Abstract Ensuring frequency stability in interconnected power systems is challenging due to continuous load variations and fluctuations in tie-line power. This study focuses on designing and optimizing controllers for Automatic Generation Control (AGC) of a two-area power system to achieve zero-frequency deviation under dynamic load conditions. Five different controllers—PID (Proportional-Integral-Derivative), PIDn, FOPID (Fractional Order PID), TID (Tilt-Integral-Derivative), and PIDA—were tested. Their parameters were optimized using seven advanced metaheuristic algorithms: Artificial Rabbit Optimization, Chernobyl Disaster Optimizer (CDO), Modified Chernobyl Disaster Optimizer (mCDO), Golden Jackal Optimization, Honey Badger Algorithm, Mont-Flame Optimization, and Spider Wasp Optimizer. A total of 35 simulation studies were conducted, and performance was evaluated using the Integral of Time-Weighted Absolute Error (ITAE) metric Among the tested controllers, the FOPID-mCDO combination achieved the lowest ITAE value (0.320684), a settling time of 3.6 s, and minimal overshoot (0.0083 Hz) and undershoot (− 0.1480 Hz). Compared to conventional PID controllers, this configuration reduced settling time by 10% and improved frequency stability under dynamic load variations. The proposed mCDO algorithm, which integrates neighborhood–global and wandering search strategies to enhance the exploration–exploitation balance of the original CDO, outperformed the standard CDO by enabling faster convergence and more precise parameter tuning. The findings indicate that the FOPID-mCDO combination is a promising approach for automatic generation control in multi-area power systems.

Abstract In areas where conventional energy sources are unavailable, alternative energy technologies play a crucial role in generating electricity. These technologies offer various benefits, such as reliable energy supply, environmental sustainability, and employment opportunities in rural regions. This study focuses on the development of a novel optimization algorithm called the modified firebug swarm algorithm (mFSO). Its objective is to determine the optimal size of an integrated renewable power system for supplying electricity to a specific remote site in Dehiba town, located in the eastern province of Tataouine, Tunisia. The proposed configuration for the standalone hybrid system involves PV/biomass/battery, and three objective functions are considered: minimizing the total energy cost (COE), reducing the loss of power supply probability (LPSP), and managing excess energy (EXC). The effectiveness of the modified algorithm is evaluated using various tests, including the Wilcoxon test, boxplot analysis, and the ten benchmark functions of the CEC2020 benchmark. Comparative analysis between the mFSO and widely used algorithms like the original Firebug Swarm Optimization (FSO), Slime Mold Algorithm (SMA), and Seagull Optimization Algorithm (SOA) demonstrates that the proposed mFSO technique is efficient and effective in solving the design problem, surpassing other optimization algorithms.

Abstract Very sensitive loads require the safe operation of electrical distribution networks, including hospitals, nuclear and radiation installations, industries used by divers, etc. To address this issue, the provided paper suggests an innovative method for evaluating the appropriate allocation of Distribution STATic COMpensator (DSTATCOM) to alleviate total power losses, relieve voltage deviation, and lessen capital annual price in power distribution grids (PDGs). An innovative approach, known as the modified capuchin search algorithm (mCapSA), has been introduced for the first time, which is capable of addressing several issues regarding optimal DSTATCOM allocation. Furthermore, the analytic hierarchy process method approach is suggested to generate the most suitable weighting factors for the objective function. In order to verify the feasibility of the proposed mCapSA methodology and the performance of DSTATCOM, it has been tested on two standard buses, the 33-bus PDG and the 118-bus PDG, with a load modeling case study based on real measurements and analysis of the middle Egyptian power distribution grid. The proposed mCapSA technique's accuracy is evaluated by comparing it to other 7 recent optimization algorithms including the original CapSA. Furthermore, the Wilcoxon sign rank test is used to assess the significance of the results. Based on the simulation results, it has been demonstrated that optimal DSTATCOM allocation contributes greatly to the reduction of power loss, augmentation of the voltage profile, and reduction of total annual costs. As a result of optimized DSTATCOM allocation in PDGs, distribution-level uncertainties can also be reduced.

Abstract The interest in incorporating environmentally friendly and renewable sources of energy, like photovoltaic (PV) technology, into electricity grids has grown significantly. These sources offer benefits, such as reduced power losses and improved voltage stability. To optimize these advantages, it is essential to determine optimal placement and management of these energy resources. This paper proposes an Improved RUNge–Kutta optimizer (IRUN) for allocating PV-based distributed generations (DGs) and Battery Energy Storage (BES) in distribution networks. IRUN utilizes three strategies to avoid local optima and enhance exploration and exploitation phases: a non-linear operator for smoother transitions, a Chaotic Local Search for thorough exploration, and diverse solution updates for refinement. The efficacy of IRUN is evaluated using 10 benchmark functions from the CEC’20 test suite, followed by statistical analysis. Next, IRUN is used to optimize the allocation of PVDG and BES to minimize energy losses in two standard IEEE distribution networks. The optimization problem is divided into two stages. In the first stage, the optimal size and the location of PV systems are calculated to meet peak load demand. In the second stage, considering time-varying load demand and intermittent PV generation, effective energy management of BES is employed. The effectiveness of IRUN is compared against the original RUN and other well-known optimization algorithms through simulation results. The comprehensive analysis demonstrates that IRUN outperforms the compared algorithms, making it a leading solution for optimizing PV distributed generation and BES allocation in distribution networks and the results show that the energy loss reduction reaches 63.54% and 68.19% when using PVand BES in IEEE 33-bus and IEEE 69 bus respectively.

