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    (1+PD)-PID cascade controller design for performance betterment of load frequency control in diverse electric power systems
    (Springer London Ltd, 2021) Celik, Emre; Ozturk, Nihat; Arya, Yogendra; Ocak, Cemil
    In our world of today developing incredibly fast, load frequency control (LFC) is an indispensable and vital element in increasing the standard of living of a country by providing a good quality of electric power. To this end, rapid and notable development has been recorded in LFC area. However, researchers worldwide need for the existence of not only effective but also computationally inexpensive control algorithm considering the limitations and difficulties in practice. Hence, this paper deals with the introduction of (1 + PD)-PID cascade controller to the relevant field. The controller is simple to implement and it connects the output of 1 + PD controller with the input of PID controller where the frequency and tie-line power deviation are applied to the latter controller as feedback signals also, which is the first attempt made in the literature. To discover the most optimistic results, controller gains are tuned concurrently by dragonfly search algorithm (DSA). For the certification purpose of the advocated approach, two-area thermal system with/without governor dead band nonlinearity is considered as test systems initially. Then single/multi-area multi-source power systems with/without a HVDC link are employed for the enriched validation purpose. The results of our proposal are analyzed in comparison with those of other prevalent works, which unveil that despite its simplicity, DSA optimized (1 + PD)-PID cascade strategy delivers better performance than others in terms of smaller values of the chosen objective function and settling time/undershoot/overshoot of the frequency and tie-line power deviations following a step load perturbation.
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    A multiobjective crayfish optimization algorithm for simultaneous topology, shape, and size optimization
    (Springer Heidelberg, 2026) Tejani, Ghanshyam; Adalja, D.; Patel, P.; Jangid; Celik, Emre
    It is significantly challenging for design engineers to optimize a truss structure's topology, size, and shape. The improvement problem is then modeled as a multiobjective problem with objectives such as minimizing the structure's weight and maximizing reliability. This paper proposes a robust quality-based multiobjective crayfish optimization algorithm (MOCOA). Six different truss designs are used to test the proposed algorithm. It reveals that in the context of consistency and precision, MOCOA is a better algorithm when compared with recent algorithms, such as MOALO, MOBA, MODA, NSGA-II, DEMO, MOWCA, and MOEA-D, overall Friedman rank. Results are reported as superior in Pareto front, hypervolume, generational distance, and spacing metric. The research paper's finding indicates that MOCOA generates suitable Pareto-optimal solutions possessing strong convergence properties with excellent spread. These findings establish a robust foundation for forthcoming research on the optimization of truss structures.
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    A New Adaptive Fuzzy Logic Controller Configuration for Control Systems Design
    (Wiley, 2026) Celik, Emre
    A new adaptive configuration of fuzzy logic controller (FLCh) based on the hyperbolic function is introduced to improve the performance of control systems. The presented configuration is named adaptive because its input scaling factors (SFs) are dynamically changed while the controller is servicing. This is accomplished by employing two hyperbolic functions with tunable slope and magnitude parameters to act on the input error signal and its derivative nonlinearly. The efficacy of our proposal is explored on a classical PID type FLC (C-PID-FLCh) and the controller parameters are simultaneously tuned using the stochastic fractal search (SFS) algorithm for proper functioning. Extensive simulations are conducted to assess the performance of the presented control scheme. A comparison study is also realised against the existing solutions to prove the true contribution of the work. The results show that thanks to the transient change in input SFs, the C-PID-FLCh exhibits faster yet nonoscillatory behaviour with reference to the reported schemes. The presented configuration does not cause major changes as far as the structure of the classical FLC is concerned, evolving C-PID-FLCh as a potential contender in control systems design.
