{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T20:20:28Z","timestamp":1780086028233,"version":"3.54.0"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The support of the special project for collaborative innovation of science and technology in 2021","award":["202121206"],"award-info":[{"award-number":["202121206"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Evol. Intel."],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s12065-024-01002-w","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T05:14:05Z","timestamp":1736399645000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi-strategy enhanced artificial rabbit optimization algorithm for solving engineering optimization problems"],"prefix":"10.1007","volume":"18","author":[{"given":"Ni-ni","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1367-5886","authenticated-orcid":false,"given":"Wen-chuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,9]]},"reference":[{"key":"1002_CR1","volume":"195","author":"W-c Wang","year":"2024","unstructured":"Wang W-c, Tian W-c, Xu D-m, Zang H-f (2024) Arctic puffin optimization: a bio-inspired metaheuristic algorithm for solving engineering design optimization. Adv Eng Softw 195:103694","journal-title":"Adv Eng Softw"},{"key":"1002_CR2","doi-asserted-by":"crossref","first-page":"5923","DOI":"10.1007\/s00500-017-2810-5","volume":"22","author":"X-S Yang","year":"2018","unstructured":"Yang X-S, Deb S, Zhao Y-X, Fong S, He X (2018) Swarm intelligence: past, present and future. Soft Comput 22:5923\u20135933","journal-title":"Soft Comput"},{"key":"1002_CR3","volume":"241","author":"B Sujoy","year":"2024","unstructured":"Sujoy B, Adel M (2024) L\u00e9vy Arithmetic Algorithm: an enhanced metaheuristic algorithm and its application to engineering optimization. Expert Syst Appl 241:122335","journal-title":"Expert Syst Appl"},{"key":"1002_CR4","doi-asserted-by":"crossref","first-page":"113106","DOI":"10.1016\/j.measurement.2023.113106","volume":"218","author":"G Xu","year":"2023","unstructured":"Xu G, Wang X (2023) Support vector regression optimized by black widow optimization algorithm combining with feature selection by MARS for mining blast vibration prediction. Measurement 218:113106","journal-title":"Measurement"},{"key":"1002_CR5","doi-asserted-by":"crossref","first-page":"113308","DOI":"10.1016\/j.eswa.2020.113308","volume":"149","author":"Anita","year":"2020","unstructured":"Anita, Yadav A, Kumar N (2020) Artificial electric field algorithm for engineering optimization problems. Expert Syst Appl 149:113308","journal-title":"Expert Syst Appl"},{"key":"1002_CR6","doi-asserted-by":"crossref","first-page":"235","DOI":"10.3390\/biomimetics8020235","volume":"8","author":"M Xu","year":"2023","unstructured":"Xu M, Cao L, Lu D, Hu Z, Yue Y (2023) Application of swarm intelligence optimization algorithms in image processing: a comprehensive review of analysis, synthesis, and optimization. Biomimetics 8:235","journal-title":"Biomimetics"},{"key":"1002_CR7","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2020.109945","volume":"216","author":"F Rosso","year":"2020","unstructured":"Rosso F, Ciancio V, Dell\u2019Olmo J, Salata F (2020) Multi-objective optimization of building retrofit in the Mediterranean climate by means of genetic algorithm application. Energy Build 216:109945","journal-title":"Energy Build"},{"key":"1002_CR8","volume":"164","author":"M Zhong","year":"2023","unstructured":"Zhong M, Wen J, Ma J, Cui H, Zhang Q, Parizi MK (2023) A hierarchical multi-leadership sine cosine algorithm to dissolving global optimization and data classification: The COVID-19 case study. Comput Biol Med 164:107212","journal-title":"Comput