Optimal Function Research Articles

Power Control of Hybrid Grid-Connected Renewable Energy System using Machine Learning

This article addresses the crucial challenge of maintaining a reliable power supply in integrated electric systems that combine solar power and energy storage. It focuses on optimizing key parameters for remote photovoltaic, wind, and battery energy storage within the grid, with a primary goal of sustaining 24-hour load demand. The study introduces a comprehensive approach that integrates fuel price dynamics and battery depletion costs into the optimization model for hybrid grids. This approach seeks a delicate balance between sustainable energy usage and cost-effectiveness. To achieve this balance, the research employs genetic algorithms, a robust computational technique. These algorithms play a vital role in calculating discharge-charge cycles and assessing battery health, addressing concerns about battery longevity and performance. Additionally, the study explores a machine learning framework, specifically nested learning, to further enhance the optimization process. Nested learning allows for the development of a sophisticated target function that considers various grid operation parameters and constraints. This machine learning approach enhances the system's ability to dynamically monitor power consumption and generation. Within this machine learning framework, the TD Lambda learning algorithm takes a prominent role in identifying the optimal function. This algorithm is well-known for its efficiency, particularly in non-Markovian scenarios. Its quick convergence enhances the overall performance of the hybrid grid, ensuring efficiency in changing conditions. The research also evaluates the impact of weight factors on various aspects, including battery charging status, algorithmic decisions, and their consequences on optimal energy pricing within the hybrid grid. This comprehensive assessment deepens the understanding of how these weight factors influence grid performance and economic viability.

Survey of Techniques and Applications in Object Recognition

Abstract: Object detection, which is closely related to video analysis and image comprehension, has received attention in years from researchers. Traditional approaches to object detection rely on crafted features and shallow trainable architectures. However, these methods face challenges. Often reach a plateau, in performance improvement. To overcome these limitations complex ensembles are often created by combining low-level image features with high-level context from object detectors and scene classifiers. The emergence of learning has introduced powerful tools that can learn semantics high level and deeper features. These tools address the limitations of architectures by introducing network structures training strategies and optimization functions. This paper provides a review of learning-based frameworks for object detection. The review starts with an overview of the history of learning with a particular emphasis on the Convolutional Neural Network (CNN) as an exemplary tool. It then delves into generic object detection architectures exploring modifications and effective strategies for improving detection performance. Recognizing the characteristics of detection tasks, the paper also briefly surveys several tasks such, as salient object detection, face detection, crowd analysis, and pedestrian detection.

Molecular investigation of vitamin D receptor (VDR) genetic variants and their impact on VDR mRNA and serum vitamin D levels in allergic rhinitis in an Indian population: A case-control study.

Vitamin D deficiency is widespread and poses a significant health concern, as emerging research links it to allergic diseases owing to its immunomodulatory functions. The optimal functioning of vitamin D and its activation depend on its nuclear receptor, vitamin D receptor (VDR). Genetic variants of VDR have been explored as potential factors in autoimmune and allergic diseases, with limited studies on their association with allergic rhinitis (AR). The present investigation aims to analyse the role of three VDR genetic variants - TaqI, FokI and BsmI - in AR susceptibility and their impact on VDR mRNA and serum vitamin D levels. A total of 550 subjects, consisting of 250 AR cases and 300 age- and gender-matched controls, underwent genotyping by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). VDR mRNA and vitamin D levels were determined by quantitative real-time PCR and chemiluminescence, respectively. Although TaqI did not exhibit significant differences, FokI demonstrated a noteworthy association with AR, particularly with the CC genotype (odds ratio [OR]: 3.34; confidence interval [CI]: 1.79-6.23). Similarly, BsmI revealed an increased risk for AR, with the GA+AA genotypes showing a 2.2-fold elevated risk (OR: 2.20; CI: 1.53-3.16). VDR mRNA expression was threefold lower in AR patients (p<.0001), accompanied by reduced serum vitamin D levels (p<.0001). In addition, CC (p=.01) and AA (p=.02) genotypes of FokI and BsmI were associated with reduced VDR mRNA levels, whereas TaqI showed no such association. Similarly, heterozygous genotypes of TaqI and FokI, as well as homozygous AA of BsmI, correlated with lower serum vitamin D levels (p<.001). This study emphasizes the intricate relationship among VDR genetic variations, altered VDR activity, immune modulation and vitamin D metabolism in AR. Further research involving diverse populations is crucial for confirming and generalizing these findings, paving the way for personalized therapeutic interventions in vitamin D-related disorders.

