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In the MRFO-PSO, the concept of velocity of the PSO is incorporated to guide the searching process of the MRFO, where the velocity is updated by the first best and the second-best solutions. By this integration, the balancing issue between the exploration phase and exploitation ability has been further improved. To illustrate the robustness and effectiveness of the MRFO-PSO, it is tested on 23 benchmark equations and it is applied to estimate the parameters of Tremblay's model with three different commercial lithium-ion batteries including the Samsung Cylindrical ICR18650-22 lithium-ion rechargeable battery, Tenergy 30209 prismatic cell, Ultralife UBBL03 (type LI-7) rechargeable battery. The study\u00a0contribution exclusively utilizes hybrid machine learning-based tuning for Tremblay's model parameters to overcome the disadvantages of human-based tuning. In addition, the comparisons of the MRFO-PSO with six recent meta-heuristic methods are performed in terms of some statistical metrics and Wilcoxon\u2019s test-based non-parametric test. As a result, the conducted performance measures have confirmed the competitive results as well as the superiority of the proposed MRFO-PSO.<\/jats:p>","DOI":"10.1007\/s44196-022-00114-4","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T16:02:20Z","timestamp":1660147340000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["On a Novel Hybrid Manta Ray Foraging Optimizer and Its Application on Parameters Estimation of Lithium-Ion Battery"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1553-0130","authenticated-orcid":false,"given":"Rizk M.","family":"Rizk-Allah","sequence":"first","affiliation":[]},{"given":"Mohamed I.","family":"Zineldin","sequence":"additional","affiliation":[]},{"given":"Abd Allah A.","family":"Mousa","sequence":"additional","affiliation":[]},{"given":"S.","family":"Abdel-Khalek","sequence":"additional","affiliation":[]},{"given":"Mohamed S.","family":"Mohamed","sequence":"additional","affiliation":[]},{"given":"V\u00e1clav","family":"Sn\u00e1\u0161el","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,10]]},"reference":[{"key":"114_CR1","doi-asserted-by":"publisher","first-page":"105190","DOI":"10.1016\/j.knosys.2019.105190","volume":"191","author":"A Faramarzi","year":"2020","unstructured":"Faramarzi, A., Heidarinejad, M., Stephens, B., Mirjalili, S.: Equilibrium optimizer: a novel optimization algorithm. 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