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Yayın A modified artificial protozoa optimizer for robust parameter identification in nonlinear dynamic systems(Multidisciplinary Digital Publishing Institute (MDPI), 2026) İzci, Davut; Ekinci, Serdar; Yüksek, Gökhan; Rashdan, Mostafa; Bektaş Güneş, Burcu; Güngör, Muhammet İsmail; Salman, MohammadAccurate 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.Yayın A novel gudermannian function-driven controller architecture optimized by starfish optimizer for superior transient performance of automatic voltage regulation(Multidisciplinary Digital Publishing Institute (MDPI), 2026) İzci, Davut; Ekinci, Serdar; Jabari, Mostafa; Kocaman, Behçet; Bektaş Güneş, Burcu; Adas, Enver; Ahmad, Mohd AshrafThis paper proposes a Gudermannian function-based proportional-integral-derivative (G-PID) controller to enhance the transient performance of automatic voltage regulator (AVR) systems operating under highly dynamic conditions. By embedding the smooth and bounded nonlinear mapping of the Gudermannian function into the classical PID structure, the proposed controller improves adaptability to large signal variations while effectively suppressing overshoot. The controller parameters are optimally tuned using the starfish optimization algorithm (SFOA), which provides a robust balance between exploration and exploitation in nonlinear search spaces. Simulation results demonstrate that the SFOA-optimized G-PID controller achieves superior transient performance, with a rise time of 0.0551 s, zero overshoot, and a settling time of 0.0830 s. Comparative evaluations confirm that the proposed approach outperforms widely used optimization algorithms (particle swarm optimization, grey wolf optimizer, success history-based adaptive differential evolution with linear population size, and Kirchhoff's law algorithm) and advanced AVR control schemes, including fractional-order and higher-order PID-based designs. These results indicate that the proposed SFOA optimized G-PID controller offers a computationally efficient and structurally simple solution for high-performance voltage regulation in modern power systems.Yayın A novel hyperbolic tangent-augmented controller framework for temperature control in jacketed continuous stirred tank reactors(Nature Research, 2026) İzci, Davut; Ekinci, Serdar; Ökten, İrfan; Çınar, Rıdvan Fırat; Rashdan, Mostafa; Salman, Mohammad; Bektaş Güneş, Burcu; Ahmad, Mohd AshrafAccurate temperature regulation of jacketed continuous stirred tank reactors (CSTRs) remains a challenging task due to strong nonlinearities, tight coupling between mass and energy balances, and sensitivity to disturbances and operating-point variations. In this study, a novel augmented proportional–integral–derivative (PID) controller incorporating a hyperbolic tangent nonlinearity (APID-T) is proposed for robust temperature control of an exothermic CSTR. The controller structure extends the classical PID framework by embedding a bounded nonlinear term that enhances transient shaping and robustness while preserving simplicity and practical implementability. The tuning of the APID-T parameters is formulated as a constrained nonlinear optimization problem, where a composite objective function combining normalized overshoot and integral squared error is minimized. To solve this problem efficiently, the recently developed Schrödinger optimizer (SRA) is employed, exploiting its balanced exploration–exploitation mechanism. A detailed nonlinear dynamic model of the jacketed CSTR is considered, and stability characteristics around the nominal operating point are examined to ensure meaningful closed-loop operation. The proposed SRA-based APID-T design is extensively evaluated through comparative simulations against several state-of-the-art metaheuristic optimizers and alternative controller structures, including PI, PID with filter, two-degree-of-freedom PID, and fractional-order PID controllers. Performance is assessed using statistical indicators, convergence behavior, and time-domain response metrics under identical optimization settings. In addition, widely used error performance criteria, including the integral squared error, integral time absolute error, and integral time squared error, are computed to provide a comprehensive quantitative assessment of the tracking performance. The results demonstrate that the SRA-tuned APID-T controller consistently achieves lower objective-function values, faster convergence, reduced settling time, and significantly smaller overshoot compared with the competing approaches. Furthermore, frequency-domain analysis based on the Bode characteristics of the linearized open-loop system confirms favorable stability margins, supporting the robustness of the proposed control structure. Additional stability and robustness evaluations are conducted under practical non-ideal conditions, including feed-temperature disturbances, measurement noise, and multiple setpoint variations, where the controller maintains stable and accurate temperature regulation across the considered operating scenarios.Yayın Design of PI-PDN controlled