Trajectory Tracking Control of Unmanned Underwater Vehicle Based on Projected Perpendicular Guidance Method with Disturbance Observer

Unmanned underwater vehicles (UUVs) possess impressive maneuverability and versatility, but controlling them during trajectory tracking can be challenging due to their susceptibility to external disturbances and perturbations in their model parameters. Additionally, the UUV has four degrees of freedom underwater, but only three control inputs, making it a typical underactuated system. To address these issues, this paper introduces a novel optimize sliding mode control (OPSMC) algorithm grounded in projected perpendicular guidance (PPG). The PPG algorithm transforms the three-degree-of-freedom path trajectory control into two-degree-of-freedom heading tracking control and surge velocity tracking control by designing virtual posture angles. Optimized sliding mode control, based on sliding mode control, improves control precision and reduces control input chattering by constructing optimization functions for control inputs. During trajectory tracking, UUVs are susceptible to external environmental disturbances and perturbations in system model parameters. Disturbance observers are employed to estimate these disturbances and perturbations. Finally, MATLAB/Simulink is used for numerical simulation experiments. The simulation results demonstrate that the PPG algorithm effectively enables underactuated UUVs to achieve trajectory tracking control. The designed optimized sliding mode controller and disturbance observer enhance the control precision and robustness of the system.

Recent advances in carbon-based sulfur host materials for lithium-sulfur batteries

Lithium-sulfur batteries (LSBs) have been brought into focus as the development direction of the next-generation power battery system due to their high energy density, eco-friendliness, and low cost, which has a broad application prospect in the field of energy storage. However, some problems are still unresolved in the sulfur cathode, e.g., poor electric conductivity, serious volume expansion of sulfur, shuttle effect caused by easy dissolution of lithium polysulfides in the electrolyte, and slow redox reaction kinetics of sulfur species. These issues lead to poor cycle stability and rate performance, making it hard to meet the requirement for LSBs in practical applications. Since the inherent nature of sulfur is the root cause of the above problems, reasonable design of functional sulfur hosts will be an effective way to break through the current bottlenecks of LSBs. The review covers the latest research progress on carbon-based sulfur host materials of LSBs, including structural design and functional optimization strategies, aiming to prepare multifunctional sulfur host materials by integrating physical confinement, chemical adsorption, and catalytic effect towards lithium polysulfides. The obstacles and future prospects of carbon-based sulfur hosts have also been brought forward, which provides in-depth guidance for practical application of LSBs.

Deep learning model for enhanced power loss prediction in the frequency domain for magnetic materials

AbstractThis paper outlines the methodology for predicting power loss in magnetic materials. It starts by introducing the concept of core loss and the complexity of modelling it. Steinmetz's equation is presented to calculate power loss based on frequency and magnetic flux density, but its limitations are highlighted. As an alternative, a neural network‐based method is introduced. The proposed methodology adopts a long short‐term memory network, expressing the core loss as a function of magnetic flux density, frequency, temperature, and wave classification. Fast Fourier transform was implemented to reduce the data points of the sampled flux density waveform while preserving its characteristics. Analyzing in the frequency domain enabled streamlining the training of the model. The input features were arranged as required, and the network architecture was designed with appropriate layers and optimal activation functions. Through extensive training using the datasets, the model assimilated intricate relationships between input variables and known power loss. Evaluation and validation metrics were subsequently employed to gauge the performance of the trained network. This innovative methodology aims to significantly augment the precision of power loss predictions, providing valuable insights into the nuanced behaviour of magnetic materials.

Zero‐sum game‐based H∞ optimal finite‐time prescribed performance control for nonlinear multiagent systems with actuator faults

AbstractThis article proposes an optimal leader‐follower consensus control protocol with finite‐time prescribed performance for nonlinear multiagent systems suffering from the actuator fault based on the zero‐sum game framework. First, to guarantee the specified performance of the system within a finite time frame, a novel error transformation function (ETF) is constructed. The ETF proposed in this paper can adjust the mapping range of error transformation to achieve better error convergence performance, thereby enhancing the flexibility of the controller design. Then, to compensate for the negative impact of actuator fault, an error‐related item is separated from the optimal performance index function to design an online fault estimator, which can obtain a better error estimation accuracy. Furthermore, to lessen the need for difficult‐to‐verify persistent excitation signal in adaptive dynamic programming algorithm, both historical and real‐time data are applied to adjust the updating law of neural network weight. Moreover, it demonstrates that the signals within the closed‐loop system are bounded and the expected finite‐time prescribed performance can be ensured in the presence of faults. Finally, a numerical example of multiple single‐link robots manipulator system is executed to validate the efficacy of the developed scheme.

