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![Schematic diagram of IEEE 30 bus grid [65].](publication/372720251/figure/fig4/AS:11431281375472566@1744638617555/Schematic-diagram-of-IEEE-30-bus-grid-65_Q320.jpg)
The present study introduces a subtraction-average-based optimization algorithm (SAOA), a unique enhanced evolutionary technique for solving engineering optimization problems. The typical SAOA works by subtracting the average of searcher agents from the position of population members in the search space. To increase searching capabilities, this stu...
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... Several novel bio-inspired algorithms have been proposed, each offering unique mechanisms for balancing exploration and exploitation. For instance, the Subtraction-Average-Based Optimizer (SABO) [42] introduces a novel subtraction-average mechanism to enhance population diversity and avoid premature convergence. Similarly, the Mantis Search Algorithm (MSA) [43] mimics the hunting behavior of mantises, incorporating a dynamic balance between exploration and exploitation through a unique prey-predator interaction model. ...
This study addresses the premature convergence issue of the Black-Winged Kite Algorithm (BKA) in high-dimensional optimization problems by proposing an enhanced hybrid algorithm (BKAPI). First of all, BKA provides dynamic global exploration through its hovering and dive attack strategies, while Particle Swarm Optimization (PSO) enhances local exploitation via its velocity-based search mechanism. Then, PSO enables efficient local refinement, and Differential Evolution (DE) introduces a differential mutation strategy to maintain population diversity and prevent premature convergence. Finally, the integration ensures a balanced exploration–exploitation trade-off, overcoming BKA’s sensitivity to parameter settings and insufficient local search capabilities. By combining these mechanisms, BKAPI achieves a robust balance, significantly improving convergence speed and computational accuracy. To validate its effectiveness, the performance of the enhanced hybrid algorithm is rigorously evaluated against seven other intelligent optimization algorithms using the CEC 2017 and CEC 2022 benchmark test functions. Experimental results demonstrate that the proposed integrated strategy surpasses other advanced algorithms, highlighting its superiority and strong application potential. Additionally, the algorithm’s practical utility is further confirmed through its successful application to three real-world engineering problems: welding beam design, the Himmelblau function, and visible light positioning, underscoring the effectiveness and versatility of the proposed approach.
... Here, L a denotes the total number of lyrebirds that are present in the search space, which ranges from 1 to p, and L j is the jth search agent. As is the case of every populationbased metaphor [45][46][47], each seeking lyrebird is initially randomly determined. The fitness of each lyrebird is recorded in a vector as Equation (7): ...
This study develops a novel Lyrebird Optimization Algorithm (LOA), a technique inspired by the wild behavioral strategies of lyrebirds in response to potential threats. In a two-area interconnected power system that includes non-reheat thermal stations, this algorithm is applied to handle load frequency control (LFC) by optimizing the parameters of a Proportional–Integral–Derivative controller with a filter (PIDn). This study incorporates generation rate constraints (GRCs). The efficiency of the provided LOA-PIDn is evaluated through simulations under various disturbance scenarios and is compared against other well-established optimization techniques, including the Ziegler–Nichols (ZN), genetic algorithm (GA), Bacteria Foraging Optimization Algorithm (BFOA), Firefly Approach (FA), hybridized FA and pattern search (hFA–PS), self-adaptive multi-population elitist Jaya (SAMPE-Jaya)-based PI/PID controllers, and Teaching–Learning-Based Optimizer (TLBO) IDD/PIDD controllers. The results demonstrate the LOA’s ability to minimize the integral of time multiplied by absolute error (ITAE) and achieve significantly lower settling times for the two-area frequencies and transferred power variances in comparison with other methods. The comprehensive comparison and the inclusion of real-world constraints validate the LOA as a robust and effective tool for addressing complex optimization challenges in modern power systems.
