{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:42:05Z","timestamp":1776444125294,"version":"3.51.2"},"reference-count":94,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T00:00:00Z","timestamp":1715212800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T00:00:00Z","timestamp":1715212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62062037"],"award-info":[{"award-number":["62062037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Natural Science Foundation of Jiangxi Province","award":["20212BAB202014"],"award-info":[{"award-number":["20212BAB202014"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s10586-024-04455-x","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T15:01:57Z","timestamp":1715266917000},"page":"10671-10715","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Multi-strategy enhanced Grey Wolf Optimizer for global optimization and real world problems"],"prefix":"10.1007","volume":"27","author":[{"given":"Zhendong","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donghui","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyuan","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daojing","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sammy","family":"Chan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,9]]},"reference":[{"key":"4455_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107122","author":"RK Agrawal","year":"2021","unstructured":"Agrawal, R.K., Kaur, B., Agarwal, P.: Quantum inspired particle swarm optimization with guided exploration for function optimization. Appl. Soft Comput. (2021). https:\/\/doi.org\/10.1016\/j.asoc.2021.107122","journal-title":"Appl. Soft Comput."},{"key":"4455_CR2","doi-asserted-by":"publisher","unstructured":"Rodriguez, L., Castillo, O., Garcia, M., Soria, J., Valdez, F., Melin, P.: Dynamic simultaneous adaptation of parameters in the Grey Wolf Optimizer using fuzzy logic. In 2017 IEEE international conference on fuzzy systems (FUZZ-IEEE) (2017). https:\/\/doi.org\/10.1109\/FUZZ-IEEE.2017.8015523","DOI":"10.1109\/FUZZ-IEEE.2017.8015523"},{"key":"4455_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119327","author":"Y Xiaobing","year":"2022","unstructured":"Xiaobing, Y., Nijun, J., Xuming, W., Mingyuan, L.: A hybrid algorithm based on Grey Wolf Optimizer and differential evolution for UAV path planning. Expert Syst App. (2022). https:\/\/doi.org\/10.1016\/j.eswa.2022.119327","journal-title":"Expert Syst App."},{"issue":"1","key":"4455_CR4","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","volume":"267","author":"JH Holland","year":"1992","unstructured":"Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66\u201373 (1992)","journal-title":"Sci. Am."},{"key":"4455_CR5","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008202821328","author":"R Storn","year":"1997","unstructured":"Storn, R., Price, K.: Differential evolution\u2014a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. (1997). https:\/\/doi.org\/10.1023\/A:1008202821328","journal-title":"J. Glob. Optim."},{"key":"4455_CR6","doi-asserted-by":"publisher","unstructured":"Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation, 25\u201328 Sept, pp. 4661\u20134667 (2007). https:\/\/doi.org\/10.1109\/CEC.2007.4425083","DOI":"10.1109\/CEC.2007.4425083"},{"issue":"4598","key":"4455_CR7","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1126\/science.220.4598.671","volume":"220","author":"S Kirkpatrick","year":"1983","unstructured":"Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671\u2013680 (1983)","journal-title":"Science"},{"issue":"13","key":"4455_CR8","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","volume":"179","author":"E Rashedi","year":"2009","unstructured":"Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232\u20132248 (2009)","journal-title":"Inf. Sci."},{"key":"4455_CR9","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1007\/s00521-015-1870-7","volume":"27","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili, S., Mirjalili, S.M., Hatamlou, A.: Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput. Appl. 27, 495\u2013513 (2016)","journal-title":"Neural Comput. Appl."