Proposed Optimization Technique Research Articles

Structural optimization of a rotary joint by hybrid method of FEM, neural-fuzzy and water cycle–moth flame algorithm for robotics and automation manufacturing

Rotary joint is used in robotic arm for mobility aids and rehabilitation device. The rotary joint often requires a compactness, a lightness, and a large load capacity in robotics as well as automation manufacturing. However, the experience-based design methods take a lot of time, finances, and human resources to achieve the mentioned multiple functions. To overcome the above difficulties, the article proposes a new design technique to solve the structural optimization for the rotary joint. The proposed optimization technique is formed by topology method, analysis of variance, finite element method, adaptive neuro-fuzzy inference system model, and water cycle moth-flame optimization algorithm. A new rotary joint is designed via the topology optimization. The adaptive neuro-fuzzy inference system is optimized by Taguchi technique to enhance the modeling accuracy. The geometry of the joint is optimized by the water cycle moth-flame optimization algorithm. The results found that the rotary joint can stand a torque of 357.46 N.mm with the equivalent stress up to 489.98 MPa. The difference between the optimal prediction results and simulations are 0.27% and 0.58% for the moment reaction and the equivalent stress, respectively. The small error proves the developed hybrid method has a high reliability.

Stochastic Optimization of the CPL-Function-Based Model for RF Power Transistors

In this article, the simultaneous perturbation stochastic approximation (SPSA) algorithm is proposed to optimize the threshold of the canonical piecewise linear (CPL) function-based models for radio frequency (RF) power transistors. Compared with the existing standard CPL-function-based model, the proposed optimization technique can greatly improve model performance. Two different CPL-based models, the CPL Y/quadratic Y (QY) model and the canonical section-wise piecewise linear (CSWPL) model, are used to verify the proposed technique. Experimental test of a 10-W GaN device from Cree is presented, showing the effectiveness of the proposed technique.

MULTIVARIATE PIECEWISE REGRESSIVE AFRICAN BUFFALO OPTIMIZATION-BASED RESOURCE-AWARE TASK SCHEDULING IN CLOUD

Distributed cloud computing handles a large number of tasks and provides many dynamic virtualized resources that aim to share as a service through the internet. While handling a large volume of tasks, task execution times, throughput, and makespan are the most significant metrics in practical scenarios. So, the scheduling task is essential to achieve accuracy and correctness on task completion. A novel technique called Multivariate Piecewise Regressive African Buffalo Optimization-based Resource Aware Task Scheduling (MPRABO-RATS) is introduced for improving the task scheduling efficiency and minimizing time consumption. First, the cloud user dynamically generates numerous heterogeneous tasks in the cloud environments. After receiving the tasks, the task scheduler in the cloud server finds the resource-optimized virtual machine using the Multivariate Piecewise Regressive African Buffalo Optimization technique. The proposed optimization technique uses the Multivariate Piecewise Regression function for analyzing the different resources availablity such as CPU Time, Memory, Bandwidth, and Energy before the task scheduling. Initially, the population of the virtual machine is defined. After that, the fitness is measured using Multivariate Piecewise Regression. Based on the fitness estimation, the resource-efficient virtual machine is determined. Finally, the task scheduler assigns the tasks to the resource-optimized virtual machine with higher efficiency. Experimental evaluation is carried out in the CloudSim simulator on the factors such as task scheduling Efficiency, Throughput, Makespan, and Memory Consumption with respect to a number of tasks. The observed results indicate that the MPRABO-RATS technique offers an efficient solution in terms of achieving higher task scheduling Efficiency, Throughput, and Minimizing the Makespan as well as Memory Consumption than the conventional scheduling techniques

Nonlinear marine predator algorithm: A cost-effective optimizer for fair power allocation in NOMA-VLC-B5G networks