DFIG system via a new objective function and starfish optimizer(Institute of Electrical and Electronics Engineers Inc., 2025) İzci, Davut; Artun, Fatma; Ekinci, Serdar; Ghandour, Raymond; Salman, Mohammad; Ghith, EhabThis study presents a novel approach to transient performance regulation in doubly fed induction generator (DFIG) systems by employing a cascaded proportional-integral (PI) and proportional-derivative with a filter (PDN) controller tuned via the starfish optimization algorithm (SFOA). To improve both transient and steady-state response, a custom scalar objective function is formulated, integrating rise time, settling time, overshoot, and steady-state error. The designed controller structure combines an outer PI loop and an inner filtered PDN loop, allowing precise reference tracking and enhanced robustness. The performance of the proposed SFOA-based controller is rigorously evaluated against well-established optimization algorithms including genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), grey wolf optimizer (GWO), and synergistic swarm optimization algorithm (SSOA). Statistical performance, optimal parameter values, and detailed time-domain simulations are provided for comparison. The results clearly demonstrate that the SFOA-tuned PI-PDN controller delivers superior consistency, minimal overshoot, and faster dynamic response. This performance is validated across multiple trials, highlighting the potential of SFOA as an effective tool for control system design in renewable energy applications.Yayın Enhanced temperature control of continuous stirred tank reactors using QIO-based 2-DoF PID controller(Universitas Muhammadiyah Yogyakarta, 2025) Ekinci, Serdar; İzci, Davut; Jabari, Mostafa; Ma'arif, AlfianAccurate temperature control of continuous stirred tank reactors (CSTRs) remains a major challenge due to the nonlinear dynamics and inherent time delay of the system. Conventional proportional-integral-derivative (PID) controllers often struggle to maintain optimal performance under such complexities, highlighting the need for more advanced control strategies. In this study, a two-degree-of-freedom (2-DOF) PID controller is designed and optimized using the quadratic interpolation optimization (QIO) to enhance temperature regulation in CSTRs. The proposed approach aims to minimize steady-state error, settling time, and overshoot. To implement this method, the nonlinear model of the CSTR is linearized around a stable operating point, and the controller parameters are tuned by minimizing a composite cost function consisting of normalized overshoot and instantaneous error. Simulation results demonstrate that the QIO-based 2-DOF PID controller significantly outperforms other metaheuristic approaches such as differential evolution, particle swarm optimization, slime mould algorithm, and greater cane rat algorithm. Furthermore, comparisons with recent works reveal substantial improvements in rise time, settling time, and steady-state accuracy.Yayın Global-best-guided electric eel foraging optimizer for robust parameter identification of Lorenz and memristive chaotic systems(Nature Research, 2026) İzci, Davut; Ekinci, Serdar; Ökten, İrfan; Tümen, Vedat; Bektaş Güneş, Burcu; Rashdan, Mostafa; Salman, MohammadAccurate parameter identification in chaotic dynamical systems constitutes a challenging inverse problem due to extreme sensitivity to initial conditions, pronounced nonlinearity, and highly multimodal error landscapes. To address these challenges, this study proposes a global-best-guided electric eel foraging optimization algorithm (g-EEFO), which enhances the original EEFO framework by embedding a behavior-aware and phase-dependent global learning mechanism. Unlike existing EEFO variants that rely solely on stochastic foraging dynamics, g-EEFO integrates global-best information as a soft cooperative signal that modulates the interacting, resting, hunting, and migrating behaviors without overriding them. In this way, global guidance acts as a directional bias rather than a dominant attractor, preserving ecological diversity while strengthening convergence coherence. For the first time, EEFO and its improved variant are applied to chaotic system parameter estimation. The proposed method is evaluated on two representative models: the classical Lorenz system and a structurally richer memristive chaotic system. Extensive numerical experiments, including statistical analysis, convergence profiling, boxplot distributions, and parameter-evolution trajectories, demonstrate the clear superiority of g-EEFO over several state-of-the-art metaheuristics. For the Lorenz system, g-EEFO achieves a best mean squared error of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:7.02\times\:{10}<^>{-26}$$\end{document}, which is six to twenty orders of magnitude lower than competing methods, while maintaining an exceptionally small standard deviation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:4.58\times\:{10}<^>{-20}$$\end{document}). For the memristive system, g-EEFO attains a best error of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:8.19\times\:{10}<^>{-19}$$\end{document}, again