High Convergence Research Articles

Design and verification of a hybrid electrostatic discharge model for Gate-Controlled silicon controlled rectifier

The DC breakdown characteristic of electrostatic discharge (ESD) protection device, which is an important indicator of its transparency and circuit compatibility, has received widespread attention. Currently, methods such as Verilog-A hardware description language, circuit-level SPICE simulation, and process-level device modeling cannot simultaneously achieve high accuracy, good convergence, and fast simulation speed. Therefore, in this paper, a hybrid ESD model based on Technology Computer Aided Design (TCAD) device-level simulation, Monte Carlo numerical modeling and Verilog-A behavioral modeling is proposed to characterize the ESD characteristics of gate-controlled silicon-controlled rectifier (GCSCR). A device-level model of GCSCR was established using TCAD, and the working mechanism was simulated. By extracting the electric field from TCAD and using the Monte Carlo modeling method, the DC characteristic of GCSCR was accurately characterized. In addition, the snapback characteristic of the device was successfully simulated by Verilog-A. The effectiveness of the above approaches was further verified based on the 0.18 μm standard BCD (Bipolar-CMOS-DMOS) process. Comparison results indicate that the hybrid model exhibits high simulation accuracy, good convergence and good universality and scalability, providing a simple and effective modeling solution for ESD devices of different structures and sizes.1This work is supported by the National Natural Science Foundation of China (Grant No. 62174052, 61827812), Hunan Science and Technology Department Huxiang High-level Talent Gathering Project (Grant No. 2019RS1037) and Innovation Project of Science and Technology Department of Hunan Province (Grant No. 2020GK2018).

Enhanced Grey Wolf Optimization Algorithm for Mobile Robot Path Planning

In this study, an enhanced hybrid Grey Wolf Optimization algorithm (HI-GWO) is proposed to address the challenges encountered in traditional swarm intelligence algorithms for mobile robot path planning. These challenges include low convergence accuracy, slow iteration speed, and vulnerability to local optima. The HI-GWO algorithm introduces several key improvements to overcome these limitations and enhance performance. To enhance the population diversity and improve the initialization process, Gauss chaotic mapping is applied to generate the initial population. A novel nonlinear convergence factor is designed to strike a balance between global exploration and local exploitation capabilities. This factor enables the algorithm to effectively explore the solution space while exploiting the promising regions to refine the search. Furthermore, an adaptive position update strategy is developed by combining Levy flight and golden sine. This strategy enhances the algorithm’s solution accuracy, global search capability, and search speed. Levy flight allows longer jumps to explore distant regions, while golden sine guides the search towards the most promising areas. Extensive simulations on 16 standard benchmark functions demonstrate the effectiveness of the proposed HI-GWO algorithm. The results indicate that the HI-GWO algorithm outperforms other state-of-the-art intelligent algorithms in terms of optimization performance. Moreover, the performance of the HI-GWO algorithm is evaluated in a real-world path planning experiment, where a comparison with the traditional grey wolf algorithm and ant colony algorithm validates the superior efficiency of the improved algorithm. It exhibits excellent optimization ability, robust global search capability, high convergence accuracy, and enhanced robustness in diverse and complex scenarios. The proposed HI-GWO algorithm contributes to advancing the field of mobile robot path planning by providing a more effective and efficient optimization approach. Its improvements in convergence accuracy, iteration speed, and robustness make it a promising choice for various practical applications.