This paper is an influential attempt to identify and alleviate some of the issues with the recently proposed optimization technique called the Marine Predator Algorithm (MPA). With a visual investigation of its exploratory and exploitative behavior, it is observed that the transition of search from being global to local can be further improved. As an extremely cost-effective method, a set of nonlinear functions is used to change the search patterns of the MPA algorithm. The proposed algorithm, called Nonlinear Marin Predator Algorithm (NMPA), is tested on a set of benchmark functions. A comprehensive comparative study shows the superiority of the proposed method compared to the original MPA and even other recent meta-heuristics. The paper also considers solving a real-world case study around power allocation in non-orthogonal multiple access (NOMA) and visible light communications (VLC) for Beyond 5G (B5G) networks to showcase the applicability of the NMPA algorithm. NMPA algorithm also shows its superiority in solving a wide range of benchmark functions as well as obtaining fair power allocation for multiple users in NOMA-VLC-B5G systems compared with the state-of-the-art algorithms.11For the source code and the application, you can download the related files via the following links: GitHub and MathWorks: https://github.com/alisafaa12/nmpa https://uk.mathworks.com/matlabcentral/fileexchange/111135-nonlinear-marine-predator-algorithm-nmpa.

Low-Complexity Beamforming Optimization for IRS-Aided MU-MIMO Wireless Systems

In this paper, we propose a cost-efficient beamforming optimization algorithm for multi-user wireless communication systems associated with the intelligent reflecting surface (IRS). From the baseline successive refinement algorithm, which gives a sub-optimal solution for the power minimization problem under the signal-to-interference-plus-noise-ratio (SINR) constraint at each user, several optimization techniques are proposed to reduce the computation complexity while maintaining the algorithm-level performance. To reduce the number of required multiply-accumulate (MAC) operations, we first simplify the complicated matrix inversion by utilizing the channel hardening effect. We also present the two-phase refinement process for the group-level optimization of phase-shift elements, further relaxing the computation complexity as well as the processing latency. Applying the proposed optimization techniques, as a result, numerical results show that the fully-optimized algorithm can reduce the computational costs by up to 89.4% while showing less than 1 dB power loss, leading to the practical solution for the next-generation IRS-aided communication.

Mayfly Optimization Algorithm Applied to the Design of PSS and SSSC-POD Controllers for Damping Low-Frequency Oscillations in Power Systems

In this paper, it is proposed to apply the mayfly optimization algorithm (MOA) to perform the coordinated and simultaneous tuning of the parameters of supplementary damping controllers, i.e., power system stabilizer (PSS) and power oscillation damping (POD), that actuate together with the automatic voltage regulators of the synchronous generators and the static synchronous series compensator (SSSC), respectively, for damping low-frequency oscillations in power systems. The performance of the MOA is compared with the performances of the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm for solving this problem. The dynamics of the power system is represented using the current sensitivity model, and, because of that, a current injections model is proposed for the SSSC, which uses proportional-integral (PI) controllers and the residues of the current injections at the buses, obtained from the Newton–Raphson method. Tests were carried out using the New England system and the two-area symmetrical system. Both static and dynamic analyses of the operation of the SSSC were performed. To validate the proposed optimization techniques, two sets of tests were conducted: first, with the purpose of verifying the performance of the most effective algorithm for tuning the parameters of PSSs, PI, and POD controllers, and second, with the purpose of performing studies focused on small-signal stability. The results have validated the current injections model for the SSSC, as well as have indicated the superior performance of the MOA for solving the problem, accrediting it as a powerful tool for small-signal stability studies in power systems.

Optimal design of a helical coil support for dewars in fuel cell applications.

Fuel cells are gaining popularity because of their efficient energy production without causing environmental pollution. Recently, DRDO has developed a fuel cell-based air-independent propulsion (AIP) system. In this system, the hydrogen is produced onboard while oxygen is carried in liquefied form (LOX) from the land in specially designed insulated storage vessels called dewars. Such vessels are needed because LOX has a low boiling point (NBP ~ 90K) and heat of vaporization (~ 213kJ/kg), due to which it boils off easily even when there is a small amount of heat inleak from the ambient. A typical dewar consists of two vessels separated by insulation. Support members are used to hold the two vessels together. Heat inleak through the supports and the insulation of the dewar causes the boiling of LOX. The vessels are subjected to dynamic loads during the voyage due to the filling and consumption of LOX. Therefore, the support system should be designed to withstand the dynamic loads experienced by the dewar. While the support system should have enough mechanical strength to withstand the loads it is subjected to, it should also restrict the heat inleak from the ambient to minimize the LOX boil-off. To meet this requirement, we need to optimize the support system design. Design optimization of support systems is especially critical in submarines to reduce the snorkeling frequency. Even though the dewars are available commercially for various applications, their design methodologies are not available in the open literature. Cylindrical rods are generally used as support members. In earlier studies, the authors have shown that helical coils give better thermal performance than tension rods as dewar supports. These two support systems involve different design criteria. It is important to evolve an optimal design of the support system to maximize the mechanical strength of the support while minimizing the heat inleak through the support. In this paper, we present a design methodology for optimizing helical support. We have proposed a modified optimization technique derived from the classical genetic algorithm (GA) for this purpose. The modification has been done by ensuring the design feasibility of the coil at each step of the algorithm. The proposed optimization technique has been tested on a LOX dewar, and an optimal design of the helical coil support has been obtained.

