A novel improved version of hunger games search algorithm for function optimization and efficient controller design of buck converter system

İzci, Davut; Ekinci, Serdar

doi:10.1016/j.prime.2022.100039

Cited by 26 publications

(17 citation statements)

References 26 publications

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“…The proposed OCSANM was compared with the original CSA (Feng et al, 2021) and two of the best-performed algorithms so far for control of the buck converter system with FOPID controller, namely LFDSA (Izci et al, 2022b) and HGS (Izci and Ekinci, 2022) algorithms. The population and maximum iteration numbers were set to 25 and 40, respectively, and a run time of 30 has been utilized for all the algorithms.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Moreover, the closed-loop transfer functions of the combined system are calculated using these parameters, which are given in equations (28)–(31), respectively. It is also worth reminding that equations (30) and (31) represent the transfer functions of the current best two algorithms that have been proposed for tuning an FOPID controller for a buck converter system, namely LFDSA (Izci et al, 2022b) and HGS (Izci and Ekinci, 2022), respectively.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

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A novel opposition-based hybrid cooperation search algorithm with Nelder–Mead for tuning of FOPID-controlled buck converter

Ersali,

Hekimoğlu

2024

Transactions of the Institute of Measurement and Control

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This paper introduces a novel metaheuristic algorithm named the opposition-based cooperation search algorithm with Nelder–Mead (OCSANM). This enhanced algorithm builds upon the cooperation search algorithm (CSA) by incorporating opposition-based learning (OBL) and the Nelder–Mead simplex search method. The primary application of this algorithm is the design of a fractional-order proportional–integral–derivative (FOPID) controller for a buck converter system. A comprehensive evaluation is conducted using statistical boxplot analysis, nonparametric statistical tests and convergence response comparisons to assess the algorithm’s performance and confirm its superiority over CSA. Furthermore, the FOPID-controlled buck converter system based on OCSANM is compared with two top-performing algorithms: one using a hybridized approach of Lévy flight distribution with simulated annealing (LFDSA) and the other employing the improved hunger games search (IHGS) algorithm. This comparison encompasses transient and frequency responses, performance indices and robustness analysis. The results reveal the notable advantages of the proposed OCSANM-based system, including 25.8% and 8.7% faster rise times, 26% and 8.8% faster settling times compared with the best-performing approaches, namely LFDSA and IHGS, respectively. In addition, the OCSANM-based system exhibits a 34.7% and 9.6% wider bandwidth than the existing approaches-based systems. Incorporating voltage and current responses of the buck converter’s switched circuit with the OCSANM-based FOPID controller further underscores the algorithm’s effectiveness. To provide a comprehensive assessment, the paper also compares the proposed approach’s time and frequency domain responses with those of 17 other state-of-the-art approaches attempting to control buck converter systems similarly. These findings affirm the effectiveness of the OCSANM in designing FOPID controllers for buck converter systems.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Simulation Results and Discussionmentioning

confidence: 99%

A novel opposition-based hybrid cooperation search algorithm with Nelder–Mead for tuning of FOPID-controlled buck converter

Ersali,

Hekimoğlu

2024

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

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“…The unimodal benchmark functions, F1–F7, play a crucial role in evaluating an algorithm's ability to converge to the global optimum. Details of those functions can be found in our previous studies [69–71]. With a single minimum point, these functions assess the algorithm's capacity to efficiently explore the search space and locate the global solution.…”

Section: Classical and Recent Cec Benchmark Functionsmentioning

confidence: 99%

“…Evaluating algorithms on these functions enables us to assess their capability to navigate complex landscapes and escape local optima to find the global optimum. The inclusion of functions such as Schwefel, Rastrigin, Ackley, Griewank, Penalized, and Penalized2 [69–71] in evaluation allows us to thoroughly examine the GOANM algorithm's performance in terms of global optimization in the presence of multiple optima. Fixed‐dimensional multimodal benchmark functions, F14–F23, provide a specific context for evaluating algorithms, where the dimensionality of the search space is predetermined.…”