Biol Med"},{"key":"1002_CR9","first-page":"137","volume":"11","author":"M Karimzadeh Parizi","year":"2020","unstructured":"Karimzadeh Parizi M, Keynia F, Khatibi Bardsiri A (2020) Woodpecker Mating Algorithm (WMA): a nature-inspired algorithm for solving optimization problems. Int J Nonlinear Anal Appl 11:137\u2013157","journal-title":"Int J Nonlinear Anal Appl"},{"key":"1002_CR10","doi-asserted-by":"crossref","first-page":"919","DOI":"10.3233\/JIFS-201075","volume":"40","author":"MK Parizi","year":"2021","unstructured":"Parizi MK, Keynia F, Bardsiri AK (2021) OWMA: An improved self-regulatory woodpecker mating algorithm using opposition-based learning and allocation of local memory for solving optimization problems. J Intell Fuzzy Syst 40:919\u2013946","journal-title":"J Intell Fuzzy Syst"},{"key":"1002_CR11","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1142\/S0219622021500176","volume":"20","author":"MK Parizi","year":"2021","unstructured":"Parizi MK, Keynia F, Bardsiri AK (2021) HSCWMA: a new hybrid SCA-WMA algorithm for solving optimization problems. Int J Inf Technol Decis Mak 20:775\u2013808","journal-title":"Int J Inf Technol Decis Mak"},{"key":"1002_CR12","first-page":"221","volume":"4","author":"M Karimzadeh Parizi","year":"2021","unstructured":"Karimzadeh Parizi M, Keynia F, Khatibi Bardsiri A (2021) Woodpecker mating algorithm for optimal economic load dispatch in a power system with conventional generators. Int J Ind Electron Control Optim 4:221\u2013234","journal-title":"Int J Ind Electron Control Optim"},{"key":"1002_CR13","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1142\/S0219622022500675","volume":"22","author":"J Zhang","year":"2023","unstructured":"Zhang J, Li H, Parizi MK (2023) HWMWOA: a Hybrid WMA-WOA algorithm with adaptive Cauchy mutation for global optimization and data classification. Int J Inf Technol Decis Mak 22:1195\u20131252","journal-title":"Int J Inf Technol Decis Mak"},{"key":"1002_CR14","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1080\/02533839.2022.2078418","volume":"45","author":"J Gonga","year":"2022","unstructured":"Gonga J, Parizi MK (2022) GWMA: the parallel implementation of woodpecker mating algorithm on the GPU. J Chin Inst Eng 45:556\u2013568","journal-title":"J Chin Inst Eng"},{"key":"1002_CR15","doi-asserted-by":"crossref","first-page":"104314","DOI":"10.1016\/j.engappai.2021.104314","volume":"104","author":"H Zamani","year":"2021","unstructured":"Zamani H, Nadimi-Shahraki MH, Gandomi AH (2021) QANA: Quantum-based avian navigation optimizer algorithm. Eng Appl Artif Intell 104:104314","journal-title":"Eng Appl Artif Intell"},{"key":"1002_CR16","doi-asserted-by":"crossref","first-page":"114616","DOI":"10.1016\/j.cma.2022.114616","volume":"392","author":"H Zamani","year":"2022","unstructured":"Zamani H, Nadimi-Shahraki MH, Gandomi AH (2022) Starling murmuration optimizer: a novel bio-inspired algorithm for global and engineering optimization. Comput Methods Appl Mech Eng 392:114616","journal-title":"Comput Methods Appl Mech Eng"},{"key":"1002_CR17","first-page":"1","volume":"29","author":"X Wang","year":"2022","unstructured":"Wang X, Hu H, Liang Y, Zhou L (2022) On the mathematical models and applications of swarm intelligent optimization algorithms. Arch Comput Methods Eng 29:1\u201328","journal-title":"Arch Comput Methods Eng"},{"key":"1002_CR18","doi-asserted-by":"crossref","first-page":"105082","DOI":"10.1016\/j.engappai.2022.105082","volume":"114","author":"L Wang","year":"2022","unstructured":"Wang L, Cao Q, Zhang Z, Mirjalili S, Zhao W (2022) Artificial rabbits optimization: a new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell 114:105082","journal-title":"Eng