Optimal Planning of Electric Vehicle Charging Station along with Multiple Distributed Generator Units

Saving energy through the minimization of power losses in a distribution system is a key activity for efficient operation. Distributed Generation (DG) is one of the most efficient approaches to minimize losses. With increase in installation of Electric Vehicle Charging Stations (EVCSs) for Electrical Vehicles (EVs) in larger scale, optimal planning of EVCSs becomes a major challenge for distribution system operator. With increased EV load penetration in the electricity system, generation-demand mismatch and power losses increases. This results in poor voltage level, and deterioration in voltage stability margin. To mitigate the adverse impacts of increasing EV load penetration on Radial Distribution Systems (RDS), it is essential to integrate EVCSs at appropriate locations. The EVs integration into smart distribution systems involves Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) in charging and discharging modes of operation respectively for exchange of power with the grid thus resulting in energy management. The inappropriate planning of EVCSs causes a negative impact on the distribution system such as voltage deviation and an increase in power losses. In order to minimize this, DG units are integrated with EVCSs. The DGs assist in keeping the voltage profile within limitations, resulting in reduced power flows and losses, thereby enhancing power quality and reliability. Therefore, the DGs should be optimally allocated and sized along with the EVCS to avoid problems such as protection, voltage rise, and reverse power flow problems. This paper showcases a method to minimize losses using optimal location and sizing of multiple DGs and EVCS operating in G2V and V2G modes. The sizing and location of different types of DG units including renewables and non-renewables along with EV charging station is proposed in this study. This methodology overall reduces the power losses and also improves voltages of the network. The implementation is done by using the Simultaneous Particle Swarm Optimization technique (PSO) for IEEE 15, 33, 69 and 85 bus systems. The results indicate that the proposed optimization technique improves efficiency and performance of the system by optimal planning and operation of both DGs and EVs.

Surrogate-Assisted Multistate Tuning-Driven EM Optimization for Microwave Tunable Filter

This article proposes a novel surrogate-assisted multistate tuning-driven electromagnetic (EM) optimization technique to address the challenges of microwave tunable filter design with multiple tuning states. The desired multiple tuning states are satisfied simultaneously using the proposed surrogate-assisted technique. The proposed surrogate model is composed of several subsurrogate models. Each subsurrogate model is developed to perform the optimization for each tuning state. The subsurrogate models share the same values of nontunable parameters and possess different values of tunable parameters. The overall surrogate model is developed to find a single set of optimal solutions for nontunable parameters and multiple sets of optimal solutions for tuning parameters simultaneously. Parallel computation scheme is exploited to generate the training samples for establishing the proposed surrogate model. Furthermore, a new trust-region updating formulation specifically for multistate tuning is proposed to improve the convergence of the proposed optimization algorithm. Using the proposed optimization technique, different tuning states are considered together and optimized simultaneously. The values of nontunable design parameters are constrained by all tuning states and consequently there is a higher chance that more suitable solutions can be found to satisfy all the desired tuning states simultaneously. The proposed technique for the tunable filter design with multiple tuning states has a better capability of avoiding local minima and can reach the optimal solution more effectively in comparison with the existing optimization method. Two microwave examples are used to validate the proposed technique.