Rehabilitation potential in patients with prostate cancer: psychological aspect

Objective. To determine a list of psychological characteristics associated with the prognosis of prostate cancer outcome and rehabilitation potential in patients with cancer. Materials and methods. The study of psychological characteristics (subjective and personal characteristics of study participants (n = 148)) associated with rehabilitation potential was carried out using the following psychological methods: Methodology of Subjective Control Level (E.F. Bazhin, E.A. Golynkina, L.M. Etkind), Questionnaire of ways to cope (adapted by T.L. Kryukova, E.V. Kuftyak, M.S. Zamyshlyaeva), Questionnaire SF-36 “Assessment of the quality of life”, Test of vitality (adapted by D.A. Leontiev, E.I. Rasskazova), Basic Beliefs Scale (adapted by M.A. Padun, A.V. Kotelnikova). Results. A favorable prognosis for the outcome of prostate cancer is associated with the patient`s involvement in the events of his life and active participation in them, understanding of the significance of the problem, partial independence from others, slight limitation of role functioning due to the emotional state, optimal social functioning in combination with beliefs about the benevolence of the world around him and ability to control events. Conclusions. A list of psychological characteristics associated with the course of the disease in patients with prostate cancer was obtained. These psychological characteristics can define the rehabilitation potential, being associated with the possibilities of recovery after antitumor treatment.

Fractional view analytical analysis of generalized regularized long wave equation

Abstract In this research study, we focus on the generalized regularized long wave equation and the modified regularized long wave equation, which play pivotal roles in characterizing plasma waves in oceans and ion acoustic waves in shallow water, a domain deeply rooted in physical phenomena. Employing two computational techniques, namely, the optimal auxiliary function method and the Laplace iterative transform method, we approximate these equations. These formulas are used to characterize plasma waves in oceans and ion acoustic waves in shallow water. The results discovered have important ramifications for our comprehension of many physical events. Our results show that both methods are robust, easy to use, and successful. Both methods yield results that are satisfactory to each other. With the use of tables and graphs, we compared the two suggested approaches. The findings suggest that the suggested methods can be widely applied to explore other real-world problems.

#1645 The role of donor Cystatin-C in posttransplant outcomes in kidney transplantation

Abstract Background and Aims Cystatin-C is an established marker of glomerular filtration rate in kidney disease. High levels of cystatin-C have been associated with unfavorable outcomes (acute kidney injury, cardiovascular morbidity) in critically ill patients. The aim of this study was to evaluate the levels of Cystatin-C in donors after brain (DBD) and circulatory death (DCD) and investigate its association with posttransplant graft function. Method Plasma samples from 303 donors (283 deceased, 20 living donors; LD) were obtained from the Quality in Organ Donation (QUOD) and the Oxford Transplant (OTB) biobanks. QUOD is a national multi-center UK wide bioresource of deceased donor clinical samples collected during donor management and organ procurement. OTB is a living donor biobank established to obtain clinical samples as control cohorts. Only deceased donors with both grafts transplanted were included in the study. Plasma Cystatin-C from LD samples as assessed as an optimal control group. The samples were linked to donor and recipient metadata obtained from the National Transplant Registry (NHSBT). Using Luminex assays, plasma Cystatin-C was quantified in samples collected during donor management (prior to cross clamp in DBDs and LDs and at withdrawal of support in DCDs). We performed descriptive statistics and multivariate linear and logistic analysis to determine the association between Cystatin-C, primary non-function (PNF), delayed graft function (DGF), acute rejection (AR) and 12-month posttransplant graft function. Results Among 303 donors (median age: 50 years), 126 were DCDs, 157 DBDs (20.1% of them were expanded criteria donors, ECD) and 20 LDs. ECDs and DCDs had significantly higher levels of Cystatin-C, compared to DBDs. In a total of 606 recipients, 11 (1.9%) developed PNF, and 128 (21%) developed DGF (24 as pairs, 80 non-pairs). Forty-six recipients (7.6%) experienced at least one AR episode and 60 (10%) donors had lost the graft during follow-up. Donor levels of Cystatin-C were associated with DGF and were further elevated in donors of kidneys which developed PNF (P-value &amp;lt; .0001, Fig. 1). Grafts with acute rejections were retrieved from donors with higher levels of Cystatin-C, but the difference did not meet statistical significance. Cystatin-C levels were significantly lower (P-value &amp;lt; .0001) in donors who offered concordant grafts with optimal 12-month post-transplant function (eGFR &amp;gt;60 ml/min), compared with grafts with intermediate function (GFR 30-60 ml/min) or eGFR &amp;lt;30ml/min (Fig. 2). LDs had significantly lower levels of Cystatin-C compared to deceased donors in all studied outcomes (Figs 1 and 2). Cystatin-C during donor management was an independent risk factor for inferior 12-month paired graft function, after the implementation of three different multivariate models, including donor (β-coef = −11.3, P-value = .001), recipient (β-coef = −16, P-value &amp;lt; .001) and post-transplant characteristics (β-coef = −13.1, P-value = .002). Conclusion In a large cohort of deceased and living donors, we were able to evaluate the circulating levels of Cystatin-C during donor management with the use of high-end performance technology. The concentration of Cystatin-C was notably different among donor types and was significantly higher in donors with kidneys that developed PNF or had suboptimal graft function 12-month posttransplant. Our findings suggest that Cystatin-C may have a role in identifying higher-risk donors during donor management and offers the opportunity for granular assessment of donor kidney quality prior to transplantation.

