{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T10:23:30Z","timestamp":1769163810376,"version":"3.49.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T00:00:00Z","timestamp":1695686400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T00:00:00Z","timestamp":1695686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106046"],"award-info":[{"award-number":["62106046"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106055"],"award-info":[{"award-number":["62106055"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2019A1515110474"],"award-info":[{"award-number":["2019A1515110474"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Real-world optimization problems often have multiple optimal solutions and simultaneously finding these optimal solutions is beneficial yet challenging. Brain storm optimization (BSO) is a relatively new paradigm of swarm intelligence algorithm that has been shown to be effective in solving global optimization problems, but it has not been fully exploited for multimodal optimization problems. A simple control strategy for the step size parameter in BSO cannot meet the need of optima finding task in multimodal landscapes and can possibly be refined and optimized. In this paper, we propose an adaptive BSO (ABSO) algorithm that adaptively adjusts the step size parameter according to the quality of newly created solutions. Extensive experiments are conducted on a set of multimodal optimization problems to evaluate the performance of ABSO and the experimental results show that ABSO outperforms existing BSO algorithms and some recently developed algorithms. BSO has great potential in multimodal optimization and is expected to be useful for solving real-world optimization problems that have multiple optimal solutions.<\/jats:p>","DOI":"10.1007\/s44196-023-00326-2","type":"journal-article","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T11:02:33Z","timestamp":1695726153000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Brain Storm Optimization Algorithm with an Adaptive Parameter Control Strategy for Finding Multiple Optimal Solutions"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5769-3456","authenticated-orcid":false,"given":"Yuhui","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Wenhong","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Shaohao","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Zijia","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,26]]},"reference":[{"issue":"4","key":"326_CR1","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1109\/TEVC.2016.2638437","volume":"21","author":"X Li","year":"2016","unstructured":"Li, X., Epitropakis, M.G., Deb, K., Engelbrecht, A.: Seeking multiple solutions: an updated survey on niching methods and their applications. IEEE Trans. Evol. Comput. 21(4), 518\u2013538 (2016)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"3","key":"326_CR2","first-page":"508","volume":"24","author":"T Huang","year":"2019","unstructured":"Huang, T., Gong, Y.-J., Kwong, S., Wang, H., Zhang, J.: A niching memetic algorithm for multi-solution traveling salesman problem. IEEE Trans. Evol. Comput. 24(3), 508\u2013522 (2019)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"326_CR3","doi-asserted-by":"crossref","unstructured":"Hu, Y., Zhang, K.: Multimodal optimization evolutionary algorithm for RNA secondary structure prediction. In: The Fifth International Conference on Biological Information and Biomedical Engineering, Association for Computing Machinery, Hangzhou, China, pp. 1\u20137 (2021)","DOI":"10.1145\/3469678.3469714"},{"issue":"10","key":"326_CR4","doi-asserted-by":"publisher","first-page":"4225","DOI":"10.1109\/TITS.2019.2939224","volume":"21","author":"T Huang","year":"2019","unstructured":"Huang, T., Gong, Y.-J., Zhang, Y.-H., Zhan, Z.-H., Zhang, J.: Automatic planning of multiple itineraries: a niching genetic evolution approach. IEEE Trans. Intell. Transp. Syst. 21(10), 4225\u20134240 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"326_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2021.126480","volume":"586","author":"JJ Lotf","year":"2022","unstructured":"Lotf, J.J., Azgomi, M.A., Reza, E.D.M.: An improved influence maximization method for social networks based on genetic algorithm. Phys. A Stat. Mech. Appl. 586, 126480 (2022)","journal-title":"Phys. A Stat. Mech. Appl."