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    A novel EEFO-tuned cascaded PI-PD controller for nonlinear dynamic regulation of DC-DC buck converters under uncertainty
    (Sage Publications Ltd, 2026) Izci, Davut; Ertugrul, Edip; Ekinci, Serdar; Jabari, Mostafa; Bajaj, Mohit; Blazek, Vojtech; Celik, Emre
    DC-DC buck converters are inherently nonlinear systems that often operate under dynamically changing conditions, parameter uncertainties, and external disturbances, posing significant challenges for conventional control strategies. This paper introduces a novel cascaded proportional-integral and proportional-derivative (PI-PD) controller architecture, in which all controller parameters are optimally tuned using the recently developed Electric Eel Foraging Optimizer (EEFO), a bio-inspired metaheuristic algorithm modeled on the electrolocation and hunting behaviors of electric eels. The proposed control structure uniquely integrates a dual-loop configuration: the inner PI loop eliminates steady-state error, while the outer PD loop enhances dynamic response and mitigates rapid transient fluctuations. This cascaded arrangement enables decoupled tuning of steady-state and transient characteristics, offering superior control flexibility compared to conventional single-loop PID designs. To calibrate the controller, EEFO is employed to minimize a composite performance objective function that simultaneously considers settling time and overshoot, ensuring well-damped and rapid system behavior. A comprehensive set of simulation experiments was conducted in a MATLAB/Simulink environment to evaluate the proposed method against multiple benchmark algorithms-including the flood algorithm, gazelle optimization algorithm, and artificial hummingbird algorithm-as well as classical PID, PID acceleration (PIDA), and fractional-order PID (FOPID) controllers optimized by state-of-the-art metaheuristics. Across all key performance metrics-including rise time, settling time, percentage overshoot, peak time, and steady-state error-the EEFO-tuned cascaded PI-PD controller demonstrated consistently superior results, achieving near-zero overshoot, ultra-fast convergence, and minimal output deviation. Beyond nominal conditions, extensive robustness analyses were conducted to validate the controller's effectiveness under realistic disturbances, such as abrupt load changes, high-frequency measurement noise, time-delay effects in feedback channels, and +/- 10%-15% parametric variations in inductance and capacitance. In all scenarios, the controller retained stable output regulation, confirming its resilience and practical viability. To the best of our knowledge, this is the first study to deploy a cascaded PI-PD control structure specifically designed for DC-DC buck converters and optimized using the EEFO algorithm. The integration of a biologically inspired optimization framework with a decoupled dual-loop control scheme offers both architectural and algorithmic novelty. The proposed strategy addresses critical demands in nonlinear converter regulation and provides a robust, high-performance solution suitable for dynamic and uncertain power electronic environments.
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    A novel hyperbolic tangent-based PID controller tuned by the artificial lemming algorithm for nonlinear steam condenser pressure control
    (Nature Portfolio, 2026) Ekinci, Serdar; Izci, Davut; Jabari, Mostafa; Celik, Emre; Bajaj, Mohit; Vishnuram, Pradeep; Rubanenko, Olena
    Precise pressure regulation in nonlinear shell-and-tube steam condensers is essential for maintaining thermal efficiency and operational safety in power generation plants; however, conventional proportional-integral (PI) and proportional-integral-derivative (PID) controllers struggle with nonlinear dynamics, leading to overshoot, slower settling, and reduced robustness. In this regard, a novel hyperbolic tangent-based PID (tanh-PID) controller is developed in this study to introduce smooth nonlinear gain modulation, enabling enhanced damping behavior and improved transient shaping. The recently introduced artificial lemming algorithm (ALA) is employed to optimally tune the proposed controller for integral of time-weighted absolute error minimization. Extensive simulation studies are performed using a comprehensive nonlinear condenser model incorporating steam-air interactions and hot-well dynamics. The proposed strategy is benchmarked against four competitive optimization algorithms (coati optimization algorithm, dandelion optimizer, success-history based adaptive differential evolution with linear population size reduction, and adaptive artificial electric field algorithm) and compared with state-of-the-art PI and fractional-order PID (FOPID) controllers reported in the literature. The ALA-tuned tanh-PID achieves the lowest integral of time-weighted absolute error (2.1189), fastest rise time (0.5960 s), minimal settling time (12.4799 s) and overshoot (5.8056%), along with near-zero steady-state error (4.0776 x 10(-)4%), outperforming all compared methods in both transient response and steady-state accuracy. Robustness analyses further confirm superior disturbance rejection and reliable reference tracking under dynamic uncertainties. These results demonstrate that the proposed methodology offers an efficient, low-complexity, and high-performance control solution suitable for real-time deployment in industrial steam condenser systems.
