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This manuscript focuses on optimizing a Hybrid Renewable Energy System (HRES) that integrates photovoltaic (PV) panels, wind turbines (WT), and various energy storage systems (ESS), including batteries, supercapacitors (SCs), and hydrogen storage. The system uses a multi-objective optimization strategy to balance power management, aiming to minimize costs and reduce the likelihood of loss of power supply probability (LPSP). Seven different algorithms are assessed to identify the most efficient one for achieving these objectives, with the goal of selecting the algorithm that best balances cost efficiency and system performance. The system is assessed across three operational scenarios: (1) when energy supply meets demand with help from backup systems, (2) when demand exceeds supply and energy storage systems are depleted, and (3) when energy generation surpasses demand and storage systems are full. The HBA-based optimization effectively manages energy flow and storage, ensuring grid stability and minimizing overcharging risks. This system offers a reliable and sustainable power supply for isolated microgrids, effectively managing energy production, storage, and distribution. The research sets a new benchmark for future studies in decentralized energy systems, particularly in balancing technical efficiency and economic feasibility.

There is increasing growth in load demands and financial strain to upgrade the present power distribution system. It faces challenges such as power losses, voltage deviations, lack of reliability and voltage instability. There is also a sense of responsibility in the wake of environmental and energy crises to adopt distributed renewable resources for power generation. These challenges can be resolved by optimally allocating distributed generators (DGs) at different suitable locations in the radial power distribution system. Optimal allocation is a non-linear problem which is solved by powerful metaheuristic optimization algorithms. In this work, an objective function is introduced to optimally size four different types of DGs by utilizing honey badger algorithm (HBA), and comparison is drawn with grey wolf optimization (GWO) and whale optimization algorithm (WOA). The objective is to boost the voltage profile and minimize the power losses of the standard IEEE 33bus and 69-bus radial power distribution system. It is observed from the simulation results that honey badger algorithm is faster than grey wolf optimization and whale optimization algorithm in reaching accurate and optimum results in a mere one and two iterations for IEEE 33-bus and 69-bus systems, respectively. Additionally, power losses are reduced to 71% and 70% for IEEE 33-bus and 69-bus, respectively.

Electricity demand is sharply increasing with the growing population of human beings. Due to financial, social, and political barriers, there are lots of difficulties when building new thermal power plants and transmission lines. To solve this problem, renewable energy sources and flexible AC transmission systems (FACTS) can operate together in a power network. Renewable energy sources can provide additional power to the grid, whereas FACTS devices can increase the thermal limit of existing transmission lines. It is always desirable for an electrical network to operate under stable and secure conditions. The system runs at risk if any abnormality occurs in the generation, transmission, or distribution sections. This paper outlines a strategy for reducing system risks via the optimal operation of wind farms and FACTS devices. Here, a thyristor-controlled series compensator (TCSC) and a unified power flow controller (UPFC) have both been considered for differing the thermal limit of transmission lines. The impact of the wind farm, as well as the combined effect of the wind farm and FACTS devices on system economy, were investigated in this work. Both regulated and deregulated environments have been chosen to verify the proposed approach. Value at risk (VaR) and cumulative value at risk (CVaR) calculations were used to evaluate the system risk. The work was performed on modified IEEE 14 bus and modified IEEE 30-bus systems. A comparative study was carried out using different optimization techniques, i.e., Artificial Gorilla Troops Optimizer Algorithm (AGTO), Honey Badger Algorithm (HBA), and Sequential Quadratic Programming (SQP) to check the effect of renewable integration in the regulated and deregulated power systems in terms of system risk and operating cost.

This paper focuses on the elective surgical scheduling problem with multi-resource constraints, including material resources, such as operating rooms (ORs) and non-operating room (NOR) beds, and human resources (i.e., surgeons, anesthesiologists, and nurses). The objective of multi-resource constrained elective surgical scheduling (MESS) is to simultaneously minimize the average recovery completion time for all patients, the average overtime for medical staffs, and the total medical cost. This problem can be formulated as a mixed integer linear multi-objective optimization model, and the honey badger algorithm based on the Nash equilibrium (HBA-NE) is developed for the MESS. Experimental studies were carried out to test the performance of the proposed approach, and the performance of the proposed surgical scheduling scheme was validated. Finally, to narrow the gap between the optimal surgical scheduling solution and actual hospital operations, digital twin (DT) technology is adopted to build a physical-virtual hospital surgery simulation model. The experimental results show that by introducing a digital twin, the physical and virtual spaces of the smart hospital can be integrated to visually simulate and verify surgical processes.

This study introduces three soft computing (SC) optimization algorithms aimed at enhancing the efficiency of photovoltaic water pumping systems (PVWPS). These algorithms include the Gorilla Troop Algorithm (GTO), Honey Badger Algorithm (HBA), and Snake Algorithm (SAO). The goal of the SC optimizers is to maximize the output power of the PV array ( P PV ) and enhance the efficiency of the DC motor ( η ), thereby optimizing the water flow rate ( Q ) of the pumping system. The analytical modeling approach proposed in this study involves forecasting the optimal duty cycle ( D op ) for a buck-boost converter, taking into account variables such as solar radiation ( G ) and ambient temperature ( T ). A comparative analysis is conducted between the suggested SC optimizers and analytical modeling. MATLAB simulation is employed to explore an adaptive neuro-fuzzy inference system (ANFIS) trained for the proposed system. The objective is to assess system performance and accuracy. Findings indicate a strong convergence between the analytical model and the simulation model utilizing SC optimizers. Moreover, the neuro-fuzzy system trained offline, coupled with the proposed SC optimizers, demonstrates superior performance compared to traditional control methods like perturb and observe (P&O) and incremental conductance (IC). This superiority is evident across various metrics including motor efficiency ( η ), photovoltaic (PV) output power ( P PV ), water flow rate ( Q ), and time response.