},{"key":"4455_CR10","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.1109\/ICNN.1995.488968","volume":"4","author":"J Kennedy","year":"1995","unstructured":"Kennedy, J., Eberhart, R.: Particle swarm optimization. Proc ICNN\u201995 Int Conf Neural Netw 4, 1942\u20131948 (1995)","journal-title":"Proc ICNN\u201995 Int Conf Neural Netw"},{"issue":"4","key":"4455_CR11","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MCI.2006.329691","volume":"1","author":"M Dorigo","year":"2006","unstructured":"Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28\u201339 (2006)","journal-title":"IEEE Comput. Intell. Mag."},{"key":"4455_CR12","first-page":"12","volume":"2006","author":"B Basturk","year":"2006","unstructured":"Basturk, B.: An artificial bee colony (ABC) algorithm for numeric function optimization. IEEE Swarm Intell Sympos 2006, 12 (2006)","journal-title":"IEEE Swarm Intell Sympos"},{"key":"4455_CR13","doi-asserted-by":"crossref","first-page":"100793","DOI":"10.1016\/j.swevo.2020.100793","volume":"60","author":"SN Makhadmeh","year":"2021","unstructured":"Makhadmeh, S.N., Khader, A.T., Al-Betar, M.A., Naim, S., Abasi, A.K., Alyasseri, Z.A.A.: A novel hybrid Grey Wolf Optimizer with min-conflict algorithm for power scheduling problem in a smart home. Swarm Evol. Comput. 60, 100793 (2021)","journal-title":"Swarm Evol. Comput."},{"key":"4455_CR14","doi-asserted-by":"crossref","first-page":"110413","DOI":"10.1016\/j.asoc.2023.110413","volume":"143","author":"S Abbasi","year":"2023","unstructured":"Abbasi, S., Rahmani, A.M., Balador, A., Sahafi, A.: A fault-tolerant adaptive genetic algorithm for service scheduling in internet of vehicles. Appl. Soft Comput. 143, 110413 (2023)","journal-title":"Appl. Soft Comput."},{"key":"4455_CR15","doi-asserted-by":"crossref","first-page":"110367","DOI":"10.1016\/j.knosys.2023.110367","volume":"264","author":"B Zhou","year":"2023","unstructured":"Zhou, B., Zhao, Z.: An adaptive artificial bee colony algorithm enhanced by Deep Q-Learning for milk-run vehicle scheduling problem based on supply hub. Knowl. Based Syst. 264, 110367 (2023)","journal-title":"Knowl. Based Syst."},{"key":"4455_CR16","doi-asserted-by":"crossref","first-page":"110019","DOI":"10.1016\/j.comnet.2023.110019","volume":"236","author":"Z Wang","year":"2023","unstructured":"Wang, Z., Shao, L., Yang, S., Wang, J., Li, D.: CRLM: a cooperative model based on reinforcement learning and metaheuristic algorithms of routing protocols in wireless sensor networks. Comput. Netw. 236, 110019 (2023)","journal-title":"Comput. Netw."},{"key":"4455_CR17","doi-asserted-by":"crossref","first-page":"110765","DOI":"10.1016\/j.asoc.2023.110765","volume":"148","author":"S Deng","year":"2023","unstructured":"Deng, S., Li, Y., Wang, J., Cao, R., Li, M.: A feature-thresholds guided genetic algorithm based on a multi-objective feature scoring method for high-dimensional feature selection. Appl. Soft Comput. 148, 110765 (2023)","journal-title":"Appl. Soft Comput."},{"key":"4455_CR18","doi-asserted-by":"crossref","first-page":"106520","DOI":"10.1016\/j.compbiomed.2022.106520","volume":"153","author":"C Zhong","year":"2023","unstructured":"Zhong, C., Li, G., Meng, Z., Li, H., He, W.: A self-adaptive quantum equilibrium optimizer with artificial bee colony for feature selection. Comput. Biol. Med. 153, 106520 (2023)","journal-title":"Comput. Biol. Med."},{"key":"4455_CR19","doi-asserted-by":"crossref","unstructured":"Jia, Y., Qu, L., Li, X. Automatic path planning of unmanned combat aerial vehicle based on double-layer coding method with enhanced Grey Wolf Optimizer. Artif. Intell. Rev. 1\u201358 (2023)","DOI":"10.1007\/s10462-023-10481-9"},{"key":"4455_CR20","doi-asserted-by":"crossref","unstructured":"Nadimi-Shahraki, M. H., Moeini, E., Taghian, S., Mirjalili, S.: Discrete improved Grey Wolf Optimizer for community detection. J. Bion. Eng. 1\u201328 (2023)","DOI":"10.1007\/s42235-023-00387-1"},{"issue":"1","key":"4455_CR21","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67\u201382 (1997)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"4455_CR22","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, S.M., Lewis, A.: Grey Wolf Optimizer. Adv. Eng. Softw. 69, 46\u201361 (2014)","journal-title":"Adv. Eng. Softw."},{"key":"4455_CR23","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s00521-017-3272-5","volume":"30","author":"H Faris","year":"2018","unstructured":"Faris, H., Aljarah, I., Al-Betar, M.A., Mirjalili, S.: Grey Wolf Optimizer: a review of recent variants and applications. Neural Comput. Appl. 30, 413\u2013435 (2018)","journal-title":"Neural Comput. Appl."