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    Adaptive Nonlinear PID Controller for Boosted Load Frequency Management of Grids With Renewables and Energy Storage Units
    (Wiley, 2026) Celik, Emre
    Power inconsistency between generation and demand causes to swing the frequency and tie-line power across the grid. Power-frequency swings undermine the reliability, security and stability of the grid, and may even cause power blackouts. The employment of an effective controller within load frequency management loop is therefore an inevitable necessity. An adaptive nonlinear PID (ANPID) controller enjoying interesting abilities is proposed in this article. Two nonlinear gains based on configurable hyperbolic functions of the error and the rate-of-error are utilized in cascade with the classical PID controller. The nonlinear characteristics of these gains make it possible to obtain a fast yet non-oscillatory response without excessive overshoots. Further, through a mathematical framework, it is revealed that the ANPID controller is equivalent to the PIDD2 controller with adaptive gains. Gains of the ANPID controller are prolifically calibrated using stochastic fractal search (SFS) algorithm. Performance achieved is tested for various power grids and a practical speed servo system under different circumstances. Furthermore, a comprehensive comparison against the state-of-the-art is also established to appraise the literal contribution of SFS calibrated ANPID controller. The results affirm the supremacy of our proposal over previously published control architectures concerning minimum values of settling time/undershoot/error criterion of the frequency and tie-line power swings.
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    AGC performance amelioration in multi-area interconnected thermal and thermal-hydro-gas power systems using a novel controller
    (Elsevier - Division Reed Elsevier India Pvt Ltd, 2021) Arya, Yogendra; Dahiya, Pankaj; Celik, Emre; Sharma, Gulshan; Gozde, Haluk; Nasiruddin, Ibraheem
    Due to varying structure, random load demands, nonlinearities, parameters ambiguity, steadily escalating size and intricacy of the interconnected power system (IPS), automatic generation control (AGC) is treated as one of the biggest crucial issues in IPS. Hence, expert, intelligent and robust control scheme is indispensable for stable operation of IPS and supply of electricity under sudden load demand disturbances. In vision of this, in this work, a novel cascade fuzzy-proportional integral derivative incorporating filter (PIDN)-fractional order PIDN (FPIDN-FOPIDN) controller is offered as an expert control strategy to deal effectively with AGC issue of IPS. Imperialist competitive algorithm is prolifically utilized for optimizing the controller parameters. Initially, a two area non-reheat thermal IPS is studied in detail and next to attest the efficacy of the technique, the study is extended to realistic two-area multi-source thermal-hydro-gas and reheat thermal three-area systems. The prominent benefit of cascade FPIDN-FOPIDN strategy comprises its great lethargy to large load demands and its supremacy over various latest intelligent classical/fuzzy controllers. The control strategy beats several techniques concerning significant lesser settling time, oscillations, over/under shoots and different performance index values. Finally, a robustness investigation is performed in order to validate the robustness of the controller. (C) 2020 Karabuk University. Publishing services by Elsevier B.V.
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    Application of Monte Carlo simulation and stochastic fractional search algorithm for solar PV placement considering diverse solar radiations
    (Elsevier, 2025) Barutcu, Ibrahim Cagri; Sharma, Gulshan; Bokoro, Pitshou N.; Celik, Emre
    This study employs Monte Carlo Simulation (MCS) within the structure of the Stochastic Fractional Search Algorithm (SFSA) to address circumstances involving uncertainty. The goal is to improve the system's performance by creating probability distribution functions for bus voltages and branch currents. We will use the resultant distribution in chance-constrained stochastic scheduling. The objective of the present research is to analyze the impact of uncertainties in the operation of photovoltaic (PV) systems, specifically in relation to different solar radiation conditions, on the amount of power loss. The approach focuses on including stochastic constraints in distribution systems instead of depending solely on precise deterministic boundaries. The goal is to enhance efficiency and ensure optimal consumption of power. This research enhances the knowledge base on PV unit positioning in distribution systems by integrating meta-heuristic optimization and MCS into a comprehensive framework. The investigation centers on the implementation of a chance-constrained method. We evaluate the optimization results using MCS under various uncertainty scenarios to demonstrate the effectiveness of the recommended approach. Furthermore, we conduct an analysis to assess the likelihood of exceeding the system's boundaries. The strategy's effectiveness is assessed by comparing the results of the SFSA with the Firefly algorithm (FA) utilizing probabilistic evaluation and simulation.
