{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:59:34Z","timestamp":1771703974913,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,12,27]],"date-time":"2018-12-27T00:00:00Z","timestamp":1545868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003141","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["122"],"award-info":[{"award-number":["122"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This paper presents a comparison among the bee colony optimization (BCO), differential evolution (DE), and harmony search (HS) algorithms. In addition, for each algorithm, a type-1 fuzzy logic system (T1FLS) for the dynamic modification of the main parameters is presented. The dynamic adjustment in the main parameters for each algorithm with the implementation of fuzzy systems aims at enhancing the performance of the corresponding algorithms. Each algorithm (modified and original versions) is analyzed and compared based on the optimal design of fuzzy systems for benchmark control problems, especially in fuzzy controller design. Simulation results provide evidence that the FDE algorithm outperforms the results of the FBCO and FHS algorithms in the optimization of fuzzy controllers. Statistically is demonstrated that the better errors are found with the implementation of the fuzzy systems to enhance each proposed algorithm.<\/jats:p>","DOI":"10.3390\/a12010009","type":"journal-article","created":{"date-parts":[[2018,12,27]],"date-time":"2018-12-27T11:29:43Z","timestamp":1545910183000},"page":"9","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Comparative Study in Fuzzy Controller Optimization Using Bee Colony, Differential Evolution, and Harmony Search Algorithms"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7385-5689","authenticated-orcid":false,"given":"Oscar","family":"Castillo","sequence":"first","affiliation":[{"name":"Tijuana Institute of Technology, c.p 22379 Tijuana, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0159-0407","authenticated-orcid":false,"given":"Fevrier","family":"Valdez","sequence":"additional","affiliation":[{"name":"Tijuana Institute of Technology, c.p 22379 Tijuana, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9","family":"Soria","sequence":"additional","affiliation":[{"name":"Tijuana Institute of Technology, c.p 22379 Tijuana, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leticia","family":"Amador-Angulo","sequence":"additional","affiliation":[{"name":"Tijuana Institute of Technology, c.p 22379 Tijuana, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patricia","family":"Ochoa","sequence":"additional","affiliation":[{"name":"Tijuana Institute of Technology, c.p 22379 Tijuana, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cinthia","family":"Peraza","sequence":"additional","affiliation":[{"name":"Tijuana Institute of Technology, c.p 22379 Tijuana, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eswa.2016.08.024","article-title":"Cost and risk aggregation in multi-objective route planning for hazardous materials transportation\u2014A neuro-fuzzy and artificial bee colony approach","volume":"65","year":"2016","journal-title":"Expert Syst. 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