Enhancement of the Consistency and Connectivity of the Ant-tracking Algorithm via 3D U-Net with Dual-Threshold Iteration

Abstract The ant-tracking algorithm is commonly used to extract faults in geological structures. However, obtaining 3D ant-tracking data requires the calculation of various volume attributes. To obtain satisfactory data, it is necessary to iterate through the parameters of these attributes to achieve reasonable continuity. Moreover, due to the numerous parameters involved, the algorithm can produce different outputs with each execution. In this study, we aimed to enhance the performance of the ant-tracking algorithm by combining it with a basic U-Net structure. The input and corresponding labels to the model are "cubic shaped" 3D data segmented from the original 3D seismic volume to facilitate cross-validation with distinct regions. We used the label data as the single ant-tracking result to minimize the operator’s bias by executing the ant-tracking with several different parameters and executors and then taking the average. An evaluative comparison of three different loss functions (MAE, RMSE, and MSE) was conducted to identify the optimal function for training the model. Across five out of the six metrics, MSE function demonstrated predominant performance, leading to its adoption. Apart from this, a significant number of misinterpreted faults led us to propose the post-processing algorithm named "Dual-Threshold Iteration." It was initially used to extract fine blood vessels branching out from large vessels in medical image segmentation and adapted in our work to ensure a high level of continuity while ignoring worthless noise. Comparison with the F1 score and the number of 3D-connected components confirmed that the proposed method could generate reduced bias and smoothly connected fault structures.

Mathematical Model-Based Optimization of Continuous Flow Photobioreactor Operating at Steady State Using MATLAB Optimization Function

AbstractMicroalgae have received great attention recently due to its potential for sustainable CO2 emission reduction and production of biofuel. These applications are highly dependent on microalgae biomass productivity of photobioreactor (PBR). The objective of this work is to optimize continuous flow PBR at steady state using a mathematical model describes growth of the microalgae species Chlorella vulgaris. The operating conditions that affect the performance of PBR are determined as pH, CO2 percentage in the gas feed, light intensity, and dilution rate. The MATLAB optimization function (fmincon) is used to find the best design and operating variables in the tested region. The maximum biomass productivity is achieved at the upper-bound of the range for pH, CO2 percentage in the gas feed, and light intensity, while for dilution rate, there exists an optimum value that guarantees maximum productivity. The maximum biomass productivity and specific growth rate were estimated as 4.03 and 0.196 billion cells/L h, respectively. This is achieved at optimum dilution rates of 0.049 and 1.0 h−1, respectively. The fmincon optimization results agree well with the literature that used different optimization methods. The model-based optimization predicts the best performance of PBR without conducting experiments. Sensitivity analysis of model constants on biomass concentration showed that mass transfer coefficient KLa has the highest sensitivity followed by µmax, KCL, and KE which has the lowest sensitivity for biomass production. The obtained results are of technological significance for processes such as CO2-fixation and biofuel production. Research effort is needed to exploit optimization results in large-scale cultivation.

