A modified artificial protozoa optimizer for robust parameter identification in nonlinear dynamic systems

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dc.contributor.authorİzci, Davut
dc.contributor.authorEkinci, Serdar
dc.contributor.authorYüksek, Gökhan
dc.contributor.authorRashdan, Mostafa
dc.contributor.authorBektaş Güneş, Burcu
dc.contributor.authorGüngör, Muhammet İsmail
dc.contributor.authorSalman, Mohammad
dc.date.accessioned2026-02-09T08:45:11Z
dc.date.available2026-02-09T08:45:11Z
dc.date.issued2026
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractAccurate parameter identification in nonlinear and chaotic dynamic systems requires optimization algorithms that can reliably balance global exploration and local refinement in complex, multimodal search landscapes. To address this challenge, a modified artificial protozoa optimizer (mAPO) is developed in this study by embedding two complementary mechanisms into the original artificial protozoa optimizer: a probabilistic random learning strategy to enhance population diversity and global search capability, and a Nelder–Mead simplex-based local refinement stage to improve exploitation and fine-scale solution adjustment. The general optimization performance and scalability of the proposed framework are first evaluated using the CEC2017 benchmark suite. Statistical analyses conducted over shifted and rotated, hybrid, and composition functions demonstrate that mAPO achieves improved mean performance and reduced variability compared with the original APO, indicating enhanced robustness in high-dimensional and complex optimization problems. The effectiveness of mAPO is then examined in nonlinear system identification applications involving chaotic dynamics. Offline and online parameter identification experiments are performed on the Rössler chaotic system and a permanent magnet synchronous motor, including scenarios with abrupt parameter variations. Comparative simulations against APO and several state-of-the-art optimizers show that mAPO consistently yields smaller objective function values, more accurate parameter estimates, and superior statistical stability. In the PMSM case, exact parameter reconstruction with zero error is achieved across all independent runs, while rapid and smooth convergence is observed under both static and time-varying conditions.
dc.identifier.doi10.3390/biomimetics11010065
dc.identifier.issn2313-7673
dc.identifier.issue1
dc.identifier.pmid41589982
dc.identifier.scopus2-s2.0-105028479371
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.3390/biomimetics11010065
dc.identifier.urihttps://hdl.handle.net/11501/2614
dc.identifier.volume11
dc.identifier.wosWOS:001670729400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.institutionauthorBektaş Güneş, Burcu
dc.institutionauthorid0000-0002-9046-1542
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofBiomimetics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial Protozoa Optimizer
dc.subjectNelder–Mead Simplex Method
dc.subjectNonlinear Systems
dc.subjectParameter İdentification
dc.subjectRandom Learning Mechanism
dc.titleA modified artificial protozoa optimizer for robust parameter identification in nonlinear dynamic systems
dc.typeArticle

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