Section: Classical and Recent Cec Benchmark Functionsmentioning

confidence: 99%

“…These functions simulate real‐world scenarios where the problem space has a fixed number of dimensions. By including functions like Hartman 3, Hartman 6, Foxholes, Six‐Hump Camel, Kowalik, Goldstein‐Price, Branin, Shekel 10, Shekel 7, and Shekel 5 [69–71] in assessment, we can analyse the GOANM algorithm's performance in tackling fixed‐dimensional multimodal problems, showcasing its effectiveness in various real‐world optimization scenarios.…”

Section: Classical and Recent Cec Benchmark Functionsmentioning

confidence: 99%

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Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

Ekinci,

Izci,

Hussien

2024

IET Renewable Power Gen

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The pressing need for sustainable energy solutions has driven significant research in optimizing solar photovoltaic (PV) systems which is crucial for maximizing energy conversion efficiency. Here, a novel hybrid gazelle‐Nelder–Mead (GOANM) algorithm is proposed and evaluated. The GOANM algorithm synergistically integrates the gazelle optimization algorithm (GOA) with the Nelder–Mead (NM) algorithm, offering an efficient and powerful approach for parameter extraction in solar PV models. This investigation involves a thorough assessment of the algorithm's performance across diverse benchmark functions, including unimodal, multimodal, fixed‐dimensional multimodal, and CEC2020 benchmark functions. Notably, the GOANM consistently outperforms other optimization approaches, demonstrating enhanced convergence speed, accuracy, and reliability. Furthermore, the application of the GOANM is extended to the parameter extraction of the single diode and double diode models of RTC France solar cell and PV model of Photowatt‐PWP201 PV module. The experimental results consistently demonstrate that the GOANM outperforms other optimization approaches in terms of accurate parameter estimation, low root mean square values, fast convergence, and alignment with experimental data. These results emphasize its role in achieving superior performance and efficiency in renewable energy systems.

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Controlling an automatic voltage regulator using a novel Harris hawks and simulated annealing optimization technique

2023

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Maintaining the terminal voltage of a power system is a crucial process and this can be achieved via a system named automatic voltage regulator (AVR). However, an AVR needs an appropriate control method. In this context, this article proposes a novel Harris hawks optimization (HHO) and simulated annealing (SA) technique which can be used for AVR. The proposed optimization technique (HHO‐SA) combines the good exploration feature of HHO with the exceptional local search feature of SA. The HHO‐SA algorithm is introduced as a novel design method to obtain the optimum parameters for proportional + integral + derivative plus second order derivative (PID + DD) controller adopted in the AVR. Time domain objective function of the system is effectively minimized and the best PID + DD parameters are obtained. The analysis of statistical tests, convergence, transient and frequency responses, root locus, and disturbance rejection along with robustness are conducted for verifying the efficiency of the HHO‐SA algorithm. Also, the performance of the HHO‐SA tuned PID + DD controller on AVR is compared with the original HHO tuned PID + DD along with PID, FOPID, and PID + DD controllers that are adjusted by state‐of‐the‐art metaheuristic methods. The practical implementation of the proposed controller is also demonstrated in this work. The extensive simulation results and comparisons with the existing controllers adopting the same set of data demonstrate the superior control performance and good robustness of the HHO‐SA tuned PID + DD controller.

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A novel improved version of hunger games search algorithm for function optimization and efficient controller design of buck converter system

Cited by 26 publications

References 26 publications

A novel opposition-based hybrid cooperation search algorithm with Nelder–Mead for tuning of FOPID-controlled buck converter

A novel opposition-based hybrid cooperation search algorithm with Nelder–Mead for tuning of FOPID-controlled buck converter

Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

Controlling an automatic voltage regulator using a novel Harris hawks and simulated annealing optimization technique

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