{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T23:10:36Z","timestamp":1743030636618,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811981517"},{"type":"electronic","value":"9789811981524"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-19-8152-4_10","type":"book-chapter","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T16:04:02Z","timestamp":1670601842000},"page":"142-159","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improved Spotted Hyena Optimizer Fused with Multiple Strategies"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1335-7338","authenticated-orcid":false,"given":"Chunhui","family":"Mo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8619-973X","authenticated-orcid":false,"given":"Xiaofeng","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6047-8570","authenticated-orcid":false,"given":"Lin","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,10]]},"reference":[{"key":"10_CR1","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.: Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv. Eng. Softw. 114, 48\u201370 (2017)","journal-title":"Adv. Eng. Softw."},{"issue":"7","key":"10_CR2","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1111\/ele.12447","volume":"18","author":"A Ilany","year":"2015","unstructured":"Ilany, A., Booms, A.S., Holekamp, K.E.: Topological effects of network structure on long-term social network dynamics in a wild mammal. Ecol. Lett. 18(7), 687\u2013695 (2015)","journal-title":"Ecol. Lett."},{"key":"10_CR3","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.knosys.2018.03.011","volume":"150","author":"G Dhiman","year":"2018","unstructured":"Dhiman, G., Kumar, V.: Multi-objective spotted hyena optimizer: a multi-objective optimization algorithm for engineering problems. Knowl.-Based Syst. 150, 175\u2013197 (2018)","journal-title":"Knowl.-Based Syst."},{"issue":"4","key":"10_CR4","doi-asserted-by":"publisher","first-page":"3767","DOI":"10.3934\/mbe.2020211","volume":"17","author":"G Zhou","year":"2020","unstructured":"Zhou, G., Li, J., Tang, Z., et al.: An improved spotted hyena optimizer for PID parameters in an AVR system. Math. Biosci. Eng. 17(4), 3767\u20133783 (2020)","journal-title":"Math. Biosci. Eng."},{"issue":"5","key":"10_CR5","doi-asserted-by":"publisher","first-page":"6677","DOI":"10.3233\/JIFS-179746","volume":"38","author":"N Panda","year":"2020","unstructured":"Panda, N., Majhi, S.K., Singh, S., et al.: Oppositional spotted hyena optimizer with mutation operator for global optimization and application in training wavelet neural network. J. Intell. Fuzzy Syst. 38(5), 6677\u20136690 (2020)","journal-title":"J. Intell. Fuzzy Syst."},{"issue":"05","key":"10_CR6","first-page":"1290","volume":"41","author":"H Jia","year":"2021","unstructured":"Jia, H., Jiang, Z., Li, Y., et al.: Simultaneous feature selection optimization based on improved spotted hyena optimizer algorithm. J. Comput. Appl. 41(05), 1290\u20131298 (2021)","journal-title":"J. Comput. Appl."},{"issue":"4","key":"10_CR7","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341\u2013359 (1997)","journal-title":"J. Global Optim."},{"issue":"01","key":"10_CR8","first-page":"74","volume":"47","author":"H Jia","year":"2020","unstructured":"Jia, H., Jiang, Z., Li, Y., et al.: Feature selection based on simulated annealing spotted hyena optimization algorithm. Appl. Sci. Technol. 47(01), 74\u201379 (2020)","journal-title":"Appl. Sci. Technol."},{"key":"10_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cogsys.2020.09.001","volume":"65","author":"Q Luo","year":"2020","unstructured":"Luo, Q., Li, J., Zhou, Y.Q., et al.: Using spotted hyena optimizer for training feedforward neural networks. Cogn. Syst. Res. 65, 1\u201316 (2020)","journal-title":"Cogn. Syst. Res."},{"issue":"1","key":"10_CR10","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1111\/coin.12272","volume":"36","author":"N Panda","year":"2020","unstructured":"Panda, N., Majhi, S.K.: Improved spotted hyena optimizer with space transformational search for training pi-sigma higher order neural network. Comput. Intell. 36(1), 320\u2013350 (2020)","journal-title":"Comput. Intell."},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: International Conference on Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, vol. 1, pp. 695\u2013701. IEEE (2005)","DOI":"10.1109\/CIMCA.2005.1631345"},{"key":"10_CR12","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.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51\u201367 (2016)","journal-title":"Adv. Eng. Softw."},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Dhiman, G., Kaur, A.: Spotted hyena optimizer for solving engineering design problems. In: 2017 International Conference on Machine Learning and Data Science (MLDS), pp. 114\u2013119 (2017)","DOI":"10.1109\/MLDS.2017.5"},{"key":"10_CR14","doi-asserted-by":"publisher","first-page":"113612","DOI":"10.1016\/j.eswa.2020.113612","volume":"158","author":"H Chen","year":"2020","unstructured":"Chen, H., Li, W., Yang, X.: A whale optimization algorithm with chaos mechanism based on quasi-opposition for global optimization problems. Expert Syst. Appl. 158, 113612 (2020)","journal-title":"Expert Syst. Appl."},{"issue":"12","key":"10_CR15","first-page":"3640","volume":"38","author":"H Xiao-long","year":"2021","unstructured":"Xiao-long, H., Gang, Z., Yue-hua, C., et al.: Multi-class algorithm of WOA-SVM using Levy flight and elite opposition-based learning. Appl. Res. Comput. 38(12), 3640\u20133645 (2021)","journal-title":"Appl. Res. Comput."},{"key":"10_CR16","unstructured":"Yin, D., Zhang, D., Cai, P., et al.: improved sparrows search optimization algorithm and its application. Comput. Eng. Sci. 1\u20138 (2022)"},{"issue":"2","key":"10_CR17","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/4235.771163","volume":"3","author":"X Yao","year":"1999","unstructured":"Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3(2), 82\u2013102 (1999)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1","key":"10_CR18","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1007\/s11277-020-07743-y","volume":"116","author":"V Kumar","year":"2021","unstructured":"Kumar, V., Kaleka, K., Kaur, A.: Spiral-inspired spotted hyena optimizer and its application to constraint engineering problems. Wirel. Pers. Commun. 116(1), 865\u2013881 (2021)","journal-title":"Wirel. Pers. Commun."},{"issue":"09","key":"10_CR19","first-page":"1558","volume":"43","author":"L Liu","year":"2021","unstructured":"Liu, L., Fu, S., Huang, H., et al.: A grey wolf optimization algorithm based on drunkard strolling and reverse learning. Comput. Eng. Sci. 43(09), 1558\u20131566 (2021)","journal-title":"Comput. Eng. Sci."},{"key":"10_CR20","unstructured":"Zhang, X., Zhang, Y., Liu, L., et al.: Improved sparrow search algorithm fused with multiple strategies. Appl. Res. Comput. 39(04), 1086\u20131091+1117 (2022)"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995-International Conference on Neural Networks, vol. 4, pp. 1942\u20131948. IEEE (1995)","DOI":"10.1109\/ICNN.1995.488968"},{"key":"10_CR22","unstructured":"Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: MHS 1995. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39\u201343. IEEE (1995)"}],"container-title":["Communications in Computer and Information Science","Theoretical Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-8152-4_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T04:26:46Z","timestamp":1728534406000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-8152-4_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811981517","9789811981524"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-8152-4_10","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"10 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NCTCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"National Conference of Theoretical Computer Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changchun","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"40","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nctcs2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conf.ccf.org.cn\/TCS2022","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"https:\/\/conf.ccf.org.cn\/TCS2022","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}