},{"key":"4455_CR24","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2021.120211","volume":"225","author":"A Meng","year":"2021","unstructured":"Meng, A., et al.: A high-performance crisscross search based Grey Wolf Optimizer for solving optimal power flow problem. Energy 225, 120211 (2021)","journal-title":"Energy"},{"key":"4455_CR25","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1007\/s00521-017-2952-5","volume":"30","author":"S Amirsadri","year":"2018","unstructured":"Amirsadri, S., Mousavirad, S.J., Ebrahimpour-Komleh, H.: A Levy flight-based Grey Wolf Optimizer combined with back-propagation algorithm for neural network training. Neural Comput. Appl. 30, 3707\u20133720 (2018)","journal-title":"Neural Comput. Appl."},{"issue":"21","key":"4455_CR26","doi-asserted-by":"crossref","first-page":"14119","DOI":"10.1007\/s00521-021-06050-2","volume":"33","author":"S Dereli","year":"2021","unstructured":"Dereli, S.: A new modified grey wolf optimization algorithm proposal for a fundamental engineering problem in robotics. Neural Comput. Appl. 33(21), 14119\u201314131 (2021)","journal-title":"Neural Comput. Appl."},{"key":"4455_CR27","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.asoc.2018.11.047","volume":"76","author":"Q Tu","year":"2019","unstructured":"Tu, Q., Chen, X., Liu, X.: Multi-strategy ensemble Grey Wolf Optimizer and its application to feature selection. Appl. Soft Comput. 76, 16\u201330 (2019)","journal-title":"Appl. Soft Comput."},{"key":"4455_CR28","doi-asserted-by":"crossref","first-page":"105746","DOI":"10.1016\/j.knosys.2020.105746","volume":"195","author":"P Hu","year":"2020","unstructured":"Hu, P., Pan, J.-S., Chu, S.-C.: Improved binary Grey Wolf Optimizer and its application for feature selection. Knowl.-Based Syst. 195, 105746 (2020)","journal-title":"Knowl.-Based Syst."},{"issue":"21","key":"4455_CR29","doi-asserted-by":"crossref","first-page":"14583","DOI":"10.1007\/s00521-021-06099-z","volume":"33","author":"B Sathiyabhama","year":"2021","unstructured":"Sathiyabhama, B., et al.: A novel feature selection framework based on Grey Wolf Optimizer for mammogram image analysis. Neural Comput. Appl. 33(21), 14583\u201314602 (2021)","journal-title":"Neural Comput. Appl."},{"key":"4455_CR30","doi-asserted-by":"crossref","first-page":"117864","DOI":"10.1016\/j.eswa.2022.117864","volume":"206","author":"K Deep","year":"2022","unstructured":"Deep, K.: A random walk Grey Wolf Optimizer based on dispersion factor for feature selection on chronic disease prediction. Expert Syst. Appl. 206, 117864 (2022)","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"4455_CR31","doi-asserted-by":"crossref","first-page":"2186","DOI":"10.1109\/TAP.2019.2938703","volume":"68","author":"X Li","year":"2019","unstructured":"Li, X., Luk, K.M.: The Grey Wolf Optimizer and its applications in electromagnetics. IEEE Trans. Antennas Propag. 68(3), 2186\u20132197 (2019)","journal-title":"IEEE Trans. Antennas Propag."},{"key":"4455_CR32","doi-asserted-by":"crossref","first-page":"106996","DOI":"10.1016\/j.asoc.2020.106996","volume":"100","author":"A Altan","year":"2021","unstructured":"Altan, A., Karasu, S., Zio, E.: A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and Grey Wolf Optimizer. Appl. Soft Comput. 100, 106996 (2021)","journal-title":"Appl. Soft Comput."},{"issue":"6","key":"4455_CR33","doi-asserted-by":"crossref","first-page":"999","DOI":"10.3390\/math10060999","volume":"10","author":"A Alzaqebah","year":"2022","unstructured":"Alzaqebah, A., Aljarah, I., Al-Kadi, O., Dama\u0161evi\u010dius, R.: A modified grey wolf optimization algorithm for an intrusion detection system. Mathematics 10(6), 999 (2022)","journal-title":"Mathematics"},{"issue":"2","key":"4455_CR34","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1007\/s11276-021-02866-x","volume":"28","author":"M Otair","year":"2022","unstructured":"Otair, M., Ibrahim, O.T., Abualigah, L., Altalhi, M., Sumari, P.: An enhanced Grey Wolf Optimizer based particle swarm optimizer for intrusion detection system in wireless sensor networks. Wireless Netw. 28(2), 721\u2013744 (2022)","journal-title":"Wireless Netw."},{"key":"4455_CR35","doi-asserted-by":"crossref","first-page":"117597","DOI":"10.1016\/j.eswa.2022.117597","volume":"204","author":"QM Alzubi","year":"2022","unstructured":"Alzubi, Q.M., Anbar, M., Sanjalawe, Y., Al-Betar, M.A., Abdullah, R.: Intrusion detection system based on hybridizing a modified binary grey wolf optimization and particle swarm optimization. Expert Syst. Appl. 204, 117597 (2022)","journal-title":"Expert Syst. Appl."