Appl Artif Intell"},{"key":"1002_CR19","doi-asserted-by":"crossref","first-page":"105908","DOI":"10.1016\/j.bspc.2023.105908","volume":"90","author":"Y Gao","year":"2024","unstructured":"Gao Y, Hosseinzadeh H (2024) Leveraging an optimized deep belief network based on a developed version of artificial rabbits optimization for breast tumor diagnosis. Biomed Signal Process Control 90:105908","journal-title":"Biomed Signal Process Control"},{"key":"1002_CR20","first-page":"14","volume":"32","author":"A Mohammad","year":"2023","unstructured":"Mohammad A, Jamil AS, Mouhammd A, Maen A, Khaled A (2023) DT-ARO: decision tree-based artificial rabbits optimization to mitigate IoT botnet exploitation. J Netw Syst Manag 32:14","journal-title":"J Netw Syst Manag"},{"key":"1002_CR21","doi-asserted-by":"crossref","first-page":"4912","DOI":"10.3390\/math11244912","volume":"11","author":"ASA Bayoumi","year":"2023","unstructured":"Bayoumi ASA, Sehiemy RAE, Badawy M, Elhosseini M, Aljohani M, Abaza A (2023) Optimizing multi-layer perovskite solar cell dynamic models with hysteresis consideration using artificial rabbits optimization. Mathematics 11:4912","journal-title":"Mathematics"},{"key":"1002_CR22","doi-asserted-by":"crossref","first-page":"100355","DOI":"10.1016\/j.dajour.2023.100355","volume":"9","author":"RM Rizk-Allah","year":"2023","unstructured":"Rizk-Allah RM, Serdar E, Davut I (2023) An improved artificial rabbits optimization for accurate and efficient infinite impulse response system identification. Decis Anal J 9:100355","journal-title":"Decis Anal J"},{"key":"1002_CR23","volume":"238","author":"O Burcin","year":"2024","unstructured":"Burcin O, Serhat D, Tolga KH, Ugur G (2024) Optimal solution of the combined heat and power economic dispatch problem by adaptive fitness-distance balance based artificial rabbits optimization algorithm. Expert Syst Appl 238:122272","journal-title":"Expert Syst Appl"},{"key":"1002_CR24","volume":"419","author":"G Hu","year":"2024","unstructured":"Hu G, Huang F, Chen K, Wei G (2024) MNEARO: A meta swarm intelligence optimization algorithm for engineering applications. Comput Methods Appl Mech Eng 419:116664","journal-title":"Comput Methods Appl Mech Eng"},{"key":"1002_CR25","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122460","volume":"240","author":"H Bakir","year":"2024","unstructured":"Bakir H (2024) Dynamic fitness-distance balance-based artificial rabbits optimization algorithm to solve optimal power flow problem. Expert Syst Appl 240:122460","journal-title":"Expert Syst Appl"},{"key":"1002_CR26","doi-asserted-by":"crossref","first-page":"2703","DOI":"10.3390\/pr10122703","volume":"10","author":"Y Wang","year":"2022","unstructured":"Wang Y, Xiao Y, Guo Y, Li J (2022) Dynamic chaotic opposition-based learning-driven hybrid Aquila optimizer and artificial rabbits optimization algorithm: framework and applications. Processes 10:2703","journal-title":"Processes"},{"key":"1002_CR27","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107154","volume":"163","author":"M Abd Elaziz","year":"2023","unstructured":"Abd Elaziz M, Dahou A, Mabrouk A, El-Sappagh S, Aseeri AO (2023) An efficient artificial rabbits optimization based on mutation strategy for skin cancer prediction. Comput Biol Med 163:107154","journal-title":"Comput Biol Med"},{"key":"1002_CR28","doi-asserted-by":"crossref","first-page":"243","DOI":"10.3390\/biomimetics8020243","volume":"8","author":"Q Cao","year":"2023","unstructured":"Cao Q, Wang L, Zhao W, Yuan Z, Liu A, Gao Y, Ye R (2023) Vibration state identification of hydraulic units based on improved artificial rabbits optimization algorithm. Biomimetics 