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    Application of the 2-archive multi-objective cuckoo search algorithm for structure optimization
    (Nature Portfolio, 2024) Tejani, Ghanshyam G.; Mashru, Nikunj; Patel, Pinank; Sharma, Sunil Kumar; Celik, Emre
    The study suggests a better multi-objective optimization method called 2-Archive Multi-Objective Cuckoo Search (MOCS2arc). It is then used to improve eight classical truss structures and six ZDT test functions. The optimization aims to minimize both mass and compliance simultaneously. MOCS2arc is an advanced version of the traditional Multi-Objective Cuckoo Search (MOCS) algorithm, enhanced through a dual archive strategy that significantly improves solution diversity and optimization performance. To evaluate the effectiveness of MOCS2arc, we conducted extensive comparisons with several established multi-objective optimization algorithms: MOSCA, MODA, MOWHO, MOMFO, MOMPA, NSGA-II, DEMO, and MOCS. Such a comparison has been made with various performance metrics to compare and benchmark the efficacy of the proposed algorithm. These metrics comprehensively assess the algorithms' abilities to generate diverse and optimal solutions. The statistical results demonstrate the superior performance of MOCS2arc, evidenced by enhanced diversity and optimal solutions. Additionally, Friedman's test & Wilcoxon's test corroborate the finding that MOCS2arc consistently delivers superior optimization results compared to others. The results show that MOCS2arc is a highly effective improved algorithm for multi-objective truss structure optimization, offering significant and promising improvements over existing methods.
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    Commutation current ripple minimization of brushless DC motor drive based on programmed phase current references
    (Springer, 2021) Celik, Emre; Ozturk, Nihat
    Although direct phase current control of brushless DC motor prevents commutation current ripple at low speed, it occurs at high speed which has not received the deserved attention in the literature. Dealing with this current ripple is of practical significance because commutation becomes more influential for high speeds as its duration and the current ripple's amplitude increase with speed. This paper concerns with the successful application of a fuzzy logic estimator (FLE) as an expert control technique to minimize the so-called current ripple profitably. Phase current reference waveforms are programmed as a function of commanded current, angular position, and commutation angle which is adjusted online by the developed FLE as per the motor working condition. The presented approach renders the current references with changing but equal slopes during commutation to keep the other phase current constant at all times. A genetic algorithm is also deployed to optimize the FLE's rule table. Unlike the reported researches, this study does not require calculating commutation time, and use of torque observer and/or commutation detection circuits. The acceptability of our proposal is widely illustrated by simulated and experimental results using DSP TMS320F28335, which signifies that prolific performance toward commutation current ripple minimization is achieved.
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    Design of new fractional order PI-fractional order PD cascade controller through dragonfly search algorithm for advanced load frequency control of power systems
    (Springer, 2021) Celik, Emre
    Owing to integrating the dense range of distinct electric power sources, high volume of power generation units, abrupt and continuous changes in load demand, and rising utilization of power electronics, the electric power system (EPS) is striving for high-performance control schemes to counterwork the concerns depicted above. Additionally, it is highly creditable to have the controller structure as simple as possible from a viewpoint of practical implementation. Thus, this paper describes a virgin application of fractional order proportional integral-fractional order proportional derivative (FOPI-FOPD) cascade controller for load frequency control (LFC) of electric power generating systems. The proposed controller includes fractional order PI and fractional order PD controllers connected in cascade wherein orders of integrator (lambda) and differentiator (mu) may be fractional. The gains and fractional order parameters of the controller are concurrently tuned using recently proposed dragonfly search algorithm (DSA) by minimizing the integral time absolute error (ITAE) of frequency and tie-line power deviations. DSA is the mathematical model and computer simulation of static and dynamic swarming behaviors of dragonflies in nature, and its implementation in LFC studies is very rare, unveiling additional research gap to be bridged. Performance of the advocated approach is first explored on popular two-area thermal PS with/without governor dead band (GDB) nonlinearity and then on three-area hydrothermal PS with suitable generation rate constraints. To highlight the prominence and universality of our proposal, the work is extended to single-/multi-area multi-source EPSs. Several comparisons with DSA optimized FOPID controller and the relevant recent works for each test system indicate the contribution of proposed DSA optimized FOPI-FOPD cascade controller in alleviating settling time/undershoot/overshoot of frequency and tie-line power oscillations.