},{"key":"4455_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113389","author":"S Dhargupta","year":"2020","unstructured":"Dhargupta, S., Ghosh, M., Mirjalili, S., Sarkar, R.: Selective opposition based Grey Wolf Optimization. Expert Syst. App. (2020). https:\/\/doi.org\/10.1016\/j.eswa.2020.113389","journal-title":"Expert Syst. App."},{"key":"4455_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113917","author":"MH Nadimi-Shahraki","year":"2020","unstructured":"Nadimi-Shahraki, M.H., Taghian, S., Mirjalili, S.: An improved Grey Wolf Optimizer for solving engineering problems. Expert Syst. App. (2020). https:\/\/doi.org\/10.1016\/j.eswa.2020.113917","journal-title":"Expert Syst. App."},{"key":"4455_CR38","doi-asserted-by":"crossref","first-page":"110297","DOI":"10.1016\/j.knosys.2023.110297","volume":"264","author":"R Ahmed","year":"2023","unstructured":"Ahmed, R., Rangaiah, G.P., Mahadzir, S., Mirjalili, S., Hassan, M.H., Kamel, S.: Memory, evolutionary operator, and local search based improved Grey Wolf Optimizer with linear population size reduction technique. Knowl. Based Syst. 264, 110297 (2023)","journal-title":"Knowl. Based Syst."},{"key":"4455_CR39","doi-asserted-by":"crossref","first-page":"77416","DOI":"10.1109\/ACCESS.2021.3083220","volume":"9","author":"Z Xu","year":"2021","unstructured":"Xu, Z., Yang, H., Li, J., Zhang, X., Lu, B., Gao, S.: Comparative study on single and multiple chaotic maps incorporated grey wolf optimization algorithms. IEEE Access 9, 77416\u201377437 (2021)","journal-title":"IEEE Access"},{"key":"4455_CR40","doi-asserted-by":"crossref","first-page":"120824","DOI":"10.1016\/j.techfore.2021.120824","volume":"169","author":"R Rajakumar","year":"2021","unstructured":"Rajakumar, R., Sekaran, K., Hsu, C.-H., Kadry, S.: Accelerated grey wolf optimization for global optimization problems. Technol. Forecast. Soc. Chang. 169, 120824 (2021)","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"4455_CR41","doi-asserted-by":"crossref","first-page":"107139","DOI":"10.1016\/j.knosys.2021.107139","volume":"226","author":"X Yu","year":"2021","unstructured":"Yu, X., Xu, W., Li, C.: Opposition-based learning Grey Wolf Optimizer for global optimization. Knowl.-Based Syst. 226, 107139 (2021)","journal-title":"Knowl.-Based Syst."},{"key":"4455_CR42","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.ins.2021.11.076","volume":"586","author":"Z Meng","year":"2022","unstructured":"Meng, Z., Zhong, Y., Mao, G., Liang, Y.: PSO-sono: a novel PSO variant for single-objective numerical optimization. Inf. Sci. 586, 176\u2013191 (2022)","journal-title":"Inf. Sci."},{"key":"4455_CR43","doi-asserted-by":"crossref","first-page":"106761","DOI":"10.1016\/j.asoc.2020.106761","volume":"97","author":"MH Nadimi-Shahraki","year":"2020","unstructured":"Nadimi-Shahraki, M.H., Taghian, S., Mirjalili, S., Faris, H.: MTDE: an effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems. Appl. Soft Comput. 97, 106761 (2020)","journal-title":"Appl. Soft Comput."},{"issue":"4","key":"4455_CR44","doi-asserted-by":"crossref","first-page":"862","DOI":"10.3390\/math11040862","volume":"11","author":"MH Nadimi-Shahraki","year":"2023","unstructured":"Nadimi-Shahraki, M.H., Zamani, H., Fatahi, A., Mirjalili, S.: MFO-SFR: an enhanced moth-flame optimization algorithm using an effective stagnation finding and replacing strategy. Mathematics 11(4), 862 (2023)","journal-title":"Mathematics"},{"key":"4455_CR45","doi-asserted-by":"crossref","unstructured":"Zamani, H., Nadimi-Shahraki, M. H., Mirjalili, S., Soleimanian Gharehchopogh, F., Oliva, D.: A critical review of moth-flame optimization algorithm and its variants: structural reviewing, performance evaluation, and statistical analysis. Arch. Comput. Methods Eng. 1\u201349 (2024)","DOI":"10.1007\/s11831-023-10037-8"},{"key":"4455_CR46","doi-asserted-by":"crossref","unstructured":"Fatahi, A., Nadimi-Shahraki, M. H., Zamani, H.: An improved binary quantum-based avian navigation optimizer algorithm to select effective feature subset from medical data: a COVID-19 case study. J. Bion. Eng. 1\u201321 (2023)","DOI":"10.1007\/s42235-023-00433-y"},{"key":"4455_CR47","doi-asserted-by":"crossref","first-page":"105879","DOI":"10.1016\/j.bspc.2023.105879","volume":"90","author":"H Zamani","year":"2024","unstructured":"Zamani, H., Nadimi-Shahraki, M.H.: An evolutionary crow search algorithm equipped with interactive memory mechanism to optimize artificial neural network for disease diagnosis. Biomed. Signal Process. Control 90, 105879 (2024)","journal-title":"Biomed. Signal Process. Control"},{"issue":"1","key":"4455_CR48","doi-asserted-by":"crossref","first-page":"564","DOI":"10.3390\/app13010564","volume":"13","author":"MH Nadimi-Shahraki","year":"2022","unstructured":"Nadimi-Shahraki, M.H., Asghari Varzaneh, Z., Zamani, H., Mirjalili, S.: Binary starling murmuration optimizer algorithm to select effective features from medical data. Appl. Sci. 13(1), 564 (2022)","journal-title":"Appl. Sci."