{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T06:04:29Z","timestamp":1779516269801,"version":"3.53.1"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819509812","type":"print"},{"value":"9789819509829","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-0982-9_11","type":"book-chapter","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T22:21:18Z","timestamp":1759270878000},"page":"128-139","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Competitive Swarm Optimization Through Time-Adaptive Selection Between Adjacency-Guided and\u00a0Random Strategies"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8394-1474","authenticated-orcid":false,"given":"Yang","family":"Cao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4605-5579","authenticated-orcid":false,"given":"Rui","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5029-0294","authenticated-orcid":false,"given":"Jun","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5750-9217","authenticated-orcid":false,"given":"Masaharu","family":"Munetomo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","first-page":"84263","DOI":"10.1109\/ACCESS.2021.3085529","volume":"9","author":"M Premkumar","year":"2021","unstructured":"Premkumar, M., et al.: A new arithmetic optimization algorithm for solving real-world multiobjective CEC-2021 constrained optimization problems: diversity analysis and validations. IEEE Access 9, 84263\u201384295 (2021)","journal-title":"IEEE Access"},{"key":"11_CR2","doi-asserted-by":"publisher","first-page":"55988","DOI":"10.1109\/ACCESS.2022.3177218","volume":"10","author":"Z Zhang","year":"2022","unstructured":"Zhang, Z., Gao, Y., Zuo, W.: A dual biogeography-based optimization algorithm for solving high-dimensional global optimization problems and engineering design problems. IEEE Access 10, 55988\u201356016 (2022)","journal-title":"IEEE Access"},{"key":"11_CR3","doi-asserted-by":"publisher","first-page":"115063","DOI":"10.1109\/ACCESS.2020.3003942","volume":"8","author":"Y Guo","year":"2020","unstructured":"Guo, Y., Zhang, Y., Yu, J., Shen, X.: A spatiotemporal thermo guidance based real-time online ride-hailing dispatch framework. IEEE Access 8, 115063\u2013115077 (2020)","journal-title":"IEEE Access"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Cao, Y., Zhong, R., Yu, J., Munetomo, M.: Optimization of electricity consumption forecasting models via hyper-heuristic algorithm. In: 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS), pp. 114\u2013120 (2024)","DOI":"10.1109\/DOCS63458.2024.10704338"},{"key":"11_CR5","doi-asserted-by":"publisher","first-page":"16150","DOI":"10.1109\/ACCESS.2022.3147821","volume":"10","author":"ON Oyelade","year":"2022","unstructured":"Oyelade, O.N., Ezugwu, A.E.S., Mohamed, T.I.A., Abualigah, L.: Ebola optimization search algorithm: a new nature-inspired metaheuristic optimization algorithm. IEEE Access 10, 16150\u201316177 (2022)","journal-title":"IEEE Access"},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"114443","DOI":"10.1109\/ACCESS.2023.3324061","volume":"11","author":"M Alweshah","year":"2023","unstructured":"Alweshah, M., Almiani, M., Alkhalaileh, S., Kassaymeh, S., Hezzam, E.A., Alomoush, W.: Parallel metaheuristic algorithms for solving imbalanced data classification problems. IEEE Access 11, 114443\u2013114458 (2023)","journal-title":"IEEE Access"},{"issue":"21","key":"11_CR7","doi-asserted-by":"publisher","first-page":"21299","DOI":"10.1109\/JSEN.2022.3203147","volume":"22","author":"YD Yao","year":"2022","unstructured":"Yao, Y.D., Wen, Q., Cui, Y.P., Zhao, F., Zhao, B.Z., Zeng, Y.P.: Coverage enhancement strategy in WMSNs based on a novel swarm intelligence algorithm: army ant search optimizer. IEEE Sens. J. 22(21), 21299\u201321311 (2022)","journal-title":"IEEE Sens. J."},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"124905","DOI":"10.1109\/ACCESS.2020.3007743","volume":"8","author":"Q Zhao","year":"2020","unstructured":"Zhao, Q., Li, C.: Two-stage multi-swarm particle swarm optimizer for unconstrained and constrained global optimization. IEEE Access 8, 124905\u2013124927 (2020)","journal-title":"IEEE Access"},{"key":"11_CR9","doi-asserted-by":"publisher","first-page":"34098","DOI":"10.1109\/ACCESS.2021.3061738","volume":"9","author":"F Dahan","year":"2021","unstructured":"Dahan, F., Hindi, K.E., Ghoneim, A., Alsalman, H.: An enhanced ant colony optimization based algorithm to solve QoS-aware web service composition. IEEE Access 9, 34098\u201334111 (2021)","journal-title":"IEEE Access"},{"issue":"2","key":"11_CR10","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1109\/TCYB.2014.2322602","volume":"45","author":"R Cheng","year":"2015","unstructured":"Cheng, R., Jin, Y.: A competitive swarm optimizer for large scale optimization. IEEE Trans. Cybern. 45(2), 191\u2013204 (2015)","journal-title":"IEEE Trans. Cybern."