Superior performances strategies of different hybrid renewable energy systems configurations with energy storage units

The essential function of a hybrid renewable energy system is to produce an adequate electrical supply to the load demand with low cost. This paper proposes the optimization and operation of the renewable energy power sources for electrification of isolated rural city in Algeria desert. For this purpose, a system composed by PV panel (PV), Wind Turbine (WT), Battery Bank (BB) for storage of the electrical energy and Fuel Cell (FC) with hydrogen tank (H2 Tank) and electrolyzer (Elect) system for storage of the chemical energy is used to fulfill the need of the load. In the present paper we are interested in evolutionary algorithms for solving optimization problem of hybrid configuration of power system. However, a new heuristic algorithm namely whale optimization algorithm (WOA) is used to obtained the best solution of multi-objective optimization system of cost of energy (COE), total net present cost (TNPC) and loss power supply probability (LPSP). Two recent algorithms, particle swarm optimization (PSO) and grey wolf optimizer (GWO) are also implemented in this work. Seven cases studies have been tested for examining the efficient of proposed optimization technique. The suggested whale optimization algorithm, as demonstrated by simulations and comparisons with existing methods, solves the problem of multi-objective optimization of hybrid power system configurations with high accuracy and validity.

An efficient GPU acceleration technique for CBCT based on memory aware optimization scheme

Among many of the image reconstruction techniques; the Feldkamp-Davis-Kress (FDK) is considered the most practical algorithm that used in clinical cone-beam Computed Tomography (CT) technology. In this paper, we present a full memory-aware management optimization scheme to reconstruct high-resolution volumes for a large number of X-Ray images as projections based on an efficient GPU acceleration technique. The proposed optimization technique is a scheme for FDK acceleration, that sheds new light on 4 new strategies: (1) Overcome limited device memory by evaluating different four configurations to migrate input and output data in the CPU/GPU system, (2) Reduce data transfer time during the FDK execution by using a Unified Memory (UM) model with data prefetching feature during reconstruction for data migration between host and device, (3) Accelerate the bottleneck algorithm back-projection as a result of computation mitigation, and (4) Optimize projection data layout that led to better device memory access and overcomes memory latency problem before processing and computing on GPU. The experimental results based on the proposed method show that it took 21.17 sec and 101.76 sec to reconstruct high-resolution 1024 3 , 2048 3 - voxel volume from 1800 projection of 1024 2 and 2048 2 -pixel respectively. The performance resulted from our optimization technique considered on-the-fly reconstruction (real-time), which means it can process 85 projections per second for high-resolution volumes from a large number of projections to achieve accuracy and resolution for reconstructed volume. In the term of speedup, the proposed method can achieve 1.72 speedup to reconstruct the low-resolution volume 512 3 , 1.82 speedup for 1024 3 volume and 1.70 s p e e d u p f o r 2048 3 for highest resolution volumes over baseline implementation and optimized over previous reconstruction methods based on FDK.

Development of quality microholes by electrical discharge drilling on Al/SiC composite using of Grey-desirability approach

Electric discharge drilling (EDD) is a variant of spark erosion machining and is successfully used for the drilling of quality microholes. In present research, an attempt was made to drill microholes on Al/SiC composite. The drilling on aluminum composite was made at different machining indicators to analyze its effect on drilling rate (DR), electrode wear ratio (EWR), hole overcut (HO) and circularity (C). The input parameters selected in this work are pulse on-time (Ton), peak current (Ip) and pulse off-time (Toff). The experiments were planned as per Taguchi's L9 orthogonal array (OA). It was found that with the increase in Ton value, HO and EWR increases. However, larger values of Ip and Toff decreases the HO and EWR. Due to the combination of different quality characteristics Grey theory along with desirability has been implemented for collective response of output parameters. The optimized setting for multiple performance characteristics is Ton: 40; Ip: 3; Toff: 50. An improvement of 0.0199 is observed in grade value during optimization as compared to the maximum calculated grade value. After the implementation of proposed optimization technique, an improvement of 42.88% in DR, a reduction of 33.59% in EWR, a reduction of 4.54% in HO and 0.68% in C is observed.

Adaptive Dynamic Control Based Optimization of Renewable Energy Resources for Grid-Tied Microgrids

Renewable-energy-resource-based microgrids can overcome excessive carbon footprints and increase the overall economic profile of a country. However, the intermittent nature of renewables and load variation may cause various control problems which highly affect the power quality (frequency and voltages) of the overall system. This study aims to develop an adaptive technique for the optimization of renewable energy resources (RERs). The proposed grid-tied microgrid has been designed using a wind-turbine (WT) based distributed generation, a photovoltaic (PV) system, a diesel generator as an emergency backup, and battery energy storage system (BESS). The flexible (residential) and non-flexible (industrial) loads are connected with the proposed grid. Matlab/Simulink has been used to evaluate the performance of the proposed optimization technique. Comparison with different in-use techniques shows that the proposed technique is more reliable and efficient than the state of the art optimization techniques currently in use. Moreover, this proposed system provides robust optimization of parameters of concern such as frequency and voltages, makes efficient use of the maximum power point tracking while regulating voltages, reduces the overall system cost, and increases economic profitability.