},{"issue":"2","key":"326_CR6","doi-asserted-by":"publisher","first-page":"697","DOI":"10.3390\/app13020697","volume":"13","author":"RM Aziz","year":"2023","unstructured":"Aziz, R.M., Mahto, R., Goel, K., Das, A., Kumar, P., Saxena, A.: Modified genetic algorithm with deep learning for fraud transactions of ethereum smart contract. Appl. Sci. 13(2), 697 (2023)","journal-title":"Appl. Sci."},{"key":"326_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112866","volume":"140","author":"D Devarriya","year":"2020","unstructured":"Devarriya, D., Gulati, C., Mansharamani, V., Sakalle, A., Bhardwaj, A.: Unbalanced breast cancer data classification using novel fitness functions in genetic programming. Expert Syst. Appl. 140, 112866 (2020)","journal-title":"Expert Syst. Appl."},{"key":"326_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103479","volume":"90","author":"M Pant","year":"2020","unstructured":"Pant, M., Zaheer, H., Garcia-Hernandez, L., Abraham, A., et al.: Differential evolution: a review of more than two decades of research. Eng. Appl. Artif. Intell. 90, 103479 (2020)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"326_CR9","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1016\/j.swevo.2018.06.010","volume":"44","author":"KR Opara","year":"2019","unstructured":"Opara, K.R., Arabas, J.: Differential evolution: a survey of theoretical analyses. Swarm Evol. Comput. 44, 546\u2013558 (2019)","journal-title":"Swarm Evol. Comput."},{"key":"326_CR10","doi-asserted-by":"crossref","unstructured":"Hansen, N.: A global surrogate assisted CMA-ES. In: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, Prague, Czech Republic, pp. 664\u2013672 (2019)","DOI":"10.1145\/3321707.3321842"},{"key":"326_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2019.100627","volume":"52","author":"R Biedrzycki","year":"2020","unstructured":"Biedrzycki, R.: Handling bound constraints in CMA-ES: an experimental study. Swarm Evol. Comput. 52, 100627 (2020)","journal-title":"Swarm Evol. Comput."},{"key":"326_CR12","doi-asserted-by":"publisher","first-page":"10031","DOI":"10.1109\/ACCESS.2022.3142859","volume":"10","author":"TM Shami","year":"2022","unstructured":"Shami, T.M., El-Saleh, A.A., Alswaitti, M., Al-Tashi, Q., Summakieh, M.A., Mirjalili, S.: Particle swarm optimization: a comprehensive survey. IEEE Access 10, 10031\u201310061 (2022)","journal-title":"IEEE Access"},{"key":"326_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2021.100868","volume":"63","author":"EH Houssein","year":"2021","unstructured":"Houssein, E.H., Gad, A.G., Hussain, K., Suganthan, P.N.: Major advances in particle swarm optimization: theory, analysis, and application. Swarm Evol. Comput. 63, 100868 (2021)","journal-title":"Swarm Evol. Comput."},{"key":"326_CR14","doi-asserted-by":"publisher","first-page":"3775","DOI":"10.1007\/s00500-020-05406-5","volume":"25","author":"N Rokbani","year":"2021","unstructured":"Rokbani, N., Kumar, R., Abraham, A., Alimi, A.M., Long, H.V., Priyadarshini, I., Son, L.H.: Bi-heuristic ant colony optimization-based approaches for traveling salesman problem. Soft Comput. 25, 3775\u20133794 (2021)","journal-title":"Soft Comput."},{"key":"326_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105139","volume":"114","author":"X Zhou","year":"2022","unstructured":"Zhou, X., Ma, H., Jianggang, G., Chen, H., Deng, W.: Parameter adaptation-based ant colony optimization with dynamic hybrid mechanism. Eng. Appl. Artif. Intell. 114, 105139 (2022)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"2","key":"326_CR16","first-page":"156","volume":"8","author":"A Ullah","year":"2019","unstructured":"Ullah, A.: Artificial bee colony algorithm used for load balancing in cloud computing. IAES Int. J. Artif. Intell. 8(2), 156 (2019)","journal-title":"IAES Int. J. Artif. Intell."