Chronic disease prediction with deep convolution based modified extreme-random forest classifier

A disease is said to be chronic when a disease occurs in an individual and their health conditions due to the disease last one year/more. Predicting chronic diseases have become crucial to save individual’s life and enhance their well-being. Though traditional clinical procedures are considered to perform this, it seems to be a time consuming process. Concurrently, with the progress of data mining algorithms, researchers have attempted to use different algorithms for such prediction. Nevertheless, they have been ineffective in feature extraction that negatively affected the prediction rate. To combat issues with regard to low accuracy rate, the present research intends to perform prediction of four common chronic diseases (breast cancer, heart disease, diabetes and kidney disease) affecting people worldwide. To accomplish this, the research proposes dual Deep CNN (Deep Convolutional Neural Network) for feature extraction. In this case, optimal, maximum and minimum hidden layers are used for extracting relevant features. Further, ME-RF (Modified Extreme-Random Forest) is used for classification. In this process, the research considers XGBoost algorithm comprising of certain innate advantages like high convergence and modest computations. However, when the predictability of this model is poor, it works in an ideal manner with numerous leaves in DT (Decision Tree). Simultaneously, RF comprise of several trees with equal weighted leaves by which, maximum precision and accuracy could be attained flexibly with the prevailing data. Considering this, the trees are built with RF and the research introduces this process as ME-RF. Classification performance is evaluated individually on four different considered datasets under the implementation of minimum and maximum Deep CNN network and also with the use of combined dual deep CNN networks. The overall analytical outcomes confirms the effectiveness of the proposed system.

A conjugate self-organizing migration (CSOM) and reconciliate multi-agent Markov learning (RMML) based cyborg intelligence mechanism for smart city security

Ensuring the privacy and trustworthiness of smart city—Internet of Things (IoT) networks have recently remained the central problem. Cyborg intelligence is one of the most popular and advanced technologies suitable for securing smart city networks against cyber threats. Various machine learning and deep learning-based cyborg intelligence mechanisms have been developed to protect smart city networks by ensuring property, security, and privacy. However, it limits the critical problems of high time complexity, computational cost, difficulty to understand, and reduced level of security. Therefore, the proposed work intends to implement a group of novel methodologies for developing an effective Cyborg intelligence security model to secure smart city systems. Here, the Quantized Identical Data Imputation (QIDI) mechanism is implemented at first for data preprocessing and normalization. Then, the Conjugate Self-Organizing Migration (CSOM) optimization algorithm is deployed to select the most relevant features to train the classifier, which also supports increased detection accuracy. Moreover, the Reconciliate Multi-Agent Markov Learning (RMML) based classification algorithm is used to predict the intrusion with its appropriate classes. The original contribution of this work is to develop a novel Cyborg intelligence framework for protecting smart city networks from modern cyber-threats. In this system, a combination of unique and intelligent mechanisms are implemented to ensure the security of smart city networks. It includes QIDI for data filtering, CSOM for feature optimization and dimensionality reduction, and RMML for categorizing the type of intrusion. By using these methodologies, the overall attack detection performance and efficiency have been greatly increased in the proposed cyborg model. Here, the main reason of using CSOM methodology is to increase the learning speed and prediction performance of the classifier while detecting intrusions from the smart city networks. Moreover, the CSOM provides the optimized set of features for improving the training and testing operations of classifier with high accuracy and efficiency. Among other methodologies, the CSOM has the unique characteristics of increased searching efficiency, high convergence, and fast processing speed. During the evaluation, the different types of cyber-threat datasets are considered for testing and validation, and the results are compared with the recent state-of-the-art model approaches.

Application of the method of vacuum withdrawal inert radioactive gases to control of fuel rods leaking of nuclear reactors WWER types while operation