In Internet of Things (IoT) networks, identifying the primary Medium Access Control (MAC) layer protocol which is suited for a service characteristic is necessary based on the requirements of the application. In this paper, we propose Energy Efficient and Group Priority MAC (EEGP-MAC) protocol using Hybrid Q-Learning Honey Badger Algorithm (QL-HBA) for IoT Networks. This algorithm employs reinforcement agents to select an environment based on predefined actions and tasks. It makes use of Q-learning method in Honey Badger Algorithm (HBA). In this algorithm, the PAN coordinator divides the network devices into multiple subgroups based on location, energy levels and the traffic type. In group priority assignment phase, a combined metric will be derived in terms of these parameters. Then a priority will be assigned to each group based on their combined metric. From each group, the optimal number of contention nodes will be selected using hybrid QL-HBA algorithm. The fitness function is derived in terms of the number of neighbours and total traffic loads of the nodes. Then transmission slots will be allotted to the group according to their group priority. The proposed EEGP-MAC protocol is implemented in NS3. Simulation results have shown that EEGP-MAC attains 11% lesser delay, 16% lesser energy consumption with 10% higher throughput, when compared to existing QL-DGMAC protocol, in various network sizes.

Global 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.

Combined renewable energy sources (RESs) are emerging as a competitive alternative to conventional energy production facilities due to their sustainability and zero-emission characteristics. However, determining the optimal system size is complicated by two major challenges: the cost of energy (COE) and the intermittent nature of RESs. This study introduces a novel mathematical approach to optimize the sizing of photovoltaic (PV), wind, hydrogen, battery, and fuel cell systems with electrolyzers, specifically tailored for the remote area of Lavan Island. The proposed method aims to deliver electricity without reliance on the traditional electricity distribution grid, while offering a scalable solution applicable to other geographical regions. The primary objective is to achieve cost-effective electricity generation while ensuring a reliable energy supply through the evaluation of system reliability indices. A fuzzy logic system is employed to minimize the costs of a hybrid system incorporating hydroelectric, wind, solar, and battery technologies, while simultaneously calculating two key reliability metrics: the Loss of Power Supply Probability (LPSP) and the Dump Energy Probability (DEP). To optimize the objective function, this study applies three advanced algorithms: the Shuffled Frog Leaping Algorithm (SFLA), the Grasshopper Optimization Algorithm (GOA), and the Honey Badger Algorithm (HBA). These algorithms are used to determine the global optimum, with comparative analyses conducted to highlight the performance of the proposed approach. The results are evaluated based on statistical metrics, including consistency, execution time, convergence speed, and the minimization of the objective function. The findings demonstrate the superiority and the reliability of the proposed method over alternative approaches, paving the way for cost-efficient and sustainable energy solutions in isolated regions.

This paper’s main objective is to create a digital fractional order differentiator (DFOD) that is precise, wideband, and stable. First, the Honey Badger algorithm (HBA) has been used to build the first order s to z transform by minimizing the L 1 -norm based error function. Performance comparison of designs based on real coded genetic algorithms (RCGA) and differential evolution (DE) and HBA-based first order transformations. Later, the indirect discretization of the new s to z transform using continuing fraction expansion (CFE) was used to develop the fourth and fifth orders for half and one-third fractional order differentiators. The RCGA and DE-based designs are contrasted with the relative magnitude error (RME) analysis of DFODs utilizing the HBA-based transform. The suggested approach performs better in terms of its magnitude response when compared to the current methods, demonstrating the superiority of the suggested HBA-based DFODs. The maximum absolute RME values of the fifth order for half and one-third of DFODs were obtained as - 46.27 d B and - 49.12 d B respectively.

Sleep Apnea (SA) is a breathing disorder that many people experience during sleep. Polysomnography is the best way to diagnose SA, but it requires significant time, cost, and effort. A practical and efficient method of diagnosing SA is using a wearable sensor to record Electrocardiography (ECG) signals. Machine learning algorithms can be used to classify SA by extracting features from ECG signals. Recently, deep learning techniques such as Convolutional Neural Network (CNN) have been used to identify features from ECG data automatically. However, the large number of hyperparameters in CNN makes it challenging to perform this task manually. Metaheuristic algorithms such as Honey Badger Algorithm (HBA) have been successfully applied to tune CNN hyperparameters, but it still has issues with premature convergence. To address these issues, the Bare-Bones Honey Badger Algorithm (BBHBA) is proposed as an improved version of HBA. It improves the exploitation potential of solutions, reduces diversity spillover, and maintains solution diversity. The method generates new candidate solutions using Gaussian search equations and an inverse hyperbolic cosine control mechanism. The greedy selection strategy is used to improve the searcher’s capabilities effectively. To validate the proposed deep learning model, the PhysioNet Apnea-ECG database is used. The model achieves an accuracy of 90.92%, a sensitivity of 91.24%, a specificity of 90.36%, and an F1 score of 92.76% on the validation dataset. Overall, the proposed method provides a practical and efficient way to diagnose SA using wearable sensors and deep learning techniques. The BBHBA algorithm improves the performance of CNN by effectively tuning hyperparameters, providing more accurate results in SA diagnosis.