8:243","journal-title":"Biomimetics"},{"key":"1002_CR29","doi-asserted-by":"crossref","first-page":"100687","DOI":"10.1016\/j.prime.2024.100687","volume":"9","author":"E Eker","year":"2024","unstructured":"Eker E, Izci D, Ekinci S, Migdady H, Zitar RA, Abualigah L (2024) Efficient voltage regulation: An RW-ARO optimized cascaded controller approach. e-Prime Adv Electr Eng Electron Energy 9:100687","journal-title":"e-Prime Adv Electr Eng Electron Energy"},{"key":"1002_CR30","doi-asserted-by":"crossref","unstructured":"Mishra NS, Dhabal S (2024) An improved hybrid fusion of noisy medical images using differential evolution-based artificial rabbits optimization algorithm. Multidimens Syst Signal Process","DOI":"10.1007\/s11045-024-00889-z"},{"key":"1002_CR31","doi-asserted-by":"crossref","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 Evolutionary Comput 1:67\u201382","journal-title":"IEEE Trans Evolutionary Comput"},{"key":"1002_CR32","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107483","volume":"232","author":"Z Yang","year":"2021","unstructured":"Yang Z, Deng L, Wang Y, Liu J (2021) Aptenodytes Forsteri optimization: algorithm and applications. Knowl-Based Syst 232:107483","journal-title":"Knowl-Based Syst"},{"key":"1002_CR33","doi-asserted-by":"crossref","DOI":"10.1016\/j.cageo.2023.105334","volume":"174","author":"J Jiao","year":"2023","unstructured":"Jiao J, Cheng J, Liu Y, Yang H, Tan D, Cheng P, Zhang Y, Jiang C, Chen Z (2023) Inversion of TEM measurement data via a quantum particle swarm optimization algorithm with the elite opposition-based learning strategy. Comput Geosci 174:105334","journal-title":"Comput Geosci"},{"key":"1002_CR34","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1007\/s40747-022-00827-1","volume":"9","author":"D Cao","year":"2023","unstructured":"Cao D, Xu Y, Yang Z, Dong H, Li X (2023) An enhanced whale optimization algorithm with improved dynamic opposite learning and adaptive inertia weight strategy. Complex Intell Syst 9:767\u2013795","journal-title":"Complex Intell Syst"},{"key":"1002_CR35","volume":"2400","author":"W Fengbin","year":"2022","unstructured":"Fengbin W, Shaobo L, Junxing Z, Dongchao L, Xuefang W, Menghan L (2022) An improved weighted differential evolution algorithm based on the chaotic mapping and dynamic reverse learning strategy. J Phys Conf Ser 2400:012054","journal-title":"J Phys Conf Ser"},{"key":"1002_CR36","doi-asserted-by":"crossref","first-page":"1750012","DOI":"10.1142\/S1469026817500122","volume":"16","author":"S Zhang","year":"2017","unstructured":"Zhang S, Luo Q, Zhou Y (2017) Hybrid Grey Wolf optimizer using elite opposition-based learning strategy and simplex method. Int J Comput Intell Appl 16:1750012","journal-title":"Int J Comput Intell Appl"},{"key":"1002_CR37","volume":"780","author":"Q Zou","year":"2020","unstructured":"Zou Q, Fu Q, Hong X, Lu J (2020) Parameter estimation of Muskingum model based on whale optimization algorithm with elite opposition-based learning. IOP Conf Ser Mater Sci Eng 780:022013","journal-title":"IOP Conf Ser Mater Sci Eng"},{"key":"1002_CR38","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1007\/s00521-017-2952-5","volume":"30","author":"A Shima","year":"2018","unstructured":"Shima A, Jalaleddin MS, Hossein EK (2018) A Levy flight-based grey wolf optimizer combined with back-propagation algorithm for neural network training. Neural Comput Appl 30:3707\u20133720","journal-title":"Neural Comput Appl"},{"key":"1002_CR39","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1016\/j.cor.2011.09.026","volume":"40","author":"X-S Yang","year":"2013","unstructured":"Yang X-S, Deb S (2013) Multiobjective cuckoo search for design optimization. Comput