Cutting Edge: LAG3 Dimerization Is Required for TCR/CD3 Interaction and Inhibition of Antitumor Immunity.

Lymphocyte activation gene 3 (LAG3) is an inhibitory receptor that plays a critical role in controlling T cell tolerance and autoimmunity and is a major immunotherapeutic target. LAG3 is expressed on the cell surface as a homodimer but the functional relevance of this is unknown. In this study, we show that the association between the TCR/CD3 complex and a murine LAG3 mutant that cannot dimerize is perturbed in CD8+ T cells. We also show that LAG3 dimerization is required for optimal inhibitory function in a B16-gp100 tumor model. Finally, we demonstrate that a therapeutic LAG3 Ab, C9B7W, which does not block LAG3 interaction with its cognate ligand MHC class II, disrupts LAG3 dimerization and its association with the TCR/CD3 complex. These studies highlight the functional importance of LAG3 dimerization and offer additional approaches to therapeutically target LAG3.

Perioperative Nutritional, Functional, and Bone Health Optimization in Spine Surgery: A National Investigation of Spine Surgeons' Perceptions and Practices.

While perioperative nutritional, functional, and bone health status optimization in spine surgery is supported with ample evidence, the implementation and surgeon perception regarding such efforts in clinical practice remain largely unexplored. This study sought to assess the current perception of spine surgeons and implementation regarding the nutritional, functional status, and bone health perioperative optimization. An anonymous 30-question survey was distributed to orthopaedic spine fellowship and neurosurgery program directors identified through the North American Spine Society and American Association of Neurological Surgeons contact databases. The questionnaire was completed by 51 surgeon survey respondents. Among those, 62% reported no current formal nutritional optimization protocols with 14% not recommending an optimization plan, despite only 10% doubting benefits of nutritional optimization. While 5% of respondents perceived functional status optimization as nonbeneficial, 68% of respondents reported no protocol in place and 46% noted a functional status assessment relying on patient dependency. Among the respondents, 85% routinely ordered DEXA scan if there was suspicion of osteoporosis and 85% usually rescheduled surgery if bone health optimization goals were not achieved while 6% reported being suspicious of benefit from such interventions. While most responding spine surgeons believe in the benefit of perioperative nutritional and functional optimization, logistical and patient compliance challenges were noted as critical barriers toward optimization. Understanding surgeon perception and current practices may guide future efforts toward advancement of optimization protocols.

Subpocket-Based Analysis Approach for the Protein Pocket Dynamics.

Structural and dynamic characteristics of protein pockets remarkably influence their biological functions and are also important for enzyme engineering and new drug research and development. To date, several softwares have been developed to analyze the dynamic properties of protein pockets. However, due to the complexity and diversity of the pocket information during the kinetic relaxation, further improvement and capacity expansion of current tools are required. Here, we developed a platform software AlphaTraj in which a computational strategy that divides the whole protein pocket into subpockets and examines various properties of the subpockets such as survival time, stability, and correlation was proposed and implemented. We also proposed a scoring function for the subpockets as well as the whole pocket to visualize the quality of the pocket. Furthermore, we implemented automated conformational search functions for ligand docking and ligand optimization. These functions may help us to gain a deep understanding of the dynamic properties of protein pockets and accelerate the protein engineering and the design of inhibitors and small-molecule drugs. The software is freely available at https://github.com/dooo12332/AlphaTraj.git under the GNU GPL license.

Research on two‐level energy management based on tiered demand response and energy storage systems

AbstractIn response to the escalating demands of the electricity market for load dispatch optimization and the stable operation of power systems, the design of effective incentive mechanisms to guide user electricity consumption behaviour has become an urgent task. This study addresses the complexity of the power load dispatch system by analysing the characteristics and interrelations of large‐scale user load demand responses. A dual‐layer energy management model was constructed, and a demand response incentive mechanism was designed. Adaptive incentive strategies were formulated according to different electric power user demand response scenarios. Furthermore, an optimal incentive decision‐making technology oriented towards user comfort was proposed, achieving an integrated function of strategy formulation, implementation, analysis, and optimization for power demand response. Through typical applications in core business scenarios such as elasticity of power user demand response, tiered incentive mechanisms, and comprehensive user utility, the model and strategies have been confirmed to optimize the economic benefits of virtual power plants and demand‐side electricity users under the premise of ensuring user comfort. This provides a novel solution for the efficient operation of the power market.