Currently, a comprehensive approach is being developed for the integrity control of fuel rod claddings in pressurized water reactor (PWR) nuclear power plants. One of the key stages is the reliable detection of leaking fuel rods within the reactor's active zone during power operation. This study examines the general trends in the activity of fission product radionuclides in the primary coolant of various generations and designs of PWRs. The experience of Novovoronezh Nuclear Power Plant is considered regarding the method of sampling and separation of inert radioactive gases from the primary coolant. A comparison is made between different methods of measuring inert radioactive gases in the primary coolant: direct spectrometry, cryogenic method, and vacuum extraction method. The issue of reliable detection of fuel rod leakage during reactor operation is discussed, using the example of Unit 5 at Novovoronezh Nuclear Power Plant. Comparative results are presented for the analysis of the integrity of fuel rod claddings based on key reference radionuclides. The most reliable results indicating fuel rod leakage were obtained through the control of specific activities and analysis of changes in the ratio of inert radioactive gases using the vacuum extraction method. Control of the specific activity of inert radioactive gases in the primary coolant enables a highly reliable assessment of the condition of physical barriers. The proposed method of sampling and separation of inert radioactive gases demonstrates high reliability and convergence of results, serving as an additional tool for integrity control of fuel rod claddings during power operation. Having representative data on the specific activities of inert radioactive gases allows for a reliable assessment of the condition of fuel rod claddings within the reactor core.

An adaptive control algorithm for harmonic cancellation in multiple-input multiple-output vibration systems

This paper presents a time-domain adaptive algorithm designed to eliminate harmonics in a Multiple-input Multiple-output (MIMO) system, taking into account its coupling characteristics. The algorithm is an improvement over the X-filtered LMS (FxLMS) algorithm, and is specifically tailored to the triaxial vibration system. The proposed algorithm has been shown to achieve high convergence, even in the presence of identification errors, as confirmed through both theoretical derivation and numerical simulations. To validate the efficacy of the proposed MIMO harmonic cancellation algorithm, it was tested on a genuine triaxial vibration system. The experimental results clearly demonstrate that the algorithm effectively reduces the output harmonic disturbance of the three axes’ frequency components.

Synthesis of Elliptical Antenna Array using Hybrid SSWOA Algorithm

In terms of research, the elliptical antenna arrays (EAA) synthesis is relatively novel. As it does not have to be circular in construction, this novel synthesis can maneuver the primary beam in the right direction, making it easier to realize. The amplitude and angular location of the ellipse, as well as the eccentricity of the ellipse, are all taken into account in the optimization process. The proposed hybrid algorithm is the SSWOA (Salp Swarm Whale Optimization Algorithm), which combines the Salp Swarm Optimization Algorithm (SSA) with the Whale Optimization Algorithm (WOA). The SSA algorithm serves as a guide, while the WOA algorithm serves as a helper in this method. We discover that optimization has a faster convergence time and high convergence accuracy when considering the benefits of SSA and WOA and applying them to the synthesis of antenna array layouts. If Griewank, Rosenbrock, Sphere, and Rastrigin test functions are used, it’s worth noting that the hybrid method outperforms both WOA and SSA.

Coupling Dependence on Chaos Synchronization Process in a Network of Rulkov Neurons

This paper investigates the impact of network conformation on chaos synchronization in driven complex networks in a master-slave setting. We analyze the control and prediction of complex networks under perturbations. Although chaotic dynamics suppression is well understood in low-dimensional systems, it is not well described in more complex ones. To assess a network’s ability to amplify or suppress disturbances, we track ensembles of possible trajectories in the driven subsystem which give insights into nonlocal convergence properties in a master-slave setting. We demonstrate how the stability of forced networks of Rulkov neurons is dependent on network coupling architecture and how the duration of chaotic transients depends on initial conditions. Furthermore, we find that each network generator algorithm has specific sets of parameters that can lead to stable or nonstable states. Our results indicate that network types are more critical than isolated connections for suppressing chaotic dynamics in driven networks. Finally, we identify that under certain circumstances, the transient period before synchronization correlates with high convergence rates for certain initial conditions. This suggests nonuniform convergence rates in the phase space of driven neural networks.

