{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:25:08Z","timestamp":1777703108333,"version":"3.51.4"},"reference-count":35,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T00:00:00Z","timestamp":1557964800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2019,6,11]]},"abstract":"<jats:p>\n                    \u00a0As a new and promising swarm intelligence algorithm, brain storm optimization (BSO) has drawn more attention of researches and has been successfully applied to solve the real-world optimization problems. However, too many parameters make the algorithm more complex and greatly limit the convergence performance. Thus, this paper proposed a novel BSO variant, named self-adaptive BSO with\n                    <jats:italic>p<\/jats:italic>\n                    best guided step-size (SPBSO), in which a simple self-adaptive strategy is employed to choose a creating strategy in a random manner rather than depending on several adjustable parameters. In addition, the\n                    <jats:italic>p<\/jats:italic>\n                    best guided step-size and dynamic clustering number are used to accelerate the convergence speed. The experimental studies have been tested on a set of widely used benchmark functions (including the CEC 2014 problems). Experimental results and comparison with the state-of-the-art BSO variants and some recently proposed PSO and DE algorithms, have proved that the proposed algorithm is competitive.\n                  <\/jats:p>","DOI":"10.3233\/jifs-181310","type":"journal-article","created":{"date-parts":[[2019,5,17]],"date-time":"2019-05-17T11:41:41Z","timestamp":1558093301000},"page":"5423-5434","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["SPBSO: self-adaptive brain storm optimization algorithm with\n                    <i>p<\/i>\n                    best guided step-size"],"prefix":"10.1177","volume":"36","author":[{"given":"Hu","family":"Peng","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Jiujiang University, Jiujiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changshou","family":"Deng","sequence":"additional","affiliation":[{"name":"School 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