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    Design of Novel Exponential PDN Controller via Quadratic Interpolation Optimiser for Nonlinear and Unstable Ball and Beam System
    (Wiley, 2026) Izci, Davut; Ekinci, Serdar; Celik, Emre; Uyar, Murat; Bajaj, Mohit; Blazek, Vojtech; Rubanenko, Olena
    This study presents a novel exponential proportional-derivative controller with filter (exp-PDN) for stabilising the nonlinear and underactuated ball and beam system. Unlike conventional PID-based approaches, the proposed controller removes the integral term, resulting in faster transient responses and improved robustness. It incorporates nonlinear exponential shaping of both the error and its derivative, along with a filtered derivative path for enhanced noise handling. A custom multi-objective cost function, comprising the squared error, settling time, and percent overshoot, is proposed to evaluate control performance. The quadratic interpolation optimiser (QIO), a recently developed metaheuristic based on analytical interpolation, is employed to optimise the controller parameters. To validate its effectiveness, the exp-PDN controller is compared against five state-of-the-art metaheuristic algorithms: QIO, spider wasp optimiser, komodo mlipir algorithm, golden eagle optimiser, and slime mould algorithm. The QIO-optimised exp-PDN achieves the best performance, with the lowest cost value (0.3211), minimal overshoot (5.52%), fast rise time (0.97 s), and smallest steady-state error (4.1643 x 10- 4). Further comparisons with QIO-optimised phase-lead and PID-with-filter controllers demonstrate the superiority of the proposed method in both transient and steady-state behaviour. In summary, this work advances the control of nonlinear unstable systems by delivering a structurally simple yet highly responsive control architecture. The combination of dual-channel exponential shaping and efficient metaheuristic optimisation results in state-of-the-art closed-loop performance, highlighting the practical value of the proposed exp-PDN framework for real-world control applications.
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    Design optimization and real-time implementation of an LSPMSM for efficiency enhancement
    (Nature Portfolio, 2025) Ocak, Cemil; Yenipinar, Burak; Celik, Emre; Abdel-Salam, Mahmoud; Tejani, Ghanshyam G.; Mousavirad, Seyed Jalaleddin
    This study presents the design optimization and experimental validation of a Line-Start Permanent Magnet Synchronous Motor (LSPMSM) aimed at achieving IE4 efficiency class. An IE1 class induction motor (IM) was used as a reference. Only the rotor structure was modified, while the stator geometry, winding, and mechanical components were kept unchanged. The optimization process focused on rotor slot geometry, magnet placement, magnet dimensions, and core length, employing a Multi-Objective Genetic Algorithm (MOGA) to maximize efficiency while maintaining cost-effectiveness. Following the optimization, six candidate designs were evaluated based on demagnetization prediction, synchronization performance, and starting torque capability. Among them, Design C demonstrated the highest overall performance. Finite Element Analysis (FEA) confirmed that Design C met IE4 efficiency standards with a calculated efficiency of 92.15%. This result was later experimentally verified at 91.95% through thermal testing. The study further examined the cost and payback period scenarios for adopting LSPMSMs in industrial applications. Three implementation strategies were analyzed: replacing only the rotor, purchasing a new IE4 LSPMSM instead of an IE1 motor, and replacing an operational IE1 motor with an IE4 LSPMSM. The results indicated that efficiency improvement could be achieved with minimal modifications. The payback period varied depending on the investment strategy. The findings demonstrate that high-efficiency LSPMSMs can serve as direct replacements for induction motors, offering energy savings and improved performance while maintaining compatibility with existing motor housings and components.