},{"key":"326_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105311","volume":"115","author":"E Kaya","year":"2022","unstructured":"Kaya, E., Gorkemli, B., Akay, B., Karaboga, D.: A review on the studies employing artificial bee colony algorithm to solve combinatorial optimization problems. Eng. Appl. Artif. Intell. 115, 105311 (2022)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"3","key":"326_CR18","doi-asserted-by":"publisher","first-page":"598","DOI":"10.3390\/math11030598","volume":"11","author":"S Ali","year":"2023","unstructured":"Ali, S., Bhargava, A., Saxena, A., Kumar, P.: A hybrid marine predator sine cosine algorithm for parameter selection of hybrid active power filter. Mathematics 11(3), 598 (2023)","journal-title":"Mathematics"},{"key":"326_CR19","unstructured":"Shi, Y.: Brain storm optimization algorithm. In: Advances in Swarm Intelligence: Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part I 2, pp 303\u2013309. Springer (2011)"},{"key":"326_CR20","doi-asserted-by":"crossref","unstructured":"Zhan, Z., Zhang, J., Shi, Y., Liu, H.: A modified brain storm optimization. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1\u20138. IEEE (2012)","DOI":"10.1109\/CEC.2012.6256594"},{"key":"326_CR21","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/s10462-016-9471-0","volume":"46","author":"S Cheng","year":"2016","unstructured":"Cheng, S., Qin, Q., Chen, J., Shi, Y.: Brain storm optimization algorithm: a review. Artif. Intell. Rev. 46, 445\u2013458 (2016)","journal-title":"Artif. Intell. Rev."},{"issue":"1","key":"326_CR22","first-page":"150","volume":"14","author":"X Li","year":"2009","unstructured":"Li, X.: Niching without niching parameters: particle swarm optimization using a ring topology. IEEE Trans. Evol. Comput. 14(1), 150\u2013169 (2009)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"3","key":"326_CR23","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1109\/TEVC.2012.2203138","volume":"17","author":"B-Y Qu","year":"2012","unstructured":"Qu, B.-Y., Suganthan, V., Das, S.: A distance-based locally informed particle swarm model for multimodal optimization. IEEE Trans. Evol. Comput. 17(3), 387\u2013402 (2012)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"326_CR24","unstructured":"Goldberg, D.E., Richardson, J., et\u00a0al.: Genetic algorithms with sharing for multimodal function optimization. In: Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms, vol. 4149. Lawrence Erlbaum, Hillsdale (1987)"},{"key":"326_CR25","doi-asserted-by":"crossref","unstructured":"Thomsen, R.: Multimodal optimization using crowding-based differential evolution. In: Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753), vol.\u00a02, pp. 1382\u20131389. IEEE (2004)","DOI":"10.1109\/CEC.2004.1331058"},{"key":"326_CR26","doi-asserted-by":"crossref","unstructured":"P\u00e9trowski, A.: A clearing procedure as a niching method for genetic algorithms. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 798\u2013803. IEEE (1996)","DOI":"10.1109\/ICEC.1996.542703"},{"issue":"3","key":"326_CR27","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1162\/106365602760234081","volume":"10","author":"J-P Li","year":"2002","unstructured":"Li, J.-P., Balazs, M.E., Parks, G.T., Clarkson, P.J.: A species conserving genetic algorithm for multimodal function optimization. Evol. Comput. 10(3), 207\u2013234 (2002)","journal-title":"Evol. Comput."},{"issue":"5","key":"326_CR28","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1109\/TEVC.2011.2161873","volume":"16","author":"B-Y Qu","year":"2012","unstructured":"Qu, B.-Y., Suganthan, P.N., Liang, J.-J.: Differential evolution with neighborhood mutation for multimodal optimization. IEEE Trans. Evol. Comput. 16(5), 601\u2013614 (2012)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"8","key":"326_CR29","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.1109\/TCYB.2013.2282491","volume":"44","author":"W Gao","year":"2013","unstructured":"Gao, W., Yen, G.G., Liu, S.: A cluster-based differential evolution with self-adaptive strategy for multimodal optimization. IEEE Trans. Cybern. 