Abstract Many real-world problems demand optimization, minimization of costs and maximization of profits, and meta-heuristic algorithms have proficiently proved their ability to achieve optimum results. This study proposes an alternative algorithm of Lévy Flight Distribution (LFD) by integrating Opposition-based learning (OBL) operator, termed LFD-OBL, for resolving intrinsic drawbacks of the canonical LFD. The proposed approach adopts OBL operator for catering search stagnancy to ensure faster convergence rate. We validate the usefulness of our approach through IEEE CEC’20 test suite, and compare results with original LFD and several other counterparts such as Moth-flame optimization, whale optimization algorithm, grasshopper optimisation algorithm, thermal exchange optimization, sine-cosine algorithm, artificial ecosystem-based optimization, Henry gas solubility optimization, and Harris’ hawks optimization. To further validate the efficiency of LFD-OBL, we apply it on parameters optimization of Solar Cell based on the Three-Diode Photovoltaic model. The qualitative and quantitative results of all the experiments performed in this study suggest superiority of the proposed method.

Abstract Hunger Games Search (HGS) is a newly developed swarm-based algorithm inspired by the cooperative behavior of animals and their hunting strategies to find prey. However, HGS has been observed to exhibit slow convergence and may struggle with unbalanced exploration and exploitation phases. To address these issues, this study proposes a modified version of HGS called mHGS, which incorporates five techniques: (1) modified production operator, (2) modified variation control, (3) modified local escaping operator, (4) modified transition factor, and (5) modified foraging behavior. To validate the effectiveness of the mHGS method, 18 different benchmark datasets for dimensionality reduction are utilized, covering a range of sizes (small, medium, and large). Additionally, two Parkinson’s disease phonation datasets are employed as real-world applications to demonstrate the superior capabilities of the proposed approach. Experimental and statistical results obtained through the mHGS method indicate its significant performance improvements in terms of Recall, selected attribute count, Precision, F-score, and accuracy when compared to the classical HGS and seven other well-established methods: Gradient-based optimizer (GBO), Grasshopper Optimization Algorithm (GOA), Gray Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Harris Hawks Optimizer (HHO), and Ant Lion Optimizer (ALO).

Abstract A population-based optimizer called beluga whale optimization (BWO) depicts behavioral patterns of water aerobics, foraging, and diving whales. BWO runs effectively, nevertheless it retains numerous of deficiencies that has to be strengthened. Premature convergence and a disparity between exploitation and exploration are some of these challenges. Furthermore, the absence of a transfer parameter in the typical BWO when moving from the exploration phase to the exploitation phase has a direct impact on the algorithm’s performance. This work proposes a novel modified BWO (mBWO) optimizer that incorporates an elite evolution strategy, a randomization control factor, and a transition factor between exploitation and exploitation. The elite strategy preserves the top candidates for the subsequent generation so it helps generate effective solutions with meaningful differences between them to prevent settling into local maxima. The elite random mutation improves the search strategy and offers a more crucial exploration ability that prevents stagnation in the local optimum. The mBWO incorporates a controlling factor to direct the algorithm away from the local optima region during the randomization phase of the BWO. Gaussian local mutation (GM) acts on the initial position vector to produce a new location. Because of this, the majority of altered operators are scattered close to the original position, which is comparable to carrying out a local search in a small region. The original method can now depart the local optimal zone because to this modification, which also increases the optimizer’s optimization precision control randomization traverses the search space using random placements, which can lead to stagnation in the local optimal zone. Transition factor (TF) phase are used to make the transitions of the agents from exploration to exploitation gradually concerning the amount of time required. The mBWO undergoes comparison to the original BWO and 10 additional optimizers using 29 CEC2017 functions. Eight engineering problems are addressed by mBWO, involving the design of welded beams, three-bar trusses, tension/compression springs, speed reducers, the best design of industrial refrigeration systems, pressure vessel design challenges, cantilever beam designs, and multi-product batch plants. In both constrained and unconstrained settings, the results of mBWO preformed superior to those of other methods.

The rapid growth of data generated by several applications like engineering, biotechnology, energy, and others has become a crucial challenge in the high dimensional data mining. The large amounts of data, especially those with high dimensions, may contain many irrelevant, redundant, or noisy features, which may negatively affect the accuracy and efficiency of the industrial data mining process. Recently, several meta-heuristic optimization algorithms have been utilized to evolve feature selection techniques for dealing with the vast dimensionality problem. Despite optimization algorithms' ability to find the near-optimal feature subset of the search space, they still face some global optimization challenges. This paper proposes an improved version of the sooty tern optimization (ST) algorithm, namely the ST-AL method, to improve the search performance for high-dimensional industrial optimization problems. ST-AL method is developed by boosting the performance of STOA by applying four strategies. The first strategy is the use of a control randomization parameters that ensure the balance between the exploration-exploitation stages during the search process; moreover, it avoids falling into local optimums. The second strategy entails the creation of a new exploration phase based on the Ant lion (AL) algorithm. The third strategy is improving the STOA exploitation phase by modifying the main equation of position updating. Finally, the greedy selection is used to ignore the poor generated population and keeps it from diverging from the existing promising regions. To evaluate the performance of the proposed ST-AL algorithm, it has been employed as a global optimization method to discover the optimal value of ten CEC2020 benchmark functions. Also, it has been applied as a feature selection approach on 16 benchmark datasets in the UCI repository and compared with seven well-known optimization feature selection methods. The experimental results reveal the superiority of the proposed algorithm in avoiding local minima and increasing the convergence rate. The experimental result are compared with state-of-the-art algorithms, i.e., ALO, STOA, PSO, GWO, HHO, MFO, and MPA and found that the mean accuracy achieved is in range 0.94-1.00.