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    Designing a cascaded exponential PID controller via starfish optimizer for DC motor and liquid level systems
    (Nature Portfolio, 2025) Izci, Davut; Jabari, Mostafa; Celik, Emre; Ekinci, Serdar; Bajaj, Mohit; Rubanenko, Olena; Prokop, Lukas
    In this study, a novel cascaded exponential proportional-integral-derivative (exp-PID) controller tuned by the starfish optimization algorithm (SFOA) is proposed for enhancing the transient and steady-state performance of nonlinear dynamic systems. The design objective is to achieve improved adaptability, robustness, and precision under varying operating conditions and external disturbances. The exponential PID structure introduces nonlinear modulation in the proportional and derivative components, enabling smoother control action and superior damping characteristics compared to conventional PID and fractional-order PID designs. The proposed SFOA-based exp-PID controller is validated on two benchmark systems: a DC motor speed control system and a three-tank liquid-level process. Across multiple independent trials, the controller achieved outstanding results, with the DC motor system attaining a rise time of 0.0039 s, settling time of 0.0083 s, and zero overshoot, while the three-tank system reached a rise time of 1.72 s, settling time of 2.47 s, overshoot of 1.5%, and steady-state error of 9.22 x 10(-)(5)%. Comparative analyses with recently developed algorithms (including the flood algorithm, greater cane rat algorithm, mantis search algorithm, and dandelion optimizer) as well as previously reported methods demonstrate the superior convergence behavior, stability, and accuracy of the proposed controller. Statistical evaluations further confirm the method's robustness and consistent performance across repeated runs.
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    Dynamic load frequency control in Power systems using a hybrid simulated annealing based Quadratic Interpolation Optimizer
    (Nature Portfolio, 2024) Izci, Davut; Ekinci, Serdar; Celik, Emre; Bajaj, Mohit; Blazek, Vojtech; Prokop, Lukas
    Ensuring the stability and reliability of modern power systems is increasingly challenging due to the growing integration of renewable energy sources and the dynamic nature of load demands. Traditional proportional-integral-derivative (PID) controllers, while widely used, often fall short in effectively managing these complexities. This paper introduces a novel approach to load frequency control (LFC) by proposing a filtered PID (PID-F) controller optimized through a hybrid simulated annealing based quadratic interpolation optimizer (hSA-QIO). The hSA-QIO uniquely combines the local search capabilities of simulated annealing (SA) with the global optimization strengths of the quadratic interpolation optimizer (QIO), providing a robust and efficient solution for LFC challenges. The key contributions of this study include the development and application of the hSA-QIO, which significantly enhances the performance of the PID-F controller. The proposed hSA-QIO was evaluated on unimodal, multimodal, and low-dimensional benchmark functions, to demonstrate its robustness and effectiveness across diverse optimization scenarios. The results showed significant improvements in solution quality compared to the original QIO, with lower objective function values and faster convergence. Applied to a two-area interconnected power system with hybrid photovoltaic-thermal power generation, the hSA-QIO-tuned controller achieved a substantial reduction in the integral of time-weighted absolute error by 23.4%, from 1.1396 to 0.87412. Additionally, the controller reduced the settling time for frequency deviations in Area 1 by 9.9%, from 1.0574 s to 0.96191 s, and decreased the overshoot by 8.8%. In Area 2, the settling time was improved to 0.89209 s, with a reduction in overshoot by 4.8%. The controller also demonstrated superior tie-line power regulation, achieving immediate response with minimal overshoot.
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    Effective speed control of brushless DC motor using cascade 1PDf-PI controller tuned by snake optimizer
    (Springer London Ltd, 2024) Celik, Emre; Karayel, Mehmet
    This paper introduces a cascade one proportional derivative incorporating filter (1PDf)-proportional integral (PI) controller abbreviated as c-1PDf-PI to deal effectively with the speed control issue of brushless DC (BLDC) motors. Two problems exist with implementing this controller such as iterated integral overflow and derivation-based chattering owing to the noise. The former is resolved by using an equivalent expression for the integral operation, while the latter is addressed by putting a first-order filter on the derivative term. To achieve the best performance from the controller, snake optimizer (SO) is fruitfully employed for optimizing the controller parameters without need for expert knowledge/interpretation. Here, a more reasonable cost function to assess the candidate solutions is also described. Simulations and laboratory experiments using DSP of TI TMS320F28335 are performed and the results are presented which show that the reference tracking performance, torque disturbance capability and robustness of the c-1PDf-PI controller have potential. These results are also contrasted by those offered by PI and 1PDf speed control schemes individually, affirming the superior performance of our proposal. As per the results, discussion and observation of this research, we stress that good performance and simplicity are salient advantages of the c-1PDf-PI controller, rendering it a good alternative over the complicated controller designs.
