{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T22:14:40Z","timestamp":1769552080445,"version":"3.49.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T00:00:00Z","timestamp":1652918400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T00:00:00Z","timestamp":1652918400000},"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":["Artif Intell Rev"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s10462-022-10201-9","type":"journal-article","created":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T11:24:38Z","timestamp":1652959478000},"page":"1297-1317","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["On the comparative performance of recent swarm intelligence based algorithms for optimization of real-life Sterling cycle operated refrigeration\/liquefaction system"],"prefix":"10.1007","volume":"56","author":[{"given":"Bansi D.","family":"Raja","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7508-186X","authenticated-orcid":false,"given":"Vivek K.","family":"Patel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vimal J.","family":"Savsani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali R\u0131za","family":"Y\u0131ld\u0131z","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,19]]},"reference":[{"key":"10201_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2021.107250","volume":"157","author":"L Abualigah","year":"2021","unstructured":"Abualigah L, Yousri D, Abd-Elaziz M, Ewees AA, Al-qaness MA, Gandomi AH (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250","journal-title":"Comput Ind Eng"},{"key":"10201_CR2","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.enconman.2014.03.033","volume":"82","author":"MH Ahmadi","year":"2014","unstructured":"Ahmadi MH, Ahmadi MA, Mohammadi AH, Feidt M, Pourkiaei SM (2014) Multi-objective optimization of an irreversible stirling cryogenic refrigerator cycle. Energy Convers Manag 82:351\u2013360","journal-title":"Energy Convers Manag"},{"key":"10201_CR3","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.rser.2017.04.097","volume":"78","author":"MH Ahmadi","year":"2017","unstructured":"Ahmadi MH, Ahmadi MA, Maleki A, Pourfayaz F, Bidi M, A\u00e7\u00fdkkalp E (2017a) Exergetic sustainability evaluation and multi-objective optimization of performance of an irreversible nanoscale Stirling refrigeration cycle operating with Maxwell Boltzmann gas. Renew Sustain Energy Rev 78:80\u201392","journal-title":"Renew Sustain Energy Rev"},{"key":"10201_CR4","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.physa.2017.04.079","volume":"483","author":"MH Ahmadi","year":"2017","unstructured":"Ahmadi MH, Nabakhteh MA, Ahmadi MA, Pourfayaz F, Bidi M (2017b) Investigation and optimization of performance of nano-scale Stirling refrigerator using working fluid as Maxwell-Boltzmann gases. Physica A 483:337\u2013350","journal-title":"Physica A"},{"key":"10201_CR5","doi-asserted-by":"publisher","first-page":"2237","DOI":"10.1007\/s10462-019-09732-5","volume":"53","author":"HA Alsattar","year":"2020","unstructured":"Alsattar HA, Zaidan AA, Zaidan BB (2020) Novel meta-heuristic bald eagle search optimization algorithm. Artif Intell Rev 53:2237\u20132264","journal-title":"Artif Intell Rev"},{"key":"10201_CR6","doi-asserted-by":"crossref","unstructured":"Ameca-Alducin MY, Hasani-Shoreh M, Blaikie W, Neumann F, Mezura-Montes E (2018) A comparison of constraint handling techniques for dynamic constrained optimization problems. In: IEEE congress on evolutionary computation (CEC), pp 1\u20138","DOI":"10.1109\/CEC.2018.8477750"},{"key":"10201_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compstruc.2016.03.001","volume":"169","author":"A Askarzadeh","year":"2016","unstructured":"Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1\u201312","journal-title":"Comput Struct"},{"key":"10201_CR8","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.ijrefrig.2018.05.024","volume":"91","author":"A Batooei","year":"2018","unstructured":"Batooei A, Keshavarz A (2018) A gamma type stirling refrigerator optimization: an experimental and analytical investigation. Int J Refrig 91:89\u2013100","journal-title":"Int J Refrig"},{"key":"10201_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.109738","volume":"135","author":"JS Chou","year":"2020","unstructured":"Chou JS, Truong DN (2020) Multi-objective optimization inspired by behaviour of jellyfish for solving structural design problems. Chaos Solitons Fractals 135:109738","journal-title":"Chaos Solitons Fractals"},{"key":"10201_CR10","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.advengsoft.2017.05.014","volume":"114","author":"G Dhiman","year":"2017","unstructured":"Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48\u201370","journal-title":"Adv Eng Softw"},{"key":"10201_CR11","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.knosys.2018.11.024","volume":"165","author":"G Dhiman","year":"2019","unstructured":"Dhiman G, Kumar V (2019) Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems. Knowl-Based Syst 165:169\u2013196","journal-title":"Knowl-Based Syst"},{"key":"10201_CR12","unstructured":"Dorigo M, Maniezzo V, Colorni A (1991) Positive feedback as a search