... These algorithms balance searching new areas of the solution space and refining existing solutions to find optimal or near-optimal solutions [66][67][68]. They have several successful applications in the engineering field [69][70][71][72][73][74]. Recently, R. Sowmya et al. presented a newly developed populationbased metaheuristic algorithm of Newton Raphson Based Optimizer (NRBO) [75] to address complex optimization problems in a variety of domains. ...
n modern wireless networks, the integration of Reconfigurable Intelligent Surface (RIS) and Fluid Antenna System (FAS) technologies offers a promising solution to the critical challenges such as signal attenuation, interference management, and security enhancement. This paper presents an application of a novel population-based metaheuristic algorithm of Newton Raphson Based Optimizer (NRBO) to efficiently integrating of FAS and RISs in wireless systems. The designed NRBO combines gradient-based search with adaptive population dynamics and leverages the Newton-Raphson Search Rule (NRSR) and Trap Avoidance Strategy (TAS) to balance exploration and exploitation, ensuring faster convergence and avoiding local optima. NRBO dynamically configures RIS elements and fluid-based antennas, adapting to environmental changes and specific communication requirements to enhance wireless performance. Multiple objective models are designed and addressed using the NRBO: maximizing the average attainable rate for each participant, minimizing the average number of FAS in all Users, and jointly optimizing both objectives with weighted considerations. Simulation results show that the NRBO successfully save 62.5% of the FAS ports to be utilized in the jointly optimized model of both objectives. Also, it achieves more saving with 72.9% when considering minimizing the average number of FAS in all Users as a single objective
... Future work may focus on incorporating renewable energy sources into the system model to address challenges such as variability, intermittency, and low inertia. Moreover, applicability of NNA can be validated for other optimization engineering problems [57][58][59]. Additionally, the study can be extended to include key nonlinearities like generation rate constraints, governor dead bands, and system delays, enabling a more realistic evaluation of the controller's robustness under complex operating conditions. ...
Modern power systems are increasingly challenged by frequency stability issues due to dynamic load variations and the growing complexity of interconnected networks. Traditional PID controllers, while widely utilized, struggle to address the rapid fluctuations and uncertainties inherent in contemporary multi‐area interconnected power systems (MAIPS). This paper introduces an innovative approach to Load Frequency Control (LFC) using a Fractional‐Order PID (FOPID) controller, optimized by a Neural Network Algorithm (NNA). The proposed NNA‐FOPID framework leverages the biological principles of neural networks to dynamically tune controller parameters, significantly enhancing system performance. The solution is tested under various scenarios involving step load changes across multi‐area systems. The proposed method demonstrates marked improvements over traditional PID controllers and advanced optimization techniques such as Differential Evolution (DE) and Artificial Rabbits Algorithm (ARA). The comparisons show that the FOPID controller's NNA‐based design effectively and successfully handles LFC in MAIPSs for ITAE minimizations, and statistical evaluation supports its superiority.
... The TCSC is one of the most widely used seriesconnected FACTS devices, aiming to enhance transmission capability and improve system stability. The typical structure of TCSC is depicted in Fig. 7 and is marked in a pale yellow color [42], [44], [45]. TCSC comprises three main components: a capacitor with the capacitive reactance C_tcsc connected in parallel with an inductor with the inductive reactance − tcsc , controlled by two Ttcsc_1 and Ttcsc_2 opposing thyristors connected in parallel. ...
... TCSC comprises three main components: a capacitor with the capacitive reactance C_tcsc connected in parallel with an inductor with the inductive reactance − tcsc , controlled by two Ttcsc_1 and Ttcsc_2 opposing thyristors connected in parallel. The firing angles of the thyristors are controlled to regulate the electrical impedance of the TCSC according to the system's control algorithm [44], [46], [47]. ...
Sub-synchronous resonance (SSR) may result from the recent integration of wind power generating systems (WPGS) based on double-fed induction generators (DFIG) into weak grids using long transmission lines with series capacitor adjustment. The amount of series compensation used in the transmission line determines how much SSR affects the grid, which may lead to serious instability. Flexible alternating current transmission system (FACTS) devices, which aid in controlling and stabilizing grid oscillations, are a workable way to lessen the impacts of SSR. In order to analyze the efficacy of FACTS controllers in mitigating SSR, this work examines the modeling and control techniques of WPGS-DFIG employing Thyristor controlled series capacitor (TCSC), static Var compensator (SVC), and static synchronous compensator (STATCOM). Time-domain simulations on a modified IEEE First benchmark, with varying series compensation levels and grid fault circumstances, are used to verify the study's correctness and effectiveness. According to the simulation findings, the STATCOM controller mitigates SSR far more effectively than TCSC and SVC. The STATCOM controller optimizes the performance of the WPGS-DFIG system by increasing dynamic responsiveness and grid stability in SSR-prone conditions.