},{"issue":"1","key":"4455_CR49","doi-asserted-by":"crossref","first-page":"e0280006","DOI":"10.1371\/journal.pone.0280006","volume":"18","author":"MH Nadimi-Shahraki","year":"2023","unstructured":"Nadimi-Shahraki, M.H., Taghian, S., Zamani, H., Mirjalili, S., Elaziz, M.A.: MMKE: multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems. PLoS ONE 18(1), e0280006 (2023)","journal-title":"PLoS ONE"},{"key":"4455_CR50","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-022-07593-9","author":"Y Kunshan","year":"2022","unstructured":"Kunshan, Y., et al.: An information entropy-based Grey Wolf Optimizer. Soft. Comput. (2022). https:\/\/doi.org\/10.1007\/s00500-022-07593-9","journal-title":"Soft. Comput."},{"key":"4455_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.108429","author":"A Joy","year":"2022","unstructured":"Joy, A., Sriyankar, A.: Randomized Balanced Grey Wolf Optimizer (RBGWO) for solving real life optimization problems. Appl. Soft Comput. (2022). https:\/\/doi.org\/10.1016\/j.asoc.2022.108429","journal-title":"Appl. Soft Comput."},{"key":"4455_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107328","author":"M Banaie-Dezfouli","year":"2021","unstructured":"Banaie-Dezfouli, M., Nadimi-Shahraki, M.H., Beheshti, Z.: R-GWO: Representative-based Grey Wolf Optimizer for solving engineering problems. Appl. Soft Comput. (2021). https:\/\/doi.org\/10.1016\/j.asoc.2021.107328","journal-title":"Appl. Soft Comput."},{"key":"4455_CR53","doi-asserted-by":"crossref","first-page":"113882","DOI":"10.1016\/j.eswa.2020.113882","volume":"165","author":"Q Fan","year":"2021","unstructured":"Fan, Q., Huang, H., Li, Y., Han, Z., Hu, Y., Huang, D.: Beetle antenna strategy based Grey Wolf Optimization. Expert Syst. Appl. 165, 113882 (2021)","journal-title":"Expert Syst. Appl."},{"key":"4455_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116450","author":"S Shitu","year":"2022","unstructured":"Shitu, S., Jagdish Chand, B.: Mutation-driven Grey Wolf Optimizer with modified search mechanism. Expert Syst. App. (2022). https:\/\/doi.org\/10.1016\/j.eswa.2021.116450","journal-title":"Expert Syst. App."},{"key":"4455_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109100","author":"J Jiang","year":"2022","unstructured":"Jiang, J., Zhao, Z., Liu, Y., Li, W., Wang, H.: DSGWO: an improved Grey Wolf Optimizer with diversity enhanced strategy based on group-stage competition and balance mechanisms. Knowl. Based Syst. (2022). https:\/\/doi.org\/10.1016\/j.knosys.2022.109100","journal-title":"Knowl. Based Syst."},{"key":"4455_CR56","doi-asserted-by":"crossref","first-page":"103308","DOI":"10.1016\/j.adhoc.2023.103308","volume":"152","author":"Z Wang","year":"2024","unstructured":"Wang, Z., Huang, L., Yang, S., Luo, X., He, D., Chan, S.: Multi-strategy enhanced grey wolf algorithm for obstacle-aware WSNs coverage optimization. Ad Hoc Netw. 152, 103308 (2024)","journal-title":"Ad Hoc Netw."},{"key":"4455_CR57","doi-asserted-by":"crossref","first-page":"117671","DOI":"10.1016\/j.eswa.2022.117671","volume":"206","author":"Z Wang","year":"2022","unstructured":"Wang, Z., Li, Z., He, D., Chan, S.: A lightweight approach for network intrusion detection in industrial cyber-physical systems based on knowledge distillation and deep metric learning. Expert Syst. Appl. 206, 117671 (2022)","journal-title":"Expert Syst. Appl."},{"key":"4455_CR58","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07836-8","author":"M Majdi","year":"2022","unstructured":"Majdi, M., et al.: An efficient high-dimensional feature selection approach driven by enhanced multi-strategy Grey Wolf Optimizer for biological data classification. Neural Comput. App. (2022). https:\/\/doi.org\/10.1007\/s00521-022-07836-8","journal-title":"Neural Comput. App."