{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T04:56:21Z","timestamp":1772772981536,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,7,26]],"date-time":"2017-07-26T00:00:00Z","timestamp":1501027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Fuzzy logic is a soft computing technique that has been very successful in recent years when it is used as a complement to improve meta-heuristic optimization. In this paper, we present a new variant of the bio-inspired optimization algorithm based on the self-defense mechanisms of plants in the nature. The optimization algorithm proposed in this work is based on the predator-prey model originally presented by Lotka and Volterra, where two populations interact with each other and the objective is to maintain a balance. The system of predator-prey equations use four variables (\u03b1, \u03b2, \u03bb, \u03b4) and the values of these variables are very important since they are in charge of maintaining a balance between the pair of equations. In this work, we propose the use of Type-2 fuzzy logic for the dynamic adaptation of the variables of the system. This time a fuzzy controller is in charge of finding the optimal values for the model variables, the use of this technique will allow the algorithm to have a higher performance and accuracy in the exploration of the values.<\/jats:p>","DOI":"10.3390\/a10030085","type":"journal-article","created":{"date-parts":[[2017,7,26]],"date-time":"2017-07-26T11:45:01Z","timestamp":1501069501000},"page":"85","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["A New Meta-Heuristics of Optimization with Dynamic Adaptation of Parameters Using Type-2 Fuzzy Logic for Trajectory Control of a Mobile Robot"],"prefix":"10.3390","volume":"10","author":[{"given":"Camilo","family":"Caraveo","sequence":"first","affiliation":[{"name":"Division of Graduate Studies and Research, Tijuana Institute of Technology, 22414 Tijuana, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0159-0407","authenticated-orcid":false,"given":"Fevrier","family":"Valdez","sequence":"additional","affiliation":[{"name":"Division of Graduate Studies and Research, Tijuana Institute of Technology, 22414 Tijuana, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7385-5689","authenticated-orcid":false,"given":"Oscar","family":"Castillo","sequence":"additional","affiliation":[{"name":"Division of Graduate Studies and Research, Tijuana Institute of Technology, 22414 Tijuana, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2017,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.asoc.2016.02.033","article-title":"Optimization of fuzzy controller design using a new bee colony algorithm with fuzzy dynamic parameter adaptation","volume":"43","author":"Caraveo","year":"2016","journal-title":"Appl. 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