strategy, Technical Report 91\u2013016, Politecnico di Milano, Italy"},{"key":"10201_CR13","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1061\/(ASCE)0733-9496(2003)129:3(210)","volume":"129","author":"MM Eusuff","year":"2003","unstructured":"Eusuff MM, Lansey KE (2003) Optimization of water distribution network design using the shuffled frog leaping algorithm. J Water Resour Plan Manag 129:210\u2013225","journal-title":"J Water Resour Plan Manag"},{"key":"10201_CR14","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 (2020) Marine Predators Algorithm: A Nature-inspired Metaheuristic. Expert Syst Appl 152:113377","journal-title":"Expert Syst Appl"},{"key":"10201_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2019.103249","volume":"87","author":"V Hayyolalam","year":"2020","unstructured":"Hayyolalam V, Kazem AA (2020) Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems. Eng Appl Artif Intell 87:103249","journal-title":"Eng Appl Artif Intell"},{"key":"10201_CR16","first-page":"29","volume":"8","author":"V Hayyolalam","year":"2017","unstructured":"Hayyolalam V, Pourhaji-Kazem AA (2017) QoS-aware optimization of cloud service composition using symbiotic organisms search algorithm. J Intell Proc Electr Technol 8:29\u201338","journal-title":"J Intell Proc Electr Technol"},{"key":"10201_CR17","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, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849\u2013872","journal-title":"Futur Gener Comput Syst"},{"key":"10201_CR18","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1016\/j.asoc.2017.09.035","volume":"62","author":"E Jahani","year":"2018","unstructured":"Jahani E, Chizari M (2018) Tackling global optimization problems with a novel algorithm\u2013Mouth Brooding Fish algorithm. Appl Soft Comput 62:987\u20131002","journal-title":"Appl Soft Comput"},{"key":"10201_CR19","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.swevo.2018.02.013","volume":"44","author":"M Jain","year":"2019","unstructured":"Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol Comput 44:148\u2013175","journal-title":"Swarm Evol Comput"},{"key":"10201_CR20","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.swevo.2011.02.002","volume":"1","author":"D Joaqu\u00edn","year":"2011","unstructured":"Joaqu\u00edn D, Salvador G, Daniel M, Francisco H (2011) A practical tutorial on the use of non parametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1:3\u201318","journal-title":"Swarm Evol Comput"},{"key":"10201_CR21","unstructured":"Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, Technical report\u2013TR06, Erciyes University, Engineering Faculty, Computer Engineering Department"},{"key":"10201_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103541","volume":"90","author":"S Kaur","year":"2020","unstructured":"Kaur S, Awasthi LK, Sangal AL, Dhiman G (2020) Tunicate Swarm algorithm: a new bio-inspired based metaheuristic paradigm for global optimization. Eng Appl Artif Intell 90:103541","journal-title":"Eng Appl Artif Intell"},{"key":"10201_CR23","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart RC (1995) Particle swarm optimization, In: Proceedings of the 1995 IEEE international conference on neural networks, Perth, Australia, pp 1942\u20131948","DOI":"10.1109\/ICNN.1995.488968"},{"key":"10201_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113338","volume":"149","author":"M Khishe","year":"2020","unstructured":"Khishe M, Mosavi MR (2020) Chimp optimization algorithm. Expert Syst Appl 149:113338","journal-title":"Expert Syst Appl"},{"key":"10201_CR25","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1109\/TEVC.2009.2033582","volume":"14","author":"R Mallipeddi","year":"2010","unstructured":"Mallipeddi R, Suganthan PN (2010) Ensemble of constraint handling techniques. IEEE Trans Evol Comput 14:561\u2013579","journal-title":"IEEE Trans Evol Comput"},{"key":"10201_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 (2015a) The ant lion optimizer. Adv Eng Softw 83:80\u201398","journal-title":"Adv Eng Softw"},{"key":"10201_CR27","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 (2015b) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228\u2013249","journal-title":"Knowl-Based Syst"},{"key":"10201_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 (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27:1053\u20131073","journal-title":"Neural Comput Appl"},{"key":"10201_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","journal-title":"Adv Eng Softw"},{"key":"10201_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","journal-title":"Adv Eng Softw"},{"key":"10201_CR31","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.advengsoft.2017.07.002","volume":"114","author":"S Mirjalili","year":"2017","unstructured":"Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163\u2013191","journal-title":"Adv Eng Softw"},{"key":"10201_CR32","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1007\/s10489-017-1019-8","volume":"48","author":"SZ Mirjalili","year":"2018","unstructured":"Mirjalili SZ, Mirjalili S, Saremi S, Faris H, Aljarah I (2018) Grasshopper optimization algorithm for multi-objective optimization problems. Appl Intell 