44(8), 1314\u20131327 (2013)","journal-title":"IEEE Trans. Cybern."},{"key":"326_CR30","doi-asserted-by":"crossref","unstructured":"Epitropakis, M.G., Li, X., Burke, E.K.: A dynamic archive niching differential evolution algorithm for multimodal optimization. In: 2013 IEEE Congress on Evolutionary Computation, pp. 79\u201386. IEEE (2013)","DOI":"10.1109\/CEC.2013.6557556"},{"issue":"2","key":"326_CR31","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1109\/TEVC.2014.2313659","volume":"19","author":"S Biswas","year":"2014","unstructured":"Biswas, S., Kundu, S., Das, S.: Inducing niching behavior in differential evolution through local information sharing. IEEE Trans. Evol. Comput. 19(2), 246\u2013263 (2014)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"10","key":"326_CR32","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1109\/TCYB.2013.2292971","volume":"44","author":"S Biswas","year":"2014","unstructured":"Biswas, S., Kundu, S., Das, S.: An improved parent-centric mutation with normalized neighborhoods for inducing niching behavior in differential evolution. IEEE Trans. Cybern. 44(10), 1726\u20131737 (2014)","journal-title":"IEEE Trans. Cybern."},{"issue":"3","key":"326_CR33","first-page":"347","volume":"21","author":"Y-H Zhang","year":"2016","unstructured":"Zhang, Y.-H., Gong, Y.-J., Zhang, H.-X., Tian-Long, G., Zhang, J.: Toward fast niching evolutionary algorithms: a locality sensitive hashing-based approach. IEEE Trans. Evol. Comput. 21(3), 347\u2013362 (2016)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"3","key":"326_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/JOEUC.302661","volume":"34","author":"S Ma","year":"2022","unstructured":"Ma, S., Wang, Y., Zhang, S.: Improved artificial bee colony algorithm for multimodal optimization based on crowding method. J. Organ. End User Comput. (JOEUC) 34(3), 1\u201318 (2022)","journal-title":"J. Organ. End User Comput. (JOEUC)"},{"issue":"1","key":"326_CR35","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/TCYB.2020.2972907","volume":"52","author":"T Huang","year":"2020","unstructured":"Huang, T., Gong, Y.-J., Chen, W.-N., Wang, H., Zhang, J.: A probabilistic niching evolutionary computation framework based on binary space partitioning. IEEE Trans. Cybern. 52(1), 51\u201364 (2020)","journal-title":"IEEE Trans. Cybern."},{"issue":"1","key":"326_CR36","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1109\/TEVC.2019.2910721","volume":"24","author":"Z-J Wang","year":"2019","unstructured":"Wang, Z.-J., Zhan, Z.-H., Lin, Y., Wei-Jie, Yu., Wang, H., Kwong, S., Zhang, J.: Automatic niching differential evolution with contour prediction approach for multimodal optimization problems. IEEE Trans. Evol. Comput. 24(1), 114\u2013128 (2019)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"4","key":"326_CR37","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1109\/TEVC.2019.2944180","volume":"24","author":"Z-G Chen","year":"2019","unstructured":"Chen, Z.-G., Zhan, Z.-H., Wang, H., Zhang, J.: Distributed individuals for multiple peaks: a novel differential evolution for multimodal optimization problems. IEEE Trans. Evol. Comput. 24(4), 708\u2013719 (2019)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"7","key":"326_CR38","doi-asserted-by":"publisher","first-page":"3343","DOI":"10.1109\/TCYB.2019.2927780","volume":"50","author":"H Zhao","year":"2019","unstructured":"Zhao, H., Zhan, Z.-H., Lin, Y., Chen, X., Luo, X.-N., Zhang, J., Kwong, S., Zhang, J.: Local binary pattern-based adaptive differential evolution for multimodal optimization problems. IEEE Trans. Cybern. 50(7), 3343\u20133357 (2019)","journal-title":"IEEE Trans. Cybern."},{"key":"326_CR39","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.ins.2021.04.093","volume":"573","author":"W Sheng","year":"2021","unstructured":"Sheng, W., Wang, X., Wang, Z., Li, Q., Chen, Y.: Adaptive memetic differential evolution with niching competition and supporting archive strategies for multimodal optimization. Inf. Sci. 573, 316\u2013331 (2021)","journal-title":"Inf. Sci."