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Öğe Boosted sooty tern optimization algorithm for global optimization and feature selection(Elsevier Ltd, 2023) Houssein, Essam H.; Oliva, D.; Çelik, Emre; Emam, M.M.; Ghoniem, R.M.Feature selection (FS) represents an optimization problem that aims to simplify and improve the quality of highly dimensional datasets through selecting prominent features and eliminating redundant and irrelevant data to classify results better. The goals of FS comprise dimensionality reduction and enhancing the classification accuracy in general, accompanied by great significance in different fields like data mining applications, pattern classification, and data analysis. Using powerful optimization algorithms is crucial to obtaining the best subsets of information in FS. Different metaheuristics, such as the Sooty Tern Optimization Algorithm (STOA), help to optimize the FS problem. However, such kind of techniques tends to converge in sub-optimal solutions. To overcome this problem in the STOA, an improved version called mSTOA is introduced. It employs the balancing exploration/exploitation strategy, self-adaptive of the control parameters strategy, and population reduction strategy. The proposed approach is proposed for solving the FS problem, but also it has been validated over benchmark optimization problems from the CEC 2020. To assess the performance of the mSTOA, it has also been tested with different algorithms. The experiments in terms of FS provide qualitative and quantitative evidence of the capabilities of the mSTOA for extracting the optimal subset of features. Besides, statistical analyses and no-parametric tests were also conducted to validate the result obtained by the mSTOA in optimization. © 2022 Elsevier LtdÖğe 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, HassanAn 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.Öğe An improved honey badger algorithm for global optimization and multilevel thresholding segmentation: real case with brain tumor images(Springer, 2024) Houssein, Essam H.; Emam, Marwa M.; Singh, Narinder; Samee, Nagwan Abdel; Alabdulhafith, Maali; Celik, EmreGlobal optimization and biomedical image segmentation are crucial in diverse scientific and medical fields. The Honey Badger Algorithm (HBA) is a newly developed metaheuristic that draws inspiration from the foraging behavior of honey badgers. Similar to other metaheuristic algorithms, HBA encounters difficulties associated with exploitation, being trapped in local optima, and the pace at which it converges. This study aims to improve the performance of the original HBA by implementing the Enhanced Solution Quality (ESQ) method. This strategy helps to prevent becoming stuck in local optima and speeds up the convergence process. We conducted an assessment of the enhanced algorithm, mHBA, by utilizing a comprehensive collection of benchmark functions from IEEE CEC'2020. In this evaluation, we compared mHBA with well-established metaheuristic algorithms. mHBA demonstrates exceptional performance, as shown by both qualitative and quantitative assessments. Our study not only focuses on global optimization but also investigates the field of biomedical image segmentation, which is a crucial process in numerous applications involving digital image analysis and comprehension. We specifically focus on the problem of multi-level thresholding (MT) for medical image segmentation, which is a difficult process that becomes more challenging as the number of thresholds needed increases. In order to tackle this issue, we suggest a revised edition of the standard HBA, known as mHBA, which utilizes the ESQ approach. We utilized this methodology for the segmentation of Magnetic Resonance Images (MRI). The evaluation of mHBA utilizes existing metrics to gauge the quality and performance of its segmentation. This evaluation showcases the resilience of mHBA in comparison to many established optimization algorithms, emphasizing the effectiveness of the suggested technique.Öğe Improved load frequency control of interconnected power systems using energy storage devices and a new cost function(Springer London Ltd, 2023) Çelik, Emre; Öztürk, Nihat; Houssein, Essam H.This paper investigates the use of energy storage devices (ESDs) as back-up sources to escalate load frequency control (LFC) of power systems (PSs). The PS models implemented here are 2-area linear and nonlinear non-reheat thermal PSs besides 3-area nonlinear hydro-thermal PS. PID controller is employed as secondary controller in each control area and ESDs such as battery energy storage system, flywheel energy storage system and ultra-capacitor are employed to assist LFC task during crest load disturbances. PID controller parameters are optimized by salp swarm algorithm (SSA) using a new cost function. This function is innovative, improving system stability by increasing stability margin of the system. Contribution of the proposed approach are thoroughly justified by contrasting it against the renowned works in the state-of-the-art. The comparison analysis clearly unveils that SSA optimized PID controller with ESDs is able to significantly reduce settling time and unwanted oscillations of frequency and tie-line power deviations with a greater stability margin. Our proposal is also more economic than the existing solutions considering the trade-off between simplicity and effectiveness.