A meshfree large-deformation analysis method for geotechnical engineering based on the RBF field variable mapping technology

In this paper, a practical meshfree large deformation method (MFLDM) is proposed for numerical analysis in geotechnical engineerings, including: soil foundation, slop, dam, etc. The MFLDM leverages both the flexible nodal distribution in the meshfree method and the high stability in the arbitrary Lagrangian–Eulerian (ALE) framework. In each calculation step, two sets of Gauss points, fixed and moving Gauss points, are generated in the background mesh. In addition, the radial basis function (RBF) is used to map field variables, including stress, stain, and constitutive variables, between the fixed and moving Gauss points to achieve the field variable redistribution during the large-deformation analysis. The proposed MFLDM, which is written in C++ using the object-oriented programming approach, can be completely integrated into the self-development calculating system named GEODYNA and coupled with the finite element method (FEM) at the matrix level, which significantly broadens its practical application. The proposed model is verified by several numerical examples and compared with different constitutive models, including the linear elasticity model, ideal elastic–plastic model, and generalized elastic–plastic model. The comparison results verify the high accuracy, fast convergence, and good robustness of the proposed MFLDM. Finally, the proposed MFLDM is applied to a local large deformation analysis between the cut-off wall and the core wall on a deep overburden.

Effects of Laser Bandwidth in Direct-Drive High-Performance DT-Layered Implosions on the OMEGA Laser.

In direct-drive inertial confinement fusion, the laser bandwidth reduces the laser imprinting seed of hydrodynamic instabilities. The impact of varying bandwidth on the performance of direct-drive DT-layered implosions was studied in targets with different hydrodynamic stability properties. The stability was controlled by changing the shell adiabat from (α_{F}≃5) (more stable) to (α_{F}≃3.5) (less stable). These experiments show that the performance of lower adiabat implosions improves considerably as the bandwidth is raised indicating that further bandwidth increases, beyond the current capabilities of OMEGA, would be greatly beneficial. These results suggest that the future generation of ultra-broadband lasers could enable achieving high convergence and possibly high gains in direct drive ICF.

A Hybrid Genetic Algorithm-Based Parameter Identification Method for Nonlinear Hysteretic System with Experimental Verification

The Bouc–Wen nonlinear hysteretic model is widely used in nonlinear systems, and it is difficult to identify the parameters of Bouc–Wen model because of its high nonlinearity. In the paper, a hybrid genetic algorithm combined with sequential quadratic programming is proposed to solve the problem about the parameter identification of Bouc–Wen model. Sequential quadratic programming method has good local convergence and superlinear convergence rate. Therefore, compared with the genetic algorithm, the proposed identification method can accelerate the convergence speed in parameter recognition of Bouc–Wen model and achieve excellent recognition accuracy. Through the function test and the numerical simulation of the shear structure, it is proved that the proposed method has high convergence speed and accurate identification. In addition, the theory and method proposed in this paper are combined with Bouc–Wen model to identify parameters of overall restoring force model of the energy-dissipation structures, the identification results are in good agreement with the experimental results, verifying reliability and accuracy of the model and robustness of the proposed identification method.

IDENTIFICATION OF UNKNOWN PARAMETERS OF THE DYNAMIC MODEL OF MASS TRANSFER

An iterative algorithm for identifying unknown parameters of a mathematical model based on the Bayesian approach is proposed, which makes it possible to determine the most probable maximum informative estimates of these parameters. The example of the mathematical model of mass transfer dynamics shows the algorithm for finding the most probable and most informative estimate of the vector of unknown parameters, and also an analysis of the sequence of the corresponding steps is given. The results of computational experiments showed a significant dependence of the results of the calculations on the choice of the initial approximation point and slowing down the rate of convergence of the iterative process (and even its divergence) with an unsuccessful choice of the initial approximation. The validity of the obtained results is provided by analytical conclusions, the results of computational experiments, and statistical modeling. The results of computational experiments make it possible to assert that the proposed algorithm has a sufficiently high convergence for a given degree of accuracy and makes it possible to derive not only estimates of point values of mathematical model parameters based on a posteriori analysis, but also confidence intervals of these estimates. At the same time, it should be noted that the results of calculations depend significantly on the choice of the initial approximation point and the slowing of the convergence rate of the iterative process with an unsuccessful choice of the initial approximation. Analytical studies and results of calculations confirm the effectiveness of the proposed identification algorithm, which makes it possible, with the help of active, purposeful experiments, to build more accurate mathematical models. In accordance with the algorithm, a program was developed in the MatLab mathematics package and computational experiments were performed.