},{"key":"326_CR40","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/j.ins.2020.09.008","volume":"545","author":"Q Liu","year":"2021","unstructured":"Liu, Q., Du, S., van Wyk, B.J., Sun, Y.: Double-layer-clustering differential evolution multimodal optimization by speciation and self-adaptive strategies. Inf. Sci. 545, 465\u2013486 (2021)","journal-title":"Inf. Sci."},{"key":"326_CR41","doi-asserted-by":"crossref","unstructured":"Ahrari, A., Deb, K.: Multimodal optimization by evolution strategies with repelling subpopulations. In: Preuss, M., Epitropakis, M.G., Li, X., Fieldsend, J.E. (eds) Metaheuristics for Finding Multiple Solutions. Natural Computing Series. Springer, Cham, pp. 145\u2013163 (2021)","DOI":"10.1007\/978-3-030-79553-5_7"},{"issue":"11","key":"326_CR42","doi-asserted-by":"publisher","first-page":"4795","DOI":"10.3390\/app11114795","volume":"11","author":"R Ahmed","year":"2021","unstructured":"Ahmed, R., Nazir, A., Mahadzir, S., Shorfuzzaman, M., Islam, J.: Niching grey wolf optimizer for multimodal optimization problems. Appl. Sci. 11(11), 4795 (2021)","journal-title":"Appl. Sci."},{"key":"326_CR43","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.swevo.2017.05.001","volume":"37","author":"M El-Abd","year":"2017","unstructured":"El-Abd, M.: Global-best brain storm optimization algorithm. Swarm Evol. Comput. 37, 27\u201344 (2017)","journal-title":"Swarm Evol. Comput."},{"key":"326_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107645","volume":"235","author":"F Zhao","year":"2022","unstructured":"Zhao, F., Hu, X., Wang, L., Zhao, J., Tang, J.J.: A reinforcement learning brain storm optimization algorithm (BSO) with learning mechanism. Knowl. Based Syst. 235, 107645 (2022)","journal-title":"Knowl. Based Syst."},{"key":"326_CR45","doi-asserted-by":"publisher","first-page":"126871","DOI":"10.1109\/ACCESS.2019.2939353","volume":"7","author":"Yu Yang","year":"2019","unstructured":"Yang, Yu., Gao, S., Wang, Y., Lei, Z., Cheng, J., Todo, Y.: A multiple diversity-driven brain storm optimization algorithm with adaptive parameters. IEEE Access 7, 126871\u2013126888 (2019)","journal-title":"IEEE Access"},{"key":"326_CR46","doi-asserted-by":"crossref","unstructured":"Zhou, D., Shi, Y., Cheng, S.: Brain storm optimization algorithm with modified step-size and individual generation. In: Ying, T., Yuhui, S., Zhen, J. (eds.) Advances in Swarm Intelligence, pp. 243\u2013252. Springer, Berlin (2012)","DOI":"10.1007\/978-3-642-30976-2_29"},{"key":"326_CR47","unstructured":"Cheng, S., Sun, Y., Chen, J., Qin, Q., Chu, X., Lei, X., Shi, Y.: A comprehensive survey of brain storm optimization algorithms. In: 2017 IEEE Congress on Evolutionary Computation (CEC), San Sebastian, pp, 1637\u20131644 (2017)"},{"key":"326_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2020.100827","volume":"62","author":"Z Dai","year":"2021","unstructured":"Dai, Z., Fang, W., Tang, K., Li, Q.: An optima-identified framework with brain storm optimization for multimodal optimization problems. Swarm Evol. Comput. 62, 100827 (2021)","journal-title":"Swarm Evol. Comput."},{"key":"326_CR49","unstructured":"Li, X., Engelbrecht, A., Epitropakis, M.G.: Benchmark functions for cec\u20192013 special session and competition on niching methods for multimodal function optimization. RMIT University, Evolutionary Computation and Machine Learning Group, Australia, Tech. Rep (2013)"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-023-00326-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-023-00326-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-023-00326-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T04:17:45Z","timestamp":1730175465000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-023-00326-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,26]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["326"],"URL":"https:\/\/doi.org\/10.1007\/s44196-023-00326-2","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,26]]},"assertion":[{"value":"18 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2023","order":3,"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"}}],"article-number":"160"}}