},{"key":"4455_CR59","doi-asserted-by":"crossref","unstructured":"Singh, N., Singh, S. Hybrid algorithm of particle swarm optimization and Grey Wolf Optimizer for improving convergence performance. J. Appl. Math., 2017 (2017)","DOI":"10.1155\/2017\/2030489"},{"key":"4455_CR60","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/s12293-017-0234-5","volume":"9","author":"MA Tawhid","year":"2017","unstructured":"Tawhid, M.A., Ali, A.F.: A hybrid Grey Wolf Optimizer and genetic algorithm for minimizing potential energy function. Memetic Comput. 9, 347\u2013359 (2017)","journal-title":"Memetic Comput."},{"key":"4455_CR61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eswa.2018.04.028","volume":"108","author":"RA Ibrahim","year":"2018","unstructured":"Ibrahim, R.A., Abd Elaziz, M., Lu, S.: Chaotic opposition-based grey-wolf optimization algorithm based on differential evolution and disruption operator for global optimization. Expert Syst. App. 108, 1\u201327 (2018)","journal-title":"Expert Syst. App."},{"key":"4455_CR62","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.asoc.2018.02.049","volume":"67","author":"X Zhang","year":"2018","unstructured":"Zhang, X., Kang, Q., Cheng, J., Wang, X.: A novel hybrid algorithm based on biogeography-based optimization and Grey Wolf Optimizer. Appl. Soft Comput. 67, 197\u2013214 (2018)","journal-title":"Appl. Soft Comput."},{"issue":"5","key":"4455_CR63","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1007\/s42235-023-00387-1","volume":"20","author":"MH Nadimi-Shahraki","year":"2023","unstructured":"Nadimi-Shahraki, M.H., Moeini, E., Taghian, S., Mirjalili, S.: Discrete improved Grey Wolf Optimizer for community detection. J. Bionic Eng. 20(5), 2331\u20132358 (2023)","journal-title":"J. Bionic Eng."},{"key":"4455_CR64","doi-asserted-by":"crossref","first-page":"101636","DOI":"10.1016\/j.jocs.2022.101636","volume":"61","author":"MH Nadimi-Shahraki","year":"2022","unstructured":"Nadimi-Shahraki, M.H., Taghian, S., Mirjalili, S., Zamani, H., Bahreininejad, A.: GGWO: Gaze cues learning-based Grey Wolf Optimizer and its applications for solving engineering problems. J. Comput. Sci. 61, 101636 (2022)","journal-title":"J. Comput. Sci."},{"key":"4455_CR65","doi-asserted-by":"crossref","first-page":"100671","DOI":"10.1016\/j.swevo.2020.100671","volume":"54","author":"B Morales-Casta\u00f1eda","year":"2020","unstructured":"Morales-Casta\u00f1eda, B., Zaldivar, D., Cuevas, E., Fausto, F., Rodr\u00edguez, A.: A better balance in metaheuristic algorithms: does it exist? Swarm Evol. Comput. 54, 100671 (2020)","journal-title":"Swarm Evol. Comput."},{"key":"4455_CR66","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.swevo.2018.12.009","volume":"45","author":"G Xu","year":"2019","unstructured":"Xu, G., et al.: Particle swarm optimization based on dimensional learning strategy. Swarm Evol. Comput. 45, 33\u201351 (2019)","journal-title":"Swarm Evol. Comput."},{"key":"4455_CR67","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.swevo.2015.05.002","volume":"24","author":"N Lynn","year":"2015","unstructured":"Lynn, N., Suganthan, P.N.: Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol. Comput. 24, 11\u201324 (2015)","journal-title":"Swarm Evol. Comput."},{"key":"4455_CR68","doi-asserted-by":"crossref","first-page":"108361","DOI":"10.1016\/j.cie.2022.108361","volume":"171","author":"X Lin","year":"2022","unstructured":"Lin, X., Yu, X., Li, W.: A heuristic whale optimization algorithm with niching strategy for global multi-dimensional engineering optimization. Comput. Ind. Eng. 171, 108361 (2022)","journal-title":"Comput. Ind. Eng."},{"key":"4455_CR69","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.ins.2023.01.120","volume":"629","author":"C Li","year":"2023","unstructured":"Li, C., Sun, G., Deng, L., Qiao, L., Yang, G.: A population state evaluation-based improvement framework for differential evolution. Inf. Sci. 629, 15\u201338 (2023). https:\/\/doi.org\/10.1016\/j.ins.2023.01.120","journal-title":"Inf. Sci."},{"key":"4455_CR70","doi-asserted-by":"crossref","first-page":"104558","DOI":"10.1016\/j.engappai.2021.104558","volume":"108","author":"W Yang","year":"2022","unstructured":"Yang, W., et al.: A multi-strategy Whale optimization algorithm and its application. Eng. Appl. Artif. Intell. 108, 104558 (2022)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"4455_CR71","doi-asserted-by":"crossref","first-page":"120904","DOI":"10.1016\/j.eswa.2023.120904","volume":"233","author":"S Fu","year":"2023","unstructured":"Fu, S., Huang, H., Ma, C., Wei, J., Li, Y., Fu, Y.: Improved dwarf mongoose optimization algorithm using novel nonlinear control and exploration strategies. Expert Syst. Appl. 233, 120904 (2023)","journal-title":"Expert Syst. Appl."