Quantitative techno-economic comparison of a photovoltaic/wind hybrid power system with different energy storage technologies for electrification of three remote areas in Cameroon using Cuckoo search algorithm

This research work presents a techno-economic comparisons and optimal design of a photovoltaic/wind hybrid systems with different energy storage technologies for rural electrification of three different locations in Cameroon. The determination of the optimal, cost-effective, and reliable configuration is performed for the locations of Fotokol, Figuil and Idabato which describe the weather conditions diversity in Cameroon with different solar and wind energy potentials. The sizing-optimisation problem is founded on real data of wind speed and solar radiation, with the principal goal of minimizing the hybrid system net present cost (NPC) while taking into consideration the load deficit probability (LDP) as the reliability constraint. The cuckoo search algorithm with a high convergence rate is utilised to find the optimal combination of the hybrid system with the lowest cost of energy (COE) and NPC which satisfies the LDP of the considered locations. At each location, six different configurations namely photovoltaic/wind/battery, photovoltaic/battery, wind/battery, photovoltaic/wind/fuel-cell, photovoltaic/fuel-cell and wind/fuel-cell are optimised and compared in view of satisfying the heavy, medium and small activities electrical loads demand of non-domestic subscribers. The optimal configuration is obtained for each activity and location in connection with the minimum cost and best reliability. The results revealed that the optimal, cost-effective, and reliable configuration for all activities and locations is a battery-based photovoltaic/wind hybrid system. The results showed that integration of 20 photovoltaic arrays, 1 wind turbine and 27 batteries with LDP value of 0.0486 is the most appropriate configuration which leads to the lowest NPC and COE of 23,024$ and 0.1570 $/kWh, respectively for satisfying the heavy activity load demand at Fotokol. The best hybrid system configuration needed for covering loads demand for medium and small activities at Fotokol were found to be 8 photovoltaic arrays, 1 wind turbine and 19 batteries with LDP, NPC and COE values of 0.0497, 16,640$ and 0.1942 $/kWh, respectively for medium activity; and 5 photovoltaic arrays, 1 wind turbine and 10 batteries with NPC, COE and LDP values of 12,860$, 0.2120 $/kWh and 0.0494, respectively for small activity. The optimisations studies performed at the site of Figuil considering the heavy activity load demand revealed that the cost-effective configuration which leads to the minimum NPC and COE of 28,400$ and 0.1937 $/kWh, respectively consists of 26 photovoltaic panels, 1 wind turbine and 38 batteries with the LDP equal to 0.0499. The obtained results also indicated that substituting the battery banks with a chemical storage which consist of a combination of an electrolyser, hydrogen reservoir and fuel cell is possible, though the cost increases because of the important investment cost of the system constituents. Integrating photovoltaic/wind/fuel-cell at the site of Idabato gives the lowest NPC and COE values of 48,864$ and 0.8056 $/kWh, respectively with a LDP of 0.0491. The optimum sizing of this configuration has 9 photovoltaic arrays, 1 wind turbine and 15 hydrogen tanks.

An Improved Integral Sliding Mode Control for PMSM Drives Based on New Variable Rate Reaching Law With Adaptive Reduced-Order PI Observer

This paper focuses on speed control in a permanent-magnet synchronous motor (PMSM). To promote the speed tracking performance and anti-disturbance property of PMSM speed regulation system against various unknown disturbances, such as parameter mismatch, load torque variation, friction force, and so on, an improved integral sliding mode control (IISMC) method based on a new variable rate reaching law (NVRRL) and an adaptive reduced-order proportional integral observer (AROPIO) is proposed. The NVRRL method can reduce chattering, and convergence time of the system state at the same time compared to the existing sliding mode reaching law. An anti-windup sliding mode surface is proposed to overcome the overshoot problem of the integral sliding mode surface when the reference speed is large and rises in steps or large ramps. To further improve the system tracking accuracy and anti-disturbance ability, an AROPIO is used to estimate lumped disturbances and provides feedforward compensation for the IISMC. The simulation and experimental results show that the proposed IISMC+AROPIO method not only achieves high speed tracking accuracy and fast convergence speed but also achieves strong anti-interference ability.

