{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T14:44:47Z","timestamp":1783521887063,"version":"3.55.0"},"reference-count":37,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T00:00:00Z","timestamp":1687132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Hunan Province","award":["2021JJ50137"],"award-info":[{"award-number":["2021JJ50137"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>A memristor is a kind of nonlinear electronic component. Parameter identification for memristive chaotic systems is a multi-dimensional variable optimization problem. It is one of the key issues in chaotic control and synchronization. To identify the unknown parameters accurately and quickly, we introduce, in this paper, a modified Pelican Optimization Algorithm (POA) called the fractional-order chaotic Pareto Pelican Optimization Algorithm (FPPOA). First, the pelican population\u2019s diversity is augmented with the integration of a fractional chaotic sequence. Next, the utilization of the Pareto distribution is incorporated to alter the hunting strategy of pelicans in the POA. These measures are effective in hastening the speed of finding an optimal solution and circumventing local optimization issues. Thirdly, the FPPOA is used to determine the values of the parameters of the simplest memristive chaotic system, which has a property of conditional symmetry. The proposed algorithm was evaluated during simulations, where it was utilized to solve six objective functions of varying unimodal and multimodal types. The performance of the FPPOA exceeds three traditional swarm intelligence optimization algorithms. In the parameter identification experiment, the results for the parameters with the FPPOA had error rates all within a 1% range. Extensive testing shows that our new strategy has a faster rate of convergence and better optimization performance than some other traditional swarm algorithms.<\/jats:p>","DOI":"10.3390\/sym15061279","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T03:04:40Z","timestamp":1687143880000},"page":"1279","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A New Pelican Optimization Algorithm for the Parameter Identification of Memristive Chaotic System"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0396-4836","authenticated-orcid":false,"given":"Qi","family":"Xiong","sequence":"first","affiliation":[{"name":"International College, Hunan University of Arts and Sciences, Changde 415000, China"},{"name":"School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi\u2019an Jiao Tong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jincheng","family":"She","sequence":"additional","affiliation":[{"name":"International College, Hunan University of Arts and Sciences, Changde 415000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinkun","family":"Xiong","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1109\/JPROC.2012.2190812","article-title":"Memristors: Devices, models, and applications","volume":"100","author":"Mazumder","year":"2012","journal-title":"Proc. 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