Öğe Improving speed control characteristics of PMDC motor drives using nonlinear PI control(Springer London Ltd, 2024) Celik, Emre; Bal, Gungor; Ozturk, Nihat; Bekiroglu, Erdal; Houssein, Essam H.; Ocak, Cemil; Sharma, GulshanThis paper introduces a nonlinear PI controller for improved speed regulation in permanent magnet direct current (PMDC) motor drive systems. The nonlinearity comes from the exponential (Exp) block placed in front of the classical PI controller, which uses a tunable exponential function to map the speed error nonlinearly. Such a configuration has not been studied till now, thus meriting further investigation. We consider an exponential PI (EXP-PI) controller and to attain the best performance from this controller, its parameters are optimized offline using salp swarm algorithm (SSA), which borrows its inspiration from the way of forage and navigation of salps living in deep oceans. To indicate the credibility of SSA tuned EXP-PI controller convincingly, numerous experiments on speed regulation in PMDC motor have been implemented using DSP of TMS320F28335. The results obtained are also compared to similar results in the literature. It is shown that the proposed approach performs well in practice by ensuring tight tracking of the speed reference and superb torque disturbance rejection for the closed loop control. Furthermore, superior performance is achieved by the proposed nonlinear PI controller with respect to a fixed-gain PI controller.Öğe Influence of energy storage device on load frequency control of an interconnected dual-area thermal and solar photovoltaic power system(Springer London Ltd, 2022) Çelik, Emre; Öztürk, Nihat; Houssein, Essam H.The mismatch between power generation and load demand causes unwanted fluctuations in frequency and tie-line power, and load frequency control (LFC) is an inevitable mechanism to compensate the mismatch. For this issue, this paper explores the influence of energy storage device (ESD) on ameliorating the LFC performance for an interconnected dual-area thermal and solar photovoltaic (PV) power system. Initially, to alleviate the frequency and tie-line power deviations, a proportional-integral (PI) controller is chosen and utilized in the system due to its effectiveness and simplicity in practice. For achieving the highest performance from this controller, salp swarm algorithm (SSA) is employed to search for optimal controller parameters by using integral of time-multiplied absolute error (ITAE) criterion. To affirm the contribution of SSA optimized PI controller, it is contrasted with a recent approach utilizing PI controller optimized by genetic algorithm (GA) and firefly algorithm (FA). It is observed that the results acquired for SSA are better than for GA and FA. To improve the system performance further, ESD such as redox flow battery (RFB) famous for its excellent disturbance rejection capability is integrated with the thermal power unit for the first time in the literature. It is divulged from the results that the system performance with RFB has boosted considerably with regard to shorter settling time, less undershoot/overshoot and smaller ITAE value of the frequency and tie-line power fluctuations. According to the sensitivity analysis, our proposal is found robust against system parameters variations and different loading conditions.Öğe Self-adaptive Equilibrium Optimizer for solving global, combinatorial, engineering, and Multi-Objective problems(Pergamon-Elsevier Science Ltd, 2022) Houssein, Essam H.; Çelik, Emre; Mahdy, Mohamed A.; Ghoniem, Rania M.This paper proposes a self-adaptive Equilibrium Optimizer (self-EO) to perform better global, combinatorial, engineering, and multi-objective optimization problems. The new self-EO algorithm integrates four effective exploring phases, which address the potential shortcomings of the original EO. We validate the performances of the proposed algorithm over a large spectrum of optimization problems, i.e., ten functions of the CEC'20 benchmark, three engineering optimization problems, two combinatorial optimization problems, and three multi-objective problems. We compare the self-EO results to those obtained with nine other metaheuristic algorithms (MAs), including the original EO. We employ different metrics to analyze the results thoroughly. The self-EO analyses suggest that the self-EO algorithm has a greater ability to locate the optimal region, a better trade-off between exploring and exploiting mechanisms, and a faster convergence rate to (near)-optimal solutions than other algorithms. Indeed, the self-EO algorithm reaches better results than the other algorithms for most of the tested functions.Öğe Squirrel search algorithm applied to effective estimation of solar PV model parameters: a real-world practice(Springer Science and Business Media Deutschland GmbH, 2023) Maden, Dinçer; Çelik, Emre; Houssein, Essam H.; Sharma, GulshanModel parameters estimation of solar photovoltaic (PV) cells/modules using real current–voltage (I–V) data is a critical task for the performance of PV systems. Therefore, there is a necessity to procure optimal parameters of PV models using proper optimization techniques. For this aim, squirrel search algorithm (SSA) as the recent and powerful tool is employed to accomplish the mentioned task in the single-diode model (SDM) and double-diode model (DDM) of a PV unit. Of course, better parameter values can be obtained by reducing the error between the experimental and model-based estimated data. Analyses are performed under two case studies. The former considers a standard dataset of R.T.C. France silicon solar cell, whereas the latter uses an experimental dataset of a polycrystalline CS6P-220P solar module. The I-V data of this PV module were acquired when it worked under 30 °C and solar radiance of 1000W/m2 at the Engineering Faculty Campus of Düzce University, Turkey. The results of the first case study are compared with those of other prevalent approaches, which demonstrate the superiority of SSA over its competing peers. Moreover, SSA is found to handle the model parameters definition of an industrial PV module located at the university campus. Thus, the new method offers a practical tool beneficial to boost the effectiveness of PV systems. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