},{"key":"4455_CR72","doi-asserted-by":"crossref","first-page":"105841","DOI":"10.1016\/j.asoc.2019.105841","volume":"85","author":"X Zhang","year":"2019","unstructured":"Zhang, X., Liu, H., Zhang, T., Wang, Q., Wang, Y., Tu, L.: Terminal crossover and steering-based particle swarm optimization algorithm with disturbance. Appl. Soft Comput. 85, 105841 (2019)","journal-title":"Appl. Soft Comput."},{"key":"4455_CR73","doi-asserted-by":"crossref","first-page":"106768","DOI":"10.1016\/j.knosys.2021.106768","volume":"215","author":"S Molaei","year":"2021","unstructured":"Molaei, S., Moazen, H., Najjar-Ghabel, S., Farzinvash, L.: Particle swarm optimization with an enhanced learning strategy and crossover operator. Knowl.-Based Syst. 215, 106768 (2021)","journal-title":"Knowl.-Based Syst."},{"key":"4455_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117629","author":"C Ma","year":"2022","unstructured":"Ma, C., Huang, H., Fan, Q., Wei, J., Du, Y., Gao, W.: Grey Wolf Optimizer based on Aquila exploration method. Expert Syst. App. (2022). https:\/\/doi.org\/10.1016\/j.eswa.2022.117629","journal-title":"Expert Syst. App."},{"key":"4455_CR75","doi-asserted-by":"crossref","first-page":"107061","DOI":"10.1016\/j.asoc.2020.107061","volume":"101","author":"X Zhang","year":"2021","unstructured":"Zhang, X., Lin, Q., Mao, W., Liu, S., Dou, Z., Liu, G.: Hybrid PARTICLE SWARM and Grey Wolf Optimizer and its application to clustering optimization. Appl. Soft Comput. 101, 107061 (2021)","journal-title":"Appl. Soft Comput."},{"key":"4455_CR76","doi-asserted-by":"crossref","first-page":"119269","DOI":"10.1016\/j.eswa.2022.119269","volume":"215","author":"Y Shen","year":"2023","unstructured":"Shen, Y., Zhang, C., Gharehchopogh, F.S., Mirjalili, S.: An improved whale optimization algorithm based on multi-population evolution for global optimization and engineering design problems. Expert Syst. Appl. 215, 119269 (2023)","journal-title":"Expert Syst. Appl."},{"key":"4455_CR77","first-page":"1","volume":"2020","author":"Z Liu","year":"2020","unstructured":"Liu, Z., Nishi, T.: Multipopulation ensemble particle swarm optimizer for engineering design problems. Math. Probl. Eng. 2020, 1\u201330 (2020)","journal-title":"Math. Probl. Eng."},{"issue":"2","key":"4455_CR78","first-page":"83","volume":"4","author":"S Cheng","year":"2014","unstructured":"Cheng, S., Shi, Y., Qin, Q., Zhang, Q., Bai, R.: Population diversity maintenance in brain storm optimization algorithm. J. Art. Intell. Soft Compu. Res. 4(2), 83\u201397 (2014)","journal-title":"J. Art. Intell. Soft Compu. Res."},{"issue":"2","key":"4455_CR79","first-page":"633","volume":"9","author":"J Song","year":"2022","unstructured":"Song, J., Chen, C., Heidari, A.A., Liu, J., Yu, H., Chen, H.: Performance optimization of annealing salp swarm algorithm: frameworks and applications for engineering design. J. Comput. Des. Eng. 9(2), 633\u2013669 (2022)","journal-title":"J. Comput. Des. Eng."},{"key":"4455_CR80","doi-asserted-by":"crossref","first-page":"119238","DOI":"10.1016\/j.ins.2023.119238","volume":"643","author":"W Li","year":"2023","unstructured":"Li, W., Jing, J., Chen, Y., Chen, Y.: A cooperative particle swarm optimization with difference learning. Inf. Sci. 643, 119238 (2023)","journal-title":"Inf. Sci."},{"key":"4455_CR81","unstructured":"Storn, R.: On the usage of differential evolution for function optimization. In Proceedings of North American fuzzy information processing, pp. 519\u2013523. IEEE (1996)"},{"issue":"10","key":"4455_CR82","first-page":"293","volume":"10","author":"R G\u00e4mperle","year":"2002","unstructured":"G\u00e4mperle, R., M\u00fcller, S.D., Koumoutsakos, P.: A parameter study for differential evolution. Adv. Intell. Syst. Fuzzy Syst. Evol. Comput. 10(10), 293\u2013298 (2002)","journal-title":"Adv. Intell. Syst. Fuzzy Syst. Evol. Comput."},{"key":"4455_CR83","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn, R., Price, K.: Differential evolution\u2014a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341\u2013359 (1997)","journal-title":"J. Global Optim."