New Approach for Calculate Exponential Integral Function

This manuscript presents a new approach to accurately calculating exponential integral function that arises in many applications such as contamination, groundwater flow, hydrological problems and mathematical physics. The calculation is obtained with easily computed components without any restrictive assumptions A detailed comparison of the execution times is performed. The calculated results by the suggested approach are better and faster accuracy convergence than those calculated by other methods. Error analysis of the calculations is studied using the absolute error and high convergence is achieved. The suggested approach out-performs all previous methods used to calculate this function and this decision is based on the getting results.

Convergence Properties of the (μ/μI, λ)-ES on the Rastrigin Function.

The highly multimodal Rastrigin test function is analyzed by deriving a new aggregated progress rate measure. It is derived as a function of the residual distance to the optimizer by assuming normally distributed positional coordinates around the global optimizer. This assumption is justified for successful ES-runs operating with sufficiently slow step-size adaptation. The measure enables the investigation of further convergence properties. For moderately large mutation strengths a characteristic distance-dependent Rastrigin noise floor is derived. For small mutation strengths local attraction is analyzed and an escape condition is established. Both mutation strength regimes combined pose a major challenge optimizing the Rastrigin function, which can be counteracted by increasing the population size. Hence, a population scaling relation to achieve high global convergence rates is derived which shows good agreement with experimental data.

Dynamic Performance Improvement Using Model Reference Adaptive Control of Photovoltaic Systems under Fast-Changing Atmospheric Conditions

The effectiveness of a photovoltaic (PV) system can be increased by using maximum power point tracking (MPPT). The literature has suggested a number of methods for tracking the maximum power point (MPP). However, this number of methods most often presents a high convergence speed in reaching the MPP, complexity under their implementation, power fluctuations, overshoots, and great difficulty in reaching the MPP under fast-changing atmospheric conditions, thus influencing the efficiency of PV systems. Intending to improve the performance of PV systems under rapid changes in the atmosphere, this paper proposes model reference adaptive control (MRAC) as a technique for tracking the MPP based on the employ of reference models such as optimal voltage and current at the MPP ( V MPP and I MPP ). The MATLAB/Simulink environment is used to produce the simulation results; the Kyocera Solar KC 130 GT module is used here as a photovoltaic power plant, connected to a boost converter, supplying a resistive load. The Lyapunov theory was used to demonstrate the stability of the system. The simulation outcomes obtained using the suggested method are compared with those obtained by techniques such as perturb and observe (P&O), incremental conductance (INC), variable step incremental conductance (VSINC), particle swarm optimization (PSO), and grey wolf optimization (GWO), thus showing a very large improvement under standard test and fast-changing atmospheric conditions of the technique proposed on the other techniques in terms of convergence speed and tracking efficiency. The simulation results prove that the suggested method has great tracking effectiveness (>99.88%), less time for convergence (<0.01 s), and simple implementation complexity under fast-changing atmospheric conditions without both transient and steady-state power oscillations, overshoots, and chattering effects, thus causing a great minimization of energy losses, and the proposed technique reaches exactly the MPP under fast-changing atmospheric conditions.