Type-3 fuzzy dynamic adaptation of Bee colony optimization applied to mathematical functions

In this article, an Interval Type-3 Fuzzy Logic System (IT3FLS) for enhancing the performance in Bee Colony Optimization (BCO) is outlined. The efficiency of the IT3FLS approach is verified with results on a set of benchmark mathematical functions. The IT3FLS provides an approach that helps to identify the optimal values in α and β parameters that allows to improve the performance in the original BCO. The IT3FLS approach exhibits advantages in the optimization of the benchmark functions. It can be noted that a IT3FLS exhibits better results in the minimal values of the set of mathematical functions. The experimentation demostrates that the implementation of the IT3FLS approach enhances the performance of BCO when compared with respect to the variants utilizing Generalized Type-2 FLS (GT2FLS), Interval Type-2 FLS (IT2FLS) and Type-1 FLS (T1FLS).

Utilizing the Optimal Auxiliary Function Method for the Approximation of a Nonlinear Long Wave System considering Caputo Fractional Order

In this article, we explore the utilization of the Caputo derivative and the Riemann–Liouville (R–L) fractional integral to analyze the optimal auxiliary function method for approximating fractional nonlinear long waves. Approximate long wave equation with a distinct dispersion relation offers the most accurate description of shallow water wave properties. Various methods, including the Adomian decomposition technique, the variational iteration method, the optimum homotopy asymptotic method, and the new iterative technique, have been employed and compared to those obtained using the fractional-order approximate long wave equation. The results of our study indicate that the optimal auxiliary function method is highly successful and practically simple, achieving better and more rapid convergence after just one repetition. This method is recognized as an efficient approach, demonstrating high precision in solving intriguing and intricate problems. Furthermore, it proves to be more time and resource efficient than other relevant analytical techniques, leading to significant savings in both volume and time. Compared to the Adomian decomposition technique, the new iterative technique, the variational iteration method, and the optimum homotopy asymptotic method, the suggested technique is extremely accurate computationally. It is also easy to analyze and solve fractionally linked nonlinear complex phenomena that arise in science and technology. We present the numerical and graphical findings that support these conclusions.

Convergence property of Nesterov‐accelerated adaptive moment estimation with safety helmet detection and classification in smart industry application

We propose a technique for first‐order gradient‐based optimization of stochastic objective functions called Nesterov‐accelerated adaptive moment assessment, which makes use of dynamic evaluations of lower‐order moments. The adaptive moment assessment and the Nesterov acceleration gradient are combined. Consequently, it has perks, and this technique is convenient to use, numerically economical, memory‐light, and very well‐suited for challenges with massive amounts of information and characteristics. Additionally, we investigate the algorithm's convergence characteristics and propose a conservative constraint on the convergence rate. Finally, we employ this technique for the detection and classification of safety helmets.

Fuzzy analytic hierarchy process based generation management for interconnected power system

Decision makers consistently face the challenge of simultaneously assessing numerous attributes, determining their respective importance, and selecting an appropriate method for calculating their weights. This article addresses the problem of automatic generation control (AGC) in a two area power system (2-APS) by proposing fuzzy analytic hierarchy process (FAHP), an multi-attribute decision-making (MADM) technique, to determine weights for sub-objective functions. The integral-time-absolute-errors (ITAE) of tie-line power fluctuation, frequency deviations and area control errors, are defined as the sub-objectives. Each of these is given a weight by the FAHP method, which then combines them into an single final objective function. This objective function is then used to design a PID controller. To improve the optimization of the objective function, the Jaya optimization algorithm (JOA) is used in conjunction with other optimization techniques such as sine cosine algorithm (SCA), Luus–Jaakola algorithm (LJA), Nelder–Mead simplex algorithm (NMSA), symbiotic organism search algorithm (SOSA) and elephant herding optimization algorithm (EHOA). Six distinct experimental cases are conducted to evaluate the controller’s performance under various load conditions, with data plotted to show responses corresponding to fluctuations in frequency and tie-line exchange. Furthermore, statistical analysis is performed to gain a better understanding of the effectiveness of the JOA-based PID controller. For non-parametric evaluation, Friedman rank test is also used to validate the performance of the proposed JOA-based controller.