},{"key":"4455_CR84","doi-asserted-by":"crossref","unstructured":"Ronkkonen, J., Kukkonen, S., Price, K. V.: Real-parameter optimization with differential evolution. In 2005 IEEE congress on evolutionary computation, vol. 1, pp. 506\u2013513. IEEE (2005)","DOI":"10.1109\/CEC.2005.1554725"},{"key":"4455_CR85","doi-asserted-by":"crossref","unstructured":"Awad, N. H., Ali, M. Z., Suganthan, P. N.: Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems. In 2017 IEEE congress on evolutionary computation (CEC), pp. 372\u2013379. IEEE (2017)","DOI":"10.1109\/CEC.2017.7969336"},{"key":"4455_CR86","doi-asserted-by":"crossref","unstructured":"Mohamed, A. W., Hadi, A. A., Fattouh, A. M., Jambi, K. M.: LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems. In 2017 IEEE Congress on evolutionary computation (CEC), pp. 145\u2013152. IEEE (2017)","DOI":"10.1109\/CEC.2017.7969307"},{"key":"4455_CR87","doi-asserted-by":"publisher","unstructured":"Sallam, K. M., Elsayed, S. M., Chakrabortty, R. K., Ryan, M. J.: Multi-operator differential evolution algorithm for solving real-world constrained optimization problems. In 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1\u20138 (2020). https:\/\/doi.org\/10.1109\/CEC48606.2020.9185722","DOI":"10.1109\/CEC48606.2020.9185722"},{"key":"4455_CR88","doi-asserted-by":"publisher","unstructured":"Gurrola-Ramos, J., Hern\u00e0ndez-Aguirre, A., Dalmau-Cede\u00f1o, O.: COLSHADE for Real-World Single-Objective Constrained optimization Problems. In 2020 IEEE congress on evolutionary computation (CEC), pp. 1\u20138 (2020). https:\/\/doi.org\/10.1109\/CEC48606.2020.9185583","DOI":"10.1109\/CEC48606.2020.9185583"},{"key":"4455_CR89","doi-asserted-by":"publisher","unstructured":"Hellwig, M., Beyer, H. G.: A modified matrix adaptation evolution strategy with restarts for constrained real-world problems. In 2020 IEEE congress on evolutionary computation (CEC), pp. 1\u20138 (2020). https:\/\/doi.org\/10.1109\/CEC48606.2020.9185566","DOI":"10.1109\/CEC48606.2020.9185566"},{"key":"4455_CR90","doi-asserted-by":"crossref","first-page":"100693","DOI":"10.1016\/j.swevo.2020.100693","volume":"56","author":"A Kumar","year":"2020","unstructured":"Kumar, A., Wu, G., Ali, M.Z., Mallipeddi, R., Suganthan, P.N., Das, S.: A test-suite of non-convex constrained optimization problems from the real-world and some baseline results. Swarm Evol. Comput. 56, 100693 (2020)","journal-title":"Swarm Evol. Comput."},{"key":"4455_CR91","doi-asserted-by":"crossref","first-page":"110701","DOI":"10.1016\/j.asoc.2023.110701","volume":"146","author":"Q Yang","year":"2023","unstructured":"Yang, Q., Liu, J., Wu, Z., He, S.: A fusion algorithm based on whale and grey wolf optimization algorithm for solving real-world optimization problems. Appl. Soft Comput. 146, 110701 (2023)","journal-title":"Appl. Soft Comput."},{"key":"4455_CR92","unstructured":"Barbosa, H. J., Lemonge, A. C. An adaptive penalty scheme in genetic algorithms for constrained optimization problems. In Proceedings of the 4th annual conference on genetic and evolutionary computation, pp. 287\u2013294 (2002)"},{"key":"4455_CR93","first-page":"389","volume-title":"Australasian joint conference on artificial intelligence","author":"T Takahama","year":"2005","unstructured":"Takahama, T., Sakai, S., Iwane, N.: Constrained optimization by the \u03b5 constrained hybrid algorithm of particle swarm optimization and genetic algorithm. In: Australasian joint conference on artificial intelligence, pp. 389\u2013400. Springer (2005)"},{"key":"4455_CR94","doi-asserted-by":"crossref","first-page":"108343","DOI":"10.1016\/j.asoc.2021.108343","volume":"116","author":"G Yavuz","year":"2022","unstructured":"Yavuz, G., Durmu\u015f, B., Ayd\u0131n, D.: Artificial bee colony algorithm with distant savants for constrained optimization. Appl. Soft Comput. 116, 108343 (2022)","journal-title":"Appl. Soft Comput."}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04455-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-04455-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04455-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T19:44:51Z","timestamp":1725911091000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-04455-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,9]]},"references-count":94,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["4455"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-04455-x","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,9]]},"assertion":[{"value":"7 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2024","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}