Power quality improvement of distribution systems asymmetry caused by power disturbances based on particle swarm optimization-artificial neural network

With an increase of non-linear load in today’s electrical power systems, the rate of power quality drops and the voltage source and frequency deteriorate if not properly compensated with an appropriate device. Filters are most common techniques that employed to overcome this problem and improving power quality. In this paper an improved optimization technique of filter applies to the power system is based on a particle swarm optimization with using artificial neural network technique applied to the unified power flow quality conditioner (PSO-ANN UPQC). Design particle swarm optimization and artificial neural network together result in a very high performance of flexible AC transmission lines (FACTs) controller and it implements to the system to compensate all types of power quality disturbances. This technique is very powerful for minimization of total harmonic distortion of source voltages and currents as a limit permitted by IEEE-519. The work creates a power system model in MATLAB/Simulink program to investigate our proposed optimization technique for improving control circuit of filters. The work also has measured all power quality disturbances of the electrical arc furnace of steel factory and suggests this technique of filter to improve the power quality.

Hybrid particle swarm optimization‐grey wolf optimization basedsmall‐signalmodeling applied toGaNdevices

In this research, four different equivalent circuit models that characterize the substrate/buffer loading effect for GaN high electron mobility transistor (HEMT) on Si substrate have been investigated. Y-parameter measurements of an open de-embedded structure have been used to characterize this effect in GaN HEMT on Si. An optimization technique based on hybridization between two meta-heuristic techniques, which are particle swarm optimization (PSO) and grey wolf optimization (GWO) has been developed to extract physical relevant model elements. The proposed optimization technique is applied on GaN HEMTs of different sizes and validated by means of S-parameters fitting. In addition, the extracted elements are evaluated in terms of their reliability and scalability. The proposed PSO-GWO technique shows a very good agreement with previous research results. It has been found that the circuit topology of both resistive and capacitive shunt branches is more accurate in characterizing conductive and displacement substrate/buffer leakage current, respectively. The results of this research validate the applicability of the proposed technique and the model topology for small and large-signal modeling of GaN transistors on Silicon substrate.

Stochastic Approach to Hosting Limit of Transmission System and Improving Method Utilizing HVDC

According to the global de-carbonization trends, renewable energy integration has become an increasingly important issue in power systems. To achieve 100% renewable energy integration and operate a system with these resources, it is necessary to appropriately evaluate the system hosting capability and prepare appropriate planning and operation strategies using the evaluation result. So far, these interests have focused particularly on distribution-level systems. However, although the hosting limit in transmission-level systems requires further consideration, previous study is limited. This study introduces the constraints on the transmission-level hosting limit. In addition, a stochastic estimation of the hosting limit methodology in the transmission system and the use of a high voltage direct current system to improve hosting capacity are proposed and evaluated. Moreover, these methodology-based simulations are conducted using possible scenarios on the IEEE 39 bus system with some constraints, and the simulation results are presented herein. The results showed that the HVDC location selection and operation using the proposed method and optimization technique is appropriate. The strategy can be used to integrate more renewable energy. Furthermore, the proposed methodology can be applied to renewable energy integration scenario establishing a plan.

Three-Parameter Rain Drop Size Distributions From GPM Dual-Frequency Precipitation Radar Measurements: Techniques and Validation With Ground-Based Observations

Raindrop size distribution (DSD) parameters need to be estimated to determine rainfall from radar measurements of backscattered signals in the precipitating medium and to study microphysics of precipitation under varying atmospheric conditions. A technique is proposed to estimate the DSD parameters from Global Precipitation Measurement (GPM) dual-frequency products at Ku and Ka bands, which has been validated with ground-based disdrometer measurements obtained during the five-year period 2014-2018 over a tropical location, Kolkata (22°54’N, 88°35’E), India. The present method yields the three parameters of the DSD model in terms of a gamma function namely, intercept ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N₀</i> ), shape (μ) and slope (Λ). Based on a proposed optimization technique, the most appropriate μ-Λ relationship is determined during a GPM satellite pass to obtain the DSD parameters. The derived parameters have yielded mass-weighted mean diameter ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D_m</i> ) data that show much better agreement with ground-based DSD measurements than that provided by GPM DPR product. This method has also been used to obtain the height profiles of DSD that broadly correlate with ground-based micro rain radar (MRR) observations, showing how DSD varies at different altitudes. The present results demonstrate that the proposed technique is effective in obtaining three-parameter DSDs over a tropical location, and offers a useful method to study the microstructure of rain using space-borne radar.