outperforming all benchmarks by several orders of magnitude and exhibiting the highest run-to-run stability. In both cases, the estimated parameters match the true system values with near-perfect precision. These results confirm that the proposed behavior-aware global guidance fundamentally reshapes the search dynamics of EEFO, yielding substantial gains in convergence stability, numerical accuracy, and robustness. The g-EEFO therefore provides a powerful and reliable alternative for chaotic parameter identification and nonlinear system reconstruction across diverse dynamical regimes.Yayın High-precision parameter identification for the lorenz system via starfish optimization algorithm(Institute of Electrical and Electronics Engineers Inc., 2025) Ekinci, Serdar; Türkeri, Cebrail; İzci, Davut; Kiselychnyk, Oleh; Bektaş Güneş, BurcuParameter estimation plays a vital role in the accurate modeling and control of chaotic systems due to their inherent sensitivity to initial conditions and system parameters. This study introduces a novel approach for parameter identification in the Lorenz system using the starfish optimization algorithm (SFOA). The problem is formulated as a nonlinear optimization task, and the SFOA is employed to minimize the discrepancy between the observed and simulated trajectories. The proposed method is evaluated across multiple independent runs and benchmarked against several well-known optimization techniques. The results demonstrate that the SFOA achieves highly accurate and consistent parameter estimates, exhibiting superior robustness and convergence behavior compared to other methods. These findings suggest that SFOA is a promising candidate for addressing parameter estimation challenges in chaotic and nonlinear dynamical systems.Yayın Hybrid learning-driven golden jackal optimizer for reliable parameter estimation of nonlinear memristive chaotic systems(Springer Science and Business Media B.V., 2026) İzci, Davut; Ekinci, Serdar; Rizk-Allah, Rizk M.; Tümen, Vedat; Rashdan, Mostafa; Salman, Mohammad; Bektaş Güneş, Burcu; İnağ, YasinAccurate identification of parameters in chaotic and nonlinear systems is essential for ensuring precise modeling, control, and prediction of complex dynamical behaviors. However, conventional metaheuristic algorithms often struggle to maintain an effective balance between exploration and exploitation, leading to premature convergence and estimation inaccuracies. To address these challenges, this study proposes an enhanced golden jackal optimizer (en-GJO) that integrates three complementary mechanisms (Laplacian crossover learning, elite group learning, and opposition repair learning). These hybrid strategies collectively strengthen population diversity, accelerate convergence, and prevent stagnation, thereby improving both the global search capability and local refinement accuracy of the original GJO. The effectiveness of the en-GJO is first validated through extensive benchmarking on twenty-three standard test functions, including unimodal, multimodal, and fixed-dimensional multimodal problems. Comparative results against nine well-established metaheuristics (such as SSA, SCA, HHO, AEO, EO, GBO, RUN, and ARO) demonstrate that en-GJO achieves superior convergence precision and robustness, consistently yielding the lowest mean and standard-deviation values across all categories. To further verify its real-world applicability, the en-GJO is applied to the parameter identification of a memristive chaotic system, formulated as a nonlinear optimization problem using a least-squares-based objective function. Simulation results reveal that the proposed method attains the most accurate estimates of the system parameters \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left( {a,b,c,d} \right)$$\end{document}, with negligible deviation from their true values. Statistical analyses and convergence profiles confirm that en-GJO not only converges faster but also delivers more stable and repeatable performance than competing algorithms. In comparative evaluations with reported techniques such as PSO, ABC, SPSSA, GWO, POA, and FPPOA, the en-GJO achieves the smallest cost value (1.3850 x 10-13) and with a mean fitness of 1.0507 x 10-9 and a standard deviation of 2.5392 x 10-9, outperforming all compared algorithms by several orders of magnitude. The estimated system parameters converge to their true values with error rates below 0.001%, confirming the high accuracy, stability, and repeatability of the proposed approach. In summary, the proposed en-GJO offers a highly accurate, stable, and computationally efficient solution for parameter estimation in nonlinear and chaotic systems.Yayın Information sufficiency limits in metaheuristicbased IIR system identification under multi-level observation reduction(Institute of Electrical and Electronics Engineers Inc., 2026) Çınar, Rıdvan Fırat; Bektaş Güneş, Burcu; Türkeri, Cebrail; Ekinci, SerdarThis study examines information-induced limits of parameter identifiability in IIR system identification under multi-level observation representations. A