal-title":"Knowl Based Syst"},{"key":"1872_CR66","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1109\/TEVC.2008.919004","volume":"12","author":"D Simon","year":"2008","unstructured":"Simon D (2008) Biogeography-based optimization. IEEE Trans Evolut Comput 12:702\u2013713","journal-title":"IEEE Trans Evolut Comput"},{"issue":"1","key":"1872_CR67","doi-asserted-by":"crossref","first-page":"63","DOI":"10.13164\/mendel.2018.1.063","volume":"24","author":"A Kazikova","year":"2018","unstructured":"Kazikova A, Pluhacek M, Senkerik R (2018) Regarding the behavior of Bison runners within the Bison algorithm. MENDEL. 24(1):63\u201370","journal-title":"MENDEL."},{"key":"1872_CR68","doi-asserted-by":"crossref","unstructured":"Yang X-S, Deb S (2009) Cuckoo search via Levy flights. In: Proceedings of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), December 2009, India, USA, pp. 210\u2013214. IEEE Publications","DOI":"10.1109\/NABIC.2009.5393690"},{"key":"1872_CR69","volume-title":"Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective bound constrained real-parameter numerical optimization, technical report","author":"NH Awad","year":"2016","unstructured":"Awad NH, Ali MZ, Liang JJ, Qu BY, Suganthan PN (2016) Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective bound constrained real-parameter numerical optimization, technical report. Nanyang Technological University, Singapore"},{"key":"1872_CR70","volume-title":"Artificial intelligence and algorithms in intelligent systems., CSOC2018 2018. Advances in intelligent systems and computing","author":"A Kazikova","year":"2019","unstructured":"Kazikova A, Pluhacek M, Senkerik R (2019) Performance of the Bison algorithm on benchmark IEEE CEC 2017. In: Silhavy R (ed) Artificial intelligence and algorithms in intelligent systems., CSOC2018 2018. Advances in intelligent systems and computing, vol 764. Springer, Cham"},{"issue":"10","key":"1872_CR71","first-page":"1039","volume":"39","author":"X Zhang","year":"2005","unstructured":"Zhang X, Du Y, Qin G, Qin Z (2005) Adaptive particle swarm algorithm with dynamically changing inertia weight. J Xi'an Jiaotong Univ 39(10):1039\u20131042","journal-title":"J Xi'an Jiaotong Univ"},{"issue":"10","key":"1872_CR72","first-page":"1552","volume":"42","author":"D-F Wang","year":"2016","unstructured":"Wang D-F, Meng L (2016) Performance analysis and parameter selection of PSO algorithms. Acta Automat Sin 42(10):1552\u20131561","journal-title":"Acta Automat Sin"},{"issue":"2","key":"1872_CR73","first-page":"6","volume":"36","author":"JQ Tong","year":"2019","unstructured":"Tong JQ, Zhao Q, Li M (2019) Particle swarm optimization algorithm based on adaptive dynamic change. Microletronics & Computer 36(2):6\u201310","journal-title":"Microletronics & Computer"},{"key":"1872_CR74","first-page":"1779","volume":"10","author":"GJ Jiang","year":"2015","unstructured":"Jiang GJ, Ye H, Ma YH (2015) Particle swarm optimization algorithm via sampling strategy. Control and Decision 10:1779\u20131784","journal-title":"Control and Decision"},{"key":"1872_CR75","doi-asserted-by":"publisher","unstructured":"L. Sun, X. Song, Tianfei Chen (2019) An improved convergence particle swarm optimization algorithm with random sampling of control parameters. Journal of Control Science and Engineering, Article in press, https:\/\/doi.org\/10.1155\/2019\/7478498","DOI":"10.1155\/2019\/7478498"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01872-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-020-01872-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01872-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,4]],"date-time":"2021-10-04T20:34:58Z","timestamp":1633379698000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-020-01872-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,3]]},"references-count":75,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["1872"],"URL":"https:\/\/doi.org\/10.1007\/s10489-020-01872-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,3]]},"assertion":[{"value":"3 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}