Optimum Tuning of Photovoltaic System Via Hybrid Maximum Power Point Tracking Technique

A new methodology is used in this paper, for the optimal tuning of Photovoltaic (PV) by integrating the hybrid Maximum Power Point Tracking (MPPT) algorithms is proposed. The suggested hybrid MPPT algorithms can raise the performance of PV systems under partial shade conditions. It attempts to address the primary research issues in partial shading conditions in PV systems caused by clouds, trees, dirt, and dust. The proposed system computes MPPT utilizing an innovative adaptive model-based approach. In order to manage the input voltage at the Maximum PowerPoint, the MPPT algorithm changes the duty cycle of the switch in the DC-DC (Direct Current-Direct Current) converter (MPP). Temperature as well as fluctuations in output power will induce alteration in PV panel operating current and voltage. MPPT is gaining a lot of interest as a key optimization sector to solve this optimization problem in PV systems. A hybrid optimization approach is utilized to produce a combination of the Cuckoo Search-Perturb & Observe (CS-PO) and incremental conductance-particle swarm optimization (IC-PSO) algorithms. After measuring the voltage and current from the solar system, this optimization computes the output power. The IC-PSO optimization achieves Maximum PowerPoint Tracking with increased efficiency of 99.5%. The proposed optimization techniques are established in the MATLAB Simulink program to validate its efficiency.

Experimental investigation and parametric optimization during EDM of Titanium grade 9 using MOORA-fuzzy integrated multi-objective Genetic algorithm

The current work investigates an experimental study and potential of multi-objective optimization of machining performance characteristics “Material Removal Rate” (MRR) and “Tool Wear Rate” (TWR) during the “Electro-Discharge Machining” (EDM) process of Titanium grade 9 (Ti-3Al-2.5 V) (Ti G9). The Taguchi’s L9 OA has been employed for selecting the appropriate outline of the experiment by varying Spark-on time (Son), Spark-off time (Soff), Peak Current (Ip), and Gap Voltage (Vg). A hybrid technique called MOORA-Fuzzy Integrated multi-optimization GA has been proposed to determine the best satisfactory machining performances. The most favorable machining parameter obtained through optimization technique as [Son = 100 µs, Soff = 100 µs, Ip = 7A, and Vg = 70 V] for maximize MRR and minimize TWR. In the present study, the proposed optimization technique can be capable enough to solve the multi-process optimization problems of the EDM process. Moreover, it is apparent very helpful in the manufacturing sectors, reducing machining time and improving productivity.

A Novel Optimized Language-Independent Text Summarization Technique

A substantial amount of textual data is present electronically in several languages. These texts directed the gear to information redundancy. It is essential to remove this redundancy and decrease the reading time of these data. Therefore, we need a computerized text summarization technique to extract relevant information from group of text documents with correlated subjects. This paper proposes a language-independent extractive summarization technique. The proposed technique presents a clustering-based optimization technique. The clustering technique determines the main subjects of the text, while the proposed optimization technique minimizes redundancy, and maximizes significance. Experiments are devised and evaluated using BillSum dataset for the English language, MLSUM for German and Russian and Mawdoo3 for the Arabic language. The experiments are evaluated using ROUGE metrics. The results showed the effectiveness of the proposed technique compared to other language-dependent and language-independent summarization techniques. Our technique achieved better ROUGE metrics for all the utilized datasets. The technique accomplished an F-measure of 41.9% for Rouge-1, 18.7% for Rouge-2, 39.4% for Rouge-3, and 16.8% for Rouge-4 on average for all the dataset using all three objectives. Our system also exhibited an improvement of 26.6%, 35.5%, 34.65%, and 31.54% w.r.t. The recent model contributed in the summarization of BillSum in terms of ROUGE metric evaluation. Our model’s performance is higher than the compared models, especially in the metric results of ROUGE_2 which is bi-gram matching.