Oper Res 40:1616\u20131624","journal-title":"Comput Oper Res"},{"key":"1002_CR40","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.asoc.2017.06.044","volume":"60","author":"AA Heidari","year":"2017","unstructured":"Heidari AA, Pahlavani P (2017) An efficient modified grey wolf optimizer with L\u00e9vy flight for optimization tasks. Appl Soft Comput J 60:115\u2013134","journal-title":"Appl Soft Comput J"},{"key":"1002_CR41","doi-asserted-by":"crossref","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":"1002_CR42","volume":"198","author":"C Nitish","year":"2022","unstructured":"Nitish C, Muhammad MA (2022) Golden jackal optimization: a novel nature-inspired optimizer for engineering applications. Expert Syst Appl 198:116924","journal-title":"Expert Syst Appl"},{"key":"1002_CR43","doi-asserted-by":"crossref","first-page":"531","DOI":"10.3390\/e25030531","volume":"25","author":"J Wang","year":"2023","unstructured":"Wang J, Wang X, Li X, Yi J (2023) A hybrid particle swarm optimization algorithm with dynamic adjustment of inertia weight based on a new feature selection method to optimize SVM parameters. Entropy 25:531","journal-title":"Entropy"},{"key":"1002_CR44","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1166\/jctn.2019.7996","volume":"16","author":"TA Sadiq","year":"2019","unstructured":"Sadiq TA, Raheem AF, Abbas FNA (2019) Robot arm trajectory planning optimization based on integration of particle swarm optimization and A* algorithm. J Comput Theor Nanosci 16:1046\u20131055","journal-title":"J Comput Theor Nanosci"},{"key":"1002_CR45","doi-asserted-by":"crossref","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":"1002_CR46","first-page":"1658","volume":"2014","author":"R Tanabe","year":"2014","unstructured":"Tanabe R, Fukunaga AS (2014) Improving the search performance of SHADE using linear population size reduction. IEEE Congress Evolut Comput (CEC) 2014:1658\u20131665","journal-title":"IEEE Congress Evolut Comput (CEC)"},{"key":"1002_CR47","doi-asserted-by":"crossref","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","journal-title":"Evol Comput"},{"key":"1002_CR48","doi-asserted-by":"crossref","first-page":"7305","DOI":"10.1007\/s11227-022-04959-6","volume":"79","author":"J Xue","year":"2023","unstructured":"Xue J, Shen B (2023) Dung beetle optimizer: a new meta-heuristic algorithm for global optimization. J Supercomput 79:7305\u20137336","journal-title":"J Supercomput"},{"key":"1002_CR49","volume":"191","author":"A Laith","year":"2022","unstructured":"Laith A, Abd EM, Putra S, Woo GZ, Gandomi AH (2022) Reptile Search Algorithm (RSA): a nature-inspired meta-heuristic optimizer. Expert Syst Appl 191:116158","journal-title":"Expert Syst Appl"},{"key":"1002_CR50","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109215","volume":"251","author":"C Zhong","year":"2022","unstructured":"Zhong C, Li G, Meng Z (2022) Beluga whale optimization: a novel nature-inspired metaheuristic algorithm. Knowl-Based Syst 251:109215","journal-title":"Knowl-Based Syst"},{"key":"1002_CR51","volume":"404","author":"L Deng","year":"2023","unstructured":"Deng L, Liu S (2023) A multi-strategy improved slime mould algorithm for global optimization and engineering design problems. Comput Methods Appl Mech Eng 404:115764","journal-title":"Comput Methods Appl Mech Eng"},{"key":"1002_CR52","doi-asserted-by":"crossref","DOI":"10.1016\/j.swevo.2020.100671","volume":"54","author":"B Morales-Casta\u00f1eda","year":"2020","unstructured":"Morales-Casta\u00f1eda B, Zald\u00edvar D, Cuevas E, Fausto F, Rodr\u00edguez A (2020) A better balance in metaheuristic algorithms: does it exist? Swarm Evol Comput 54:100671","journal-title":"Swarm