},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Wu, M., Yang, D., Yang, Z., Guo, Y.: Competitive swarm optimizer for solving flexible jobshop scheduling problem. In: 2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT), pp. 538\u2013542 (2021)","DOI":"10.1109\/ACAIT53529.2021.9731219"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Xing, Y., et al.: Research on optimal control of regional integrated energy system based on competitive swarm optimizer. In: 2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2), pp. 1525\u20131530 (2021)","DOI":"10.1109\/EI252483.2021.9713181"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Zhong, R., Peng, F., Zhang, E., Yu, J., Munetomo, M.: Vegetation evolution with dynamic maturity strategy and diverse mutation strategy for solving optimization problems. Biomimetics 8, 454 (2023)","DOI":"10.3390\/biomimetics8060454"},{"issue":"1","key":"11_CR14","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1109\/TSMC.2020.3002566","volume":"52","author":"YF Zhang","year":"2022","unstructured":"Zhang, Y.F., Chiang, H.D., Wang, T.: A novel trust-tech-enabled trajectory-unified methodology for computing multiple optimal solutions of constrained nonlinear optimization: theory and computation. IEEE Trans. Syst. Man Cybern. Syst. 52(1), 473\u2013484 (2022)","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"issue":"9","key":"11_CR15","doi-asserted-by":"publisher","first-page":"4674","DOI":"10.1109\/TAC.2022.3165481","volume":"67","author":"D Rodrigues","year":"2022","unstructured":"Rodrigues, D., Mesbah, A.: Efficient global solutions to single-input optimal control problems via approximation by sum-of-squares polynomials. IEEE Trans. Autom. Control 67(9), 4674\u20134686 (2022)","journal-title":"IEEE Trans. Autom. Control"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Van\u00a0Thieu, N., Mirjalili, S.: MEALPY: an open-source library for latest meta-heuristic algorithms in Python. J. Syst. Archit. (2023)","DOI":"10.1016\/j.sysarc.2023.102871"},{"key":"11_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2022.129034","volume":"617","author":"N Van Thieu","year":"2023","unstructured":"Van Thieu, N., Barma, S.D., Van Lam, T., Kisi, O., Mahesha, A.: Groundwater level modeling using augmented artificial ecosystem optimization. J. Hydrol. 617, 129034 (2023)","journal-title":"J. Hydrol."},{"key":"11_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107282","volume":"105","author":"AN Ahmed","year":"2021","unstructured":"Ahmed, A.N., Van Lam, T., Hung, N.D., Van Thieu, N., Kisi, O., El-Shafie, A.: A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem. Appl. Soft Comput. 105, 107282 (2021)","journal-title":"Appl. Soft Comput."},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Mohapatra, P., Das, K., Roy, S.: A modified competitive swarm optimizer for large scale optimization problems. Appl. Soft Comput. 59, 340\u2013362 (2017)","DOI":"10.1016\/j.asoc.2017.05.060"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Xiong, G., Zhang, J., Shi, D., Yuan, X.: A simplified competitive swarm optimizer for parameter identification of solid oxide fuel cells. Energy Convers. Manag. 203, 112204 (2020)","DOI":"10.1016\/j.enconman.2019.112204"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Xiong, G., Zhang, J., Shi, D., Zhu, L., Yuan, X., Tan, Z.: Winner-leading competitive swarm optimizer with dynamic Gaussian mutation for parameter extraction of solar photovoltaic models. Energy Convers. Manag. 206, 112450 (2020)","DOI":"10.1016\/j.enconman.2019.112450"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Cao, Y., Zhong, R., Yu, J., Munetomo, M. (2025). Adjacent Distance Matrix-Based Competitive Swarm Optimizer. In: Garc\u00eda-S\u00e1nchez, P., Hart, E., Thomson, S.L. (eds) Applications of Evolutionary Computation. EvoApplications 2025. Lecture Notes in Computer Science, vol 15612. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-031-90062-4_3","DOI":"10.1007\/978-3-031-90062-4_3"}],"container-title":["Lecture Notes in Computer Science","Advances in Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0982-9_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T05:06:07Z","timestamp":1779512767000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0982-9_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,1]]},"ISBN":["9789819509812","9789819509829"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0982-9_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,1]]},"assertion":[{"value":"1 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Swarm Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Yokohama","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"swarm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iasei.org\/icsi2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}