fixed populationbased optimization framework is employed to isolate information effects from algorithmic variability, while the observation model is progressively reduced in information content. Using a persistently exciting white Gaussian input, three observation scenarios are analysed: full time-domain measurements, magnitude-only spectral observations, and band-limited magnitude representations. Although numerical convergence is achieved under all scenarios, parameter estimation accuracy deteriorates systematically as observation information decreases, as reflected in the distribution of relative parameter errors. Fisher information-based indicators are evaluated as complementary sensitivity measures and reveal consistent reductions in local information volume, while also illustrating their limitations under nonlinear and many-to-one observation mappings. The results indicate that identifiability loss originates from structural information insufficiency in the observation model and cannot be compensated by optimization alone.Yayın Master–slave architecture enhanced and improved GBO tuned cascaded PI-PDN controller for speed regulation of DC motors(Wiley, 2025) İzci, Davut; Ekinci, Serdar; Rizk-Allah, Rizk Masoud; Alribdi, Nada İbrahim; Smerat, Aseel; Alzahrani, Ahmed; Alwadain, Ayed; Snasel, Vaclav; Abualigah, LaithThis study introduces a novel master-slave architecture featuring an improved gradient-based optimizer (ImGBO) to effectively tune a cascaded proportional-integral (PI) and proportional-derivative with filter (PDN) controller specifically for DC motor speed regulation. The core novelty of this work lies in enhancing the traditional GBO algorithm by integrating an experience-based perturbed learning mechanism and an adaptive local search strategy, significantly enhancing its ability to balance exploration and exploitation during optimization. The proposed ImGBO-based cascaded PI-PDN controller is comprehensively evaluated against traditional GBO, recent metaheuristics and advanced proportional-integral-derivative (PID) and fractional-order PID (FOPID) controllers. Significant improvements were observed, with the proposed method demonstrating exceptionally short rise (0.0089 s) and settling times (0.0140 s), no overshoot, and minimal steady-state error (0.0017%). Stability analysis via pole placement and Bode plots affirmed the robust and stable operation of the controller, exhibiting a phase margin of 71.6640 degrees and infinite gain margin. These results strongly support the suitability and effectiveness of the ImGBO-based approach for precision-critical DC motor control applications.Yayın Nonlinear control of engine speed regulation using grey wolf optimizer for enhanced system stability and performance(Universitas Muhammadiyah Yogyakarta, 2025) Ekinci, Serdar; İzci, Davut; Jabari, Mostafa; Ma'arif, AlfianAccurate control of internal combustion engine speed, especially under variable load conditions, has always been a significant challenge in the automotive industry. Classical PID controllers often fail to effectively compensate for nonlinearities and environmental disturbances in spark ignition (SI) engines. To address this issue, we propose a method based on tuning PIDF controller parameters using the grey wolf optimizer (GWO) to enhance system stability and performance. This approach aims to reduce steady-state error, settling time, and overshoot. A mathematical model of the engine speed control system is developed, and the GWO algorithm is applied to optimize the PIDF gains. The performance of the GWO-based controller is then compared to other metaheuristic methods such as particle swarm optimization (PSO), differential evolution (DE), and cuckoo search (CS) algorithms through simulation. Simulation results demonstrate that the proposed GWO-based approach outperforms the alternatives by achieving better error reduction, improved stability, enhanced disturbance rejection, and faster response times.Yayın Opposition-based starfish optimization algorithm for function optimization(Institute of Electrical and Electronics Engineers Inc., 2025) Ekinci, Serdar; Türkeri, Cebrail; İzci, Davut; Kiselychnyk, Oleh; Bektaş Güneş, BurcuThis paper proposes a novel hybrid optimization algorithm, termed the opposition-based starfish optimization algorithm (OB-SFOA), designed for solving complex function optimization problems. By integrating the opposition-based learning (OBL) mechanism into the standard starfish optimization algorithm (SFOA), the proposed method aims to enhance population diversity, improve convergence speed, and reduce the risk of stagnation in local optima. The performance of OB-SFOA is evaluated using six widely used benchmark functions (Rosenbrock, Step, Schwefel, Penalized, Kowalik, and Shekel 5) covering diverse optimization characteristics including unimodality, multimodality, and fixed dimensionality. Comparative evaluations were conducted against standard SFOA, grey wolf optimizer (GWO), and RIME (an emerging physics-based algorithm). Results indicate that OB-SFOA consistently outperforms the competing algorithms in terms of average