Minimizing the electric vehicle charging stations impact in the distribution networks by simultaneous allocation of DG and DSTATCOM with considering uncertainty in load

The rise in popularity of electric vehicles (EVs) is attributable to their economical maintenance, excellent performance, and environmentally-friendly nature due to zero carbon emissions. Nevertheless, the increased utilization of EVs poses challenges for the distribution system’s efficiency. The strategic placement of electric vehicle charging stations (EVCS) is crucial in maintaining the reliability of the radial distribution system (RDS). The improper allocation of EVCS can result in degradation and affect the distribution system. To overcome this issue, a potential solution involves integrating the charging stations with the RDS by utilizing distribution static compensators (DSTATCOMs) and distributed generation (DG) to mitigate the adverse effects of EVCS on the RDS. The appropriate sizing of DG/DSTATCOM depends on variations in load stages, as it impacts the stability of the RDS. Additionally, the uncertainty of distribution loads can lead to an underestimation of power within the system, posing a primary challenge. In this proposed work, two studies were examined: (i) DG and DSTATCOM allocation considering load uncertainty without EVCS impact, and (ii) DG and DSTATCOM allocation considering load uncertainty with EVCS impact. To address this multi-objective problem, an objective function was developed to reduce real power loss while adhering to system equality and inequality constraints. To tackle the challenge, the researchers used the bald eagle search algorithm (BESA), a revolutionary metaheuristic optimization methodology. The efficacy of the proposed approach was validated using two test systems: a 34-bus system and a 118-bus system. The results obtained from these test cases demonstrate that the BESA-based solution is highly exact in reducing real power loss, increasing bus voltage, and enhancing system stability with a significantly high convergence rate. Hence, the proposed approach presents a promising solution for optimizing RDS with multiple objectives.

Improved Reptile Search Algorithm for Optimal Design of Solar Photovoltaic Module

This study focuses on the vital role of parameter extraction in optimizing and evaluating solar photovoltaic (PV) systems, as it directly influences their efficiency in converting solar energy to electricity. Researchers have extensively explored the application of various metaheuristic algorithms to accurately estimate solar PV parameters due to their crucial significance, leading to an extensive body of literature on the subject. However, the search for a robust and user-friendly optimizer with high convergence ability remains a challenging task that demands further research. To address this challenge, the study conducts a comprehensive comparative analysis of the RSALF optimizer, an innovative metaheuristic algorithm combining the reptile search algorithm (RSA) with Lévy flight (LF), for parameter extraction of PV model parameters using the Photowatt-PWP201 PV module as a case study. The experimental results demonstrate the RSALF optimizer's remarkable accuracy in parameter estimation, consistently yielding lower root mean square error values and closely aligning with experimental data. Moreover, comparative analysis with other recent optimization approaches highlights the RSALF optimizer's superiority, making it a promising tool for advancing the optimization of PV models and facilitating more efficient and sustainable solar energy utilization.

First-principles calculations to investigate optoelectronic, thermoelectric and elastic properties of novel lead-free halide perovskites CsRbPtX6 (X = Cl, Br and I) compounds for solar cells applications

The tunability and stability of halide perovskites have made them promising for a large number of applications. In this study, the optoelectronic and thermoelectric properties of novel halide lead-free halide perovskites of the form CsRbPtX6 (X = Cl, Br and I) are investigated for the first time using the Density Functional Theory (DFT). The Quantum Espresso package was used in performing the calculation of the properties of the perovskites. An analysis of their tolerance factors and octahedral factors indicated that the CsRbPtX6 (X = Cl, Br and I) are structurally stable. Likewise, their negative formation energy values imply their chemical stability and since their elastic constants meet the Born-Huang stability criteria, their mechanical stability is also confirmed. In addition to their remarkable stability, the materials are also predicted to have outstanding optoelectronic properties such as low reflectivity and good absorption in the visible region, indicating that they are suitable for photovoltaic applications. The calculated powers conversion efficiencies are ƞ = 7.27%, ƞ = 3.23% and ƞ = 6.3% for CsRbPtCl6, CsRbPtI6 and CsRbPtBr6, respectively. The materials also demonstrate excellent thermoelectric properties including ZT values close to unity and are therefore expected to possess high thermo-power convergence efficiency.