... The search space encompasses the solution space for all optimization problems [25]. The dimension of the problem is defined by the number of choice variables, representing a subset of the search space [26]. A population of search agents makes up an algorithm. ...
Due to high probability of blade faults, bearing faults, sensor faults, and communication faults in pitch systems during the long-term operation of wind turbine components, and the complex operation environment which increases the uncertainty of fault types, this paper proposes a fault diagnosis method for wind turbine components based on an Improved Dung Beetle Optimization (IDBO) algorithm to optimize Support Vector Machine (SVM). Firstly, the Halton sequence is initially employed to populate the population, effectively mitigating the issue of local optima. Secondly, the subtraction averaging optimization strategy is introduced to accelerate the dung beetle algorithm in solving complex problems and improve its global optimization ability. Finally, incorporating smooth development variation helps improve data quality and the accuracy of the model. The experimental results indicate that the IDBO-optimized SVM (IDBO-SVM) achieves a 96.7% fault diagnosis rate for wind turbine components. With the proposed IDBO-SVM method, fault diagnosis of wind turbine components is more accurate and stable, and its practical application is excellent.
... Although grid search can find the global optimal solution, it will result in enormous computational resource consumption and neglect the correlations among parameters (Vincent and Jidesh, 2023). Instead, we use the recently proposed SABO for parameter optimization, which updates the positions of population members in the search space using subtraction averages of individuals, characterized by strong optimization capability and fast convergence rates (Moustafa et al., 2023). ...
In modern breeding practices, genomic prediction (GP) uses high-density single nucleotide polymorphisms (SNPs) markers to predict genomic estimated breeding values (GEBVs) for crucial phenotypes, thereby speeding up selection breeding process and shortening generation intervals. However, due to the characteristic of genotype data typically having far fewer sample numbers than SNPs markers, overfitting commonly arise during model training. To address this, the present study builds upon the Least Squares Twin Support Vector Regression (LSTSVR) model by incorporating a Lasso regularization term named ILSTSVR. Because of the complexity of parameter tuning for different datasets, subtraction average based optimizer (SABO) is further introduced to optimize ILSTSVR, and then obtain the GP model named SABO-ILSTSVR. Experiments conducted on four different crop datasets demonstrate that SABO-ILSTSVR outperforms or is equivalent in efficiency to widely-used genomic prediction methods. Source codes and data are available at: https://github.com/MLBreeding/SABO-ILSTSVR.
... The SABT methodology was based on mathematical concepts, including average values, shifts in seeking indicative sites, and the direction of the variance between two objectively quantifiable quantities. The method used by the SABT to calculate the arithmetic average is wholly original because it relies on a certain functional called the "v-subtraction operator" [36]. Therefore, each vector-based response in the SABT group was altered to comply with the following equation. ...
This work presents an optimal methodology based on an augmented, improved, subtraction-average-based technique (ASABT) which is developed to minimize the energy-dissipated losses that occur during electrical power supply. It includes a way of collaborative learning that utilizes the most effective response with the goal of improving the ability to search. Two different scenarios are investigated. First, the suggested ASABT is used considering the shunt capacitors only to minimize the power losses. Second, simultaneous placement and sizing of both PV units and capacitors are handled. Applications of the suggested ASAB methodology are performed on two distribution systems. First, a practical Egyptian distribution system is considered. The results of the simulation show that the suggested ASABT has a significant 56.4% decrease in power losses over the original scenario using the capacitors only. By incorporating PV units in addition to the capacitors, the energy losses are reduced from 26,227.31 to 10,554 kW/day with a high reduction of 59.75% and 4.26% compared to the initial case and the SABT alone, respectively. Also, the emissions produced from the substation are greatly reduced from 110,823.88 kgCO2 to 79,189 kgCO2, with a reduction of 28.54% compared to the initial case. Second, the standard IEEE 69-node system is added to the application. Comparable results indicate that ASABT significantly reduces power losses (5.61%) as compared to SABT and enhances the minimum voltage (2.38%) with a substantial reduction in energy losses (64.07%) compared to the initial case. For both investigated systems, the proposed ASABT outcomes are compared with the Coati optimization algorithm, the Osprey optimization algorithm (OOA), the dragonfly algorithm (DA), and SABT methods; the proposed ASABT shows superior outcomes, especially in the standard deviation of the obtained losses.