Evol Comput"},{"key":"1002_CR53","doi-asserted-by":"crossref","first-page":"7665","DOI":"10.1007\/s00521-018-3592-0","volume":"31","author":"K Hussain","year":"2019","unstructured":"Hussain K, Salleh MNM, Cheng S, Shi Y (2019) On the exploration and exploitation in popular swarm-based metaheuristic algorithms. Neural Comput Appl 31:7665\u20137683","journal-title":"Neural Comput Appl"},{"key":"1002_CR54","first-page":"1603","volume":"136","author":"W Wang","year":"2023","unstructured":"Wang W, Tian W, Chau Kw, Xue Y, Xu L, Zang H (2023) An improved Bald eagle search algorithm with cauchy mutation and adaptive weight factor for engineering optimization. Comput Model Eng Sci 136:1603\u20131642","journal-title":"Comput Model Eng Sci"},{"key":"1002_CR55","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1016\/S0045-7825(01)00323-1","volume":"191","author":"CAC Coello","year":"2002","unstructured":"Coello CAC (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191:1245\u20131287","journal-title":"Comput Methods Appl Mech Eng"},{"key":"1002_CR56","volume":"183","author":"N Iraj","year":"2021","unstructured":"Iraj N, Farshid K (2021) A new optimization method based on COOT bird natural life model. Expert Syst Appl 183:115352","journal-title":"Expert Syst Appl"},{"key":"1002_CR57","volume":"403","author":"G Hu","year":"2023","unstructured":"Hu G, Yang R, Qin X, Wei G (2023) MCSA: Multi-strategy boosted chameleon-inspired optimization algorithm for engineering applications. Comput Methods Appl Mech Eng 403:115676","journal-title":"Comput Methods Appl Mech Eng"},{"key":"1002_CR58","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TSMC.2015.2437847","volume":"46","author":"R Datta","year":"2016","unstructured":"Datta R, Pradhan S, Bhattacharya B (2016) Analysis and design optimization of a robotic gripper using multiobjective genetic algorithm. IEEE Trans Syst Man Cybernet Syst 46:16\u201326","journal-title":"IEEE Trans Syst Man Cybernet Syst"},{"key":"1002_CR59","doi-asserted-by":"crossref","first-page":"5325","DOI":"10.1007\/s11269-021-03005-z","volume":"35","author":"H-t Chen","year":"2021","unstructured":"Chen H-t, Wang W-c, Chau K-w, Xu L, He J (2021) Flood control operation of reservoir group using Yin-Yang firefly algorithm. Water Resour Manage 35:5325\u20135345","journal-title":"Water Resour Manage"},{"key":"1002_CR60","doi-asserted-by":"crossref","first-page":"692","DOI":"10.3390\/w15040692","volume":"15","author":"W Wang","year":"2023","unstructured":"Wang W, Tian W, Chau K, Zang H, Ma M, Feng Z, Xu D (2023) Multi-reservoir flood control operation using improved bald eagle search algorithm with \u03b5 constraint method. Water 15:692","journal-title":"Water"}],"container-title":["Evolutionary Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-024-01002-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12065-024-01002-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-024-01002-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:26:03Z","timestamp":1740158763000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12065-024-01002-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,9]]},"references-count":60,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["1002"],"URL":"https:\/\/doi.org\/10.1007\/s12065-024-01002-w","relation":{},"ISSN":["1864-5909","1864-5917"],"issn-type":[{"value":"1864-5909","type":"print"},{"value":"1864-5917","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,9]]},"assertion":[{"value":"9 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2025","order":4,"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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}}],"article-number":"24"}}