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    Elite Bernoulli-based mutated dung beetle algorithm for global complex problems and parameter estimation of solar photovoltaic models
    (Nature Portfolio, 2025) Elhosseny, Mohamed; Abdel-Salam, Mahmoud; Nayyar, Anand; Celik, Emre; Mahajan, Shubham; Bacanin, Nebojsa; El-Hasnony, Ibrahim M.
    The Dung Beetle Optimization (DBO) algorithm is a relatively recent metaheuristic known for its simplicity, versatility, and low parameter dependence, making it a valuable tool for solving complex optimization problems. Despite its potential, DBO suffers from limitations such as slow convergence and premature stagnation in local optima. To address these critical issues, this paper introduces a novel enhanced variant named Elite Bernoulli-based Mutated Dung Beetle Optimizer with Local Escaping Operator (EBMLO-DBO), specifically designed to improve the convergence speed, search capability, and robustness of the original DBO algorithm. The motivation for this enhancement stems from DBO's limited performance in high-dimensional and non-convex problems, where it often fails to maintain an effective balance between exploration and exploitation. The novelty of the proposed EBMLO-DBO lies in the integration of four key strategies tailored to overcome these weaknesses: (i) Bernoulli map-based initialization to enhance population diversity and ensure a better global search foundation; (ii) Morlet Wavelet mutation to introduce adaptive local refinements and help the algorithm escape local optima; (iii) elite guidance to accelerate convergence by directing the population toward high-quality regions; and (iv) a local escaping operator (LEO) to dynamically refine the search process and strengthen exploitation without sacrificing exploration. The performance of EBMLO-DBO is rigorously validated using the CEC2017 and CEC2022 benchmark suites, where it achieves Friedman ranks of 1.83 and 2.7 respectively, consistently surpassing eleven state-of-the-art algorithms including PSO, HHO, WOA, and advanced methods like CMAES and IMODE. In benchmark function optimization, EBMLO-DBO demonstrates superior performance by achieving first rank in 50% of CEC2022 functions and obtaining the lowest average fitness values in 18 out of 29 CEC2017 functions. For photovoltaic parameter estimation applications, EBMLO-DBO exhibits exceptional accuracy with RMSE values of 9.8602E-4 for single diode models, 9.81307E-4 for double diode models, and 2.32066E-3 for PV module models, achieving top performance ranks of 1.45, 1.42, and 1.74, respectively. Statistical analysis using Wilcoxon signed-rank test at significance level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha =0.05$$\end{document} confirms the significant superiority of EBMLO-DBO over all compared algorithms, thereby validating the effectiveness and reliability of the proposed enhancements. Overall, the results state that EBMLO-DBO offers a significantly improved search performance and solution quality compared to the original DBO and related methods, thereby justifying the necessity and effectiveness of the proposed enhancements.
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    Enhanced automatic voltage regulation using an extended PIDA controller optimised by the snake algorithm
    (Elsevier, 2025) Chetty, Nelson Dhanpal; Gandhi, Ravi; Sharma, Gulshan; Celik, Emre; Kumar, Rajesh
    Maintaining voltage stability within acceptable limits is crucial in power systems, with Automatic Voltage Regulation (AVR) ensuring consistent performance. Traditionally, PID controllers have been widely used; however, they struggle in complex, nonlinear environments with fluctuating conditions and disturbances. This study proposes an Extended PID-Acceleration (ePIDA) controller incorporating a novel state observer-based Disturbance Observer (DOB) for enhanced voltage regulation. The Snake Optimiser (SO) is introduced for the first time in AVR tuning, leveraging its dynamic leader-follower mechanism to achieve faster convergence and optimal controller gains. The SO-ePIDA framework extends the traditional PIDA structure with a three-degree-offreedom (3DOF) approach, enhancing setpoint tracking and disturbance rejection. The proposed approach is evaluated against six widely used optimisation strategies through comparative statistical and graphical analyses, considering step-load variations and system parameter settings. Results demonstrate that the SO-ePIDA controller achieves a rise time of 0.1679 s, a settling time of 0.3123 s, and the lowest ISTAE value of 0.0046, ensuring superior transient response and steady-state accuracy. Furthermore, under a 30 % step-load disturbance, the proposed controller exhibits the fastest recovery time of 0.1065 s, significantly outperforming other methods. The AVR system was tested with +25 % and +50 % variations in system parameters to assess robustness under parametric uncertainty. The results confirm that the SO-ePIDA controller maintains stability, with rise time deviations limited to 0.1577 and 0.2004 s and ISTAE variations between 0.0116 and 0.1891, demonstrating strong adaptability under extreme operating conditions. These findings establish the SO-ePIDA framework as a robust, high-performance solution for real-world AVR applications.