48:805\u2013820","journal-title":"Appl Intell"},{"key":"10201_CR33","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/j.asej.2019.10.004","volume":"11","author":"AA Mohamed","year":"2020","unstructured":"Mohamed AA, Hassan SA, Hemeida AM, Alkhalaf S, Mahmoud MM, Eldin AM (2020) Parasitism-Predation algorithm (PPA): a novel approach for feature selection. Ain Shams Eng J 11:293\u2013308","journal-title":"Ain Shams Eng J"},{"key":"10201_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.107050","volume":"152","author":"A Mohammadi-Balani","year":"2021","unstructured":"Mohammadi-Balani A, Nayeri MD, Azar A, Taghizadeh-Yazdi M (2021) Golden eagle optimizer: a nature-inspired metaheuristic algorithm. Comput Ind Eng 152:107050","journal-title":"Comput Ind Eng"},{"key":"10201_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-021-01438-z","author":"I Naruei","year":"2021","unstructured":"Naruei I, Keynia F (2021a) Wild horse optimizer: a new meta-heuristic algorithm for solving engineering optimization problems. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-021-01438-z","journal-title":"Eng Comput"},{"key":"10201_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115352","volume":"183","author":"I Naruei","year":"2021","unstructured":"Naruei I, Keynia F (2021b) A new optimization method based on coot bird natural life model. Expert Syst Appl 183:115352","journal-title":"Expert Syst Appl"},{"key":"10201_CR37","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1109\/TEVC.2011.2180533","volume":"16","author":"TT Nguyen","year":"2012","unstructured":"Nguyen TT, Yao X (2012) Continuous dynamic constrained optimization: the challenges. IEEE Trans Evol Comput 16:769\u2013786","journal-title":"IEEE Trans Evol Comput"},{"key":"10201_CR38","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1109\/MCS.2002.1004010","volume":"22","author":"K Passino","year":"2002","unstructured":"Passino K (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22:52\u201367","journal-title":"IEEE Control Syst Mag"},{"key":"10201_CR39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-10477-1","volume-title":"Thermal system optimization: a population-based metaheuristic approach","author":"VK Patel","year":"2019","unstructured":"Patel VK, Savsani VJ, Tawhid MA (2019) Thermal system optimization: a population-based metaheuristic approach. Springer, Switzerland"},{"key":"10201_CR40","unstructured":"Pham D, Ghanbarzadeh A, Koc E, Rahim S, Zaidi M (2005) The bees algorithm: technical note, Technical report, Manufacturing engineering centre. Cardiff University, Cardiff, UK"},{"key":"10201_CR41","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.engappai.2019.01.001","volume":"80","author":"S Shadravan","year":"2019","unstructured":"Shadravan S, Naji HR, Bardsiri VK (2019) The Sailfish Optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Eng Appl Artif Intell 80:20\u201334","journal-title":"Eng Appl Artif Intell"},{"key":"10201_CR42","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1080\/21642583.2019.1708830","volume":"8","author":"J Xue","year":"2020","unstructured":"Xue J, Shen B (2020) A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Control Eng 8:22\u201334","journal-title":"Syst Sci Control Eng"},{"key":"10201_CR43","first-page":"128","volume-title":"Firefly Algorithm, nature-inspired metaheuristic algorithms","author":"XS Yang","year":"2008","unstructured":"Yang XS (2008) Firefly Algorithm, nature-inspired metaheuristic algorithms. Luniver Press, Beckington, pp 128\u2013138"},{"key":"10201_CR44","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/978-3-642-12538-6_6","volume-title":"Nature inspired cooperative strategies for optimization","author":"XS Yang","year":"2010","unstructured":"Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Gonz\u00e1lez JR, Sancho-Royo A, Pelta DA, Cruz C (eds) Nature inspired cooperative strategies for optimization. Springer, Berlin\/Heidelberg, pp 65\u201374"},{"key":"10201_CR45","first-page":"330","volume":"1","author":"XS Yang","year":"2010","unstructured":"Yang XS, Deb S (2010) Engineering optimization by cuckoo search. Int J Math Modell Numer Optim 1:330\u2013343","journal-title":"Int J Math Modell Numer Optim"},{"key":"10201_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106559","volume":"145","author":"K Zervoudakis","year":"2020","unstructured":"Zervoudakis K, Tsafarakis S (2020) A mayfly optimization algorithm. Comput Ind Eng 145:106559","journal-title":"Comput Ind Eng"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-022-10201-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-022-10201-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-022-10201-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T14:26:33Z","timestamp":1727274393000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-022-10201-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,19]]},"references-count":46,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["10201"],"URL":"https:\/\/doi.org\/10.1007\/s10462-022-10201-9","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,19]]},"assertion":[{"value":"26 April 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}