fitness, stability, and best-found solutions across all benchmark cases. These outcomes demonstrate the robustness and effectiveness of OB-SFOA as a promising candidate for general-purpose function optimization tasks.Yayın Quadratic interpolation optimization-based 2DoF-PID controller design for highly nonlinear continuous stirred-tank heater process(Nature Portfolio, 2025) Ekinci, Serdar; İzci, Davut; Bajaj, Mohit; Blazek, Vojtech; Prokop, LukasTemperature control in continuous stirred tank heater (CSTH) systems is essential for ensuring energy efficiency, safety, and product quality in industrial processes. However, the nonlinear dynamics and external disturbances make conventional proportional-integral-derivative (PID) control inadequate for reliable operation. This study presents a novel two-degrees-of-freedom PID (2DoF-PID) controller optimized using the quadratic interpolation optimization (QIO) algorithm to enhance CSTH temperature regulation. The QIO-based approach allows independent tuning for setpoint tracking and disturbance rejection, overcoming the limitations of classical PID controllers. Extensive nonlinear time-domain simulations, reference tracking, and disturbance rejection tests demonstrate the superior performance of the proposed controller in terms of reduced overshoot, faster settling time, and minimal steady-state error. Furthermore, comparative evaluations with traditional tuning methods (Murrill and Rovira) and several state-of-the-art metaheuristic optimizers (DE, PSO, FLA, MGO) validate the effectiveness and robustness of the QIO-optimized strategy. This work introduces a pioneering application of the QIO algorithm in industrial temperature control, offering a scalable and cost-efficient solution for complex nonlinear systems.Yayın Reliable parameter estimation of nonlinear chaotic systems and PMSMs with the stellar oscillation optimizer(Nature Research, 2026) Ekinci, Serdar; İzci, Davut; Jabari, Mostafa; Elsayed, Fahmi; Salman, Mohammad; Bektaş Güneş, BurcuAccurate parameter identification is a critical prerequisite for reliable modeling, analysis, and control of nonlinear dynamical systems. This study introduces the stellar oscillation optimizer (SOO), a recently proposed metaheuristic inspired by the oscillatory behavior of stars, and investigates its effectiveness in estimating system parameters through a unified optimization framework. The identification problem is formulated as the minimization of a trajectory-mismatch cost function, where candidate solutions are iteratively refined by the oscillatory dynamics of SOO. To comprehensively evaluate its performance, four benchmark systems were considered: three canonical chaotic models (Lorenz, Chen, and R & ouml;ssler) and a practical engineering case represented by a permanent-magnet synchronous motor (PMSM). The outcomes were benchmarked against several state-of-the-art algorithms, including Kirchhoff's law algorithm (KLA), Tianji's horse racing optimization (THRO), puma optimizer (PO), and hiking optimization algorithm (HOA), under a standardized protocol. The results show that SOO consistently achieves numerically convergent solutions with machine-precision-level residuals under deterministic and noise-free simulation settings, while maintaining strong robustness across independent runs. In chaotic benchmarks, the reported residuals approach floating-point limits, which indicates stable numerical convergence rather than guaranteed physical identifiability under real measurement conditions. On the PMSM model, SOO demonstrates accurate and repeatable parameter estimation within the adopted simulation framework.Yayın Spider wasp optimizer-based PID control approach for temperature management in continuous stirred tank reactors(Institute of Electrical and Electronics Engineers Inc., 2025) İzci, Davut; Jabari, Mostafa; Ekinci, Serdar; Ghandour, Raymond; Salman, MohammadTemperature control in continuous stirred tank reactors (CSTRs) poses a significant challenge due to the inherent nonlinearity of the process and the presence of time delays. These complexities often result in unstable and inefficient performance of traditional control systems. In this paper, a novel approach based on optimizing a PID-F controller using the spider wasp optimizer (SWO) is proposed to address temperature management in CSTRs. This method leverages SWO's computational capabilities to finely tune the controller parameters, effectively overcoming the challenges of nonlinearity and time delays. To implement this solution, the CSTR's mathematical model was linearized and approximated as a stable first-order plus time delay (SFOPTD) model. The performance of SWO in optimizing the PID-F controller was then compared with other established algorithms (CFOA, FLA, and MPA) through simulation studies. Simulation results demonstrate that the proposed SWO-based approach achieves a minimum rise time (0.0126 s), the lowest overshoot (0.1249%), and a fast settling time (0.0645 s). These outcomes highlight SWO's superior performance over competing algorithms, underscoring its potential for industrial applications in CSTR control.