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    An enhanced sea-horse optimizer for solving global problems and cluster head selection in wireless sensor networks
    (Springer, 2024) Houssein, Essam H.; Saad, Mohammed R.; Celik, Emre; Hu, Gang; Ali, Abdelmgeid A.; Shaban, Hassan
    An efficient variant of the recent sea horse optimizer (SHO) called SHO-OBL is presented, which incorporates the opposition-based learning (OBL) approach into the predation behavior of SHO and uses the greedy selection (GS) technique at the end of each optimization cycle. This enhancement was created to avoid being trapped by local optima and to improve the quality and variety of solutions obtained. However, the SHO can occasionally be vulnerable to stagnation in local optima, which is a problem of concern given the low diversity of sea horses. In this paper, an SHO-OBL is suggested for the tackling of genuine and global optimization systems. To investigate the validity of the suggested SHO-OBL, it is compared with nine robust optimizers, including differential evolution (DE), grey wolf optimizer (GWO), moth-flame optimization algorithm (MFO), sine cosine algorithm (SCA), fitness dependent optimizer (FDO), Harris hawks optimization (HHO), chimp optimization algorithm (ChOA), Fox optimizer (FOX), and the basic SHO in ten unconstrained test routines belonging to the IEEE congress on evolutionary computation 2020 (CEC'20). Furthermore, three different design engineering issues, including the welded beam, the tension/compression spring, and the pressure vessel, are solved using the proposed SHO-OBL to test its applicability. In addition, one of the most successful approaches to data transmission in a wireless sensor network that uses little energy is clustering. In this paper, SHO-OBL is suggested to assist in the process of choosing the optimal power-aware cluster heads based on a predefined objective function that takes into account the residual power of the node, as well as the sum of the powers of surrounding nodes. Similarly, the performance of SHO-OBL is compared to that of its competitors. Thorough simulations demonstrate that the suggested SHO-OBL algorithm outperforms in terms of residual power, network lifespan, and extended stability duration.
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    Enhancing automatic generation control in renewable energy-integrated thermal power systems with a novel PID+Iλ controller tuned by INFO algorithm
    (Sage Publications Ltd, 2025) Can, Ozay; Ayas, Mustafa Sinasi; Celik, Emre
    Electrical systems need to balance generation and demand to ensure that customers are supplied with safe and high-quality electricity. Failure to maintain this balance may result in unwanted frequency oscillation and, accordingly, tie-line power variation. Automatic generation control (AGC) is an important mechanism for controlling system responses and keeping them within predetermined bounds. Integrating renewable energy sources (RESs) into the grid can be complicated due to their erratic and weather-dependent nature, leading to imbalances in generation and consumption. To reduce these disparities, this study focuses on developing a novel proportional-integral-derivative (PID)+I-lambda controller for the AGC in a two-area thermal power system utilizing wind turbines and photovoltaic (PV) panels as renewables. For achieving the best possible performance, the controller parameters are optimized using the weighted mean of vectors (INFO) algorithm. Simulation studies are conducted to evaluate the performance of the proposed controller under different load demands and RES scenarios. Comparative studies are also conducted to evaluate the true efficacy of the INFO-tuned PID+I-lambda controller against some published control schemes available in the literature. It is found that our proposal outperforms its rivals in mitigating the unwanted system oscillations, lessening the overshoot/undershoot, and shortening the settling time of frequency and tie-line power responses.