... To improve the transfer capacity that is accessible in power systems, the proposed GA was integrated with twofold mutation probabilities. A modified version of Subtraction-Average-Based Optimizer (SAO) for TCSC allocation, which lessens losses in electric power grids, is provided in [39]. This study modifies the usual SAO by incorporating a cooperative learning strategy powered by the leader solution. ...
This paper addresses the critical challenge of optimal allocation of Thyristor-Controlled Series Compensator (TCSC) devices in transmission power systems through an innovative optimization framework. Leveraging an Enhanced Gradient-Based Algorithm (EGBA) augmented with a crossover operator, the proposed methodology seeks to promote diversity in the solutions generated in each iteration, aiming to maximize the efficiency of power transmission networks. The algorithm incorporates key components such as the Gradient Search Process (GSP) and Local Escaping Process (LEP) to guide the exploration process and prevent premature convergence to suboptimal solutions. Additionally, the crossover operator, a novel addition in the EGBA, facilitates the exchange of TCSC configurations between solutions, contributing to solution diversity and potentially revealing novel optimal allocations. Initially, the EGBA and GBA performances are estimated using the CEC 2017 benchmarks. Moreover, to assess the practical applicability of the suggested EGBA, it is specifically tailored and implemented to enhance the operation of transmission power systems. The primary objective is to minimize technical power losses, considering varying numbers of TCSC devices with experimentation on two distinct IEEE power systems, one with 30 buses and another with 57 buses. The results are analyzed to validate the ability of the EGBA method in optimizing power systems and addressing technical losses. The novel proposed EGBA method significantly reduces power losses compared to the original GBA method in both tested power systems. In the first system, the EGBA achieved 0.85%, 2.99%, and 1.32% lower losses than the GBA when optimizing for one, two, and three TCSC devices, respectively. In addition, the objective of enhancing the security margin of the transmission lines is involved to optimize the power flow besides the minimization function of power losses. Similarly, in the second system, the EGBA outperformed the original GBA by 5.19%, 6.32%, and 5.12% for the same TCSC configurations. The simulation results demonstrate that the proposed EGBA is not only more effective but also more efficient than the original GBA and other recent approaches.
... The dwarf mongoose optimizer (DMO) is created by studying the foraging behavior of dwarf mongoose animals (DMAs). In the presented meta-heuristic technique (DMO), the population of DMAs is initially generated as follows [56,58]: ...
This article presents a modified intelligent metaheuristic form of the Dwarf Mongoose Optimizer (MDMO) for optimal modeling and parameter extraction of solar photovoltaic (SPV) systems. The foraging manner of the dwarf mongoose animals (DMAs) motivated the DMO’s primary design. It makes use of distinct DMA societal groups, including the alpha category, scouts, and babysitters. The alpha female initiates foraging and chooses the foraging path, bedding places, and distance travelled for the group. The newly presented MDMO has an extra alpha-directed knowledge-gaining strategy to increase searching expertise, and its modifying approach has been led to some extent by the amended alpha. For two diverse SPV modules, Kyocera KC200GT and R.T.C. France SPV modules, the proposed MDMO is used as opposed to the DMO to efficiently estimate SPV characteristics. By employing the MDMO technique, the simulation results improve the electrical characteristics of SPV systems. The minimization of the root mean square error value (RMSE) has been used to compare the efficiency of the proposed algorithm and other reported methods. Based on that, the proposed MDMO outperforms the standard DMO. In terms of average efficiency, the MDMO outperforms the standard DMO approach for the KC200GT module by 91.7%, 84.63%, and 75.7% for the single-, double-, and triple-diode versions, respectively. The employed MDMO technique for the R.T.C France SPV system has success rates of 100%, 96.67%, and 66.67%, while the DMO’s success rates are 6.67%, 10%, and 0% for the single-, double-, and triple-diode models, respectively.
