{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:58:19Z","timestamp":1760709499136,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,10]],"date-time":"2018-01-10T00:00:00Z","timestamp":1515542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Nature-Inspired Computing andMetaheuristics Algorithms for Optimizing DataMining 303 Performance","award":["MYRG2016-00069"],"award-info":[{"award-number":["MYRG2016-00069"]}]},{"name":"A Scalable Data Stream Mining Methodology: Stream-based Holistic Analytics and Reasoning in Parallel","award":["FDCT\/126\/2014\/A3"],"award-info":[{"award-number":["FDCT\/126\/2014\/A3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Nowadays, swarm intelligence algorithms are becoming increasingly popular for solving many optimization problems. The Wolf Search Algorithm (WSA) is a contemporary semi-swarm intelligence algorithm designed to solve complex optimization problems and demonstrated its capability especially for large-scale problems. However, it still inherits a common weakness for other swarm intelligence algorithms: that its performance is heavily dependent on the chosen values of the control parameters. In 2016, we published the Self-Adaptive Wolf Search Algorithm (SAWSA), which offers a simple solution to the adaption problem. As a very simple schema, the original SAWSA adaption is based on random guesses, which is unstable and naive. In this paper, based on the SAWSA, we investigate the WSA search behaviour more deeply. A new parameter-guided updater, the Gaussian-guided parameter control mechanism based on information entropy theory, is proposed as an enhancement of the SAWSA. The heuristic updating function is improved. Simulation experiments for the new method denoted as the Gaussian-Guided Self-Adaptive Wolf Search Algorithm (GSAWSA) validate the increased performance of the improved version of WSA in comparison to its standard version and other prevalent swarm algorithms.<\/jats:p>","DOI":"10.3390\/e20010037","type":"journal-article","created":{"date-parts":[[2018,1,10]],"date-time":"2018-01-10T12:41:10Z","timestamp":1515588070000},"page":"37","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Gaussian Guided Self-Adaptive Wolf Search Algorithm Based on Information Entropy Theory"],"prefix":"10.3390","volume":"20","author":[{"given":"Qun","family":"Song","sequence":"first","affiliation":[{"name":"Department of Computer and Information Science, University of Macau, Macau 999078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon","family":"Fong","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, University of Macau, Macau 999078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suash","family":"Deb","sequence":"additional","affiliation":[{"name":"Decision Sciences and Modelling Program, Victoria University, Melbourne 8001, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5636-1660","authenticated-orcid":false,"given":"Thomas","family":"Hanne","sequence":"additional","affiliation":[{"name":"Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, 4600 Olten, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11721-012-0075-2","article-title":"Swarm robotics: A review from the swarm engineering perspective","volume":"7","author":"Brambilla","year":"2013","journal-title":"Swarm Intell."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hanne, T., and Dornberger, R. 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