Jaya Algorithm Research Articles

A discrete Jaya algorithm for vehicle routing problems with uncertain demands

Vehicle Routing Problem with Uncertain Demands (VRPUD) is one of the research hotspots in the field of logistics scheduling. In this paper, a Discrete Jaya (DJaya) algorithm is presented for the VRPUD to minimize the total cost. A novel dividing-point-based coding scheme is designed to represent solutions with higher robustness. In addition, an efficient repair strategy is embedded into the decoding process to avoid the failure of producing feasible solutions. The best and worst solutions are employed to generate offspring solutions in DJaya. Several efficient local search methods are also presented to enhance the exploitation ability and increase the diversity of solutions. Based on the benchmark data sets of the VRPUD, numerical simulations are carried out for the proposed DJaya algorithm with uncertain demands. Computational results and comparisons with the state-of-the-art algorithms demonstrate the superiority of the proposed algorithm in solving VRPUD.

Energy consumption optimization of chiller plants with the genetic algorithm based GWO and JAYA algorithm in the dynamic pricing demand response

Demand response is a program in the electrical system that is used to increase or decrease the energy demand of the consumers where the consumers can actively respond to the system requirements. We developed an energy scheduling system to schedule the consumer load in a dynamic pricing scheme to reduce the cost and lower the peak demand. The problem formulation is done to model the food storage chiller plant for cost minimization with the consumer constraint. The optimal schedule is to be generated in a real time varying electricity price scheme. To solve this optimization problem Grey wolf optimizer (GWO) algorithm and JAYA optimization algorithms are used. Besides, the Genetic algorithm (GA) based GWO and GA based JAYA algorithm are developed and simulated. The genetic algorithm improves the population diversity of the solutions. The simulation results show a 22 % cost reduction and 10 % energy consumption reduction as compared with normal control method and consumer constraints are satisfied. The algorithms GA-GWO and GA-JAYA give better results for optimal cost minimization in terms of the reduced cost, energy consumption, energy consumption shift to low electricity price hours and temperature maintained below the mean set temperature through the entire duration.

Optimization enabled LeNet for big data classification using MapReduce framework on COVID-19 data

ABSTRACT The big data clustering is a requisite for generating the data in the digitalised globe. The old-fashioned clustering approaches are not large sized and highly unorganised big data. Thus, to obtain the efficiency of big data clustering, a new architecture is required. This work introduces the Jaya African Vulture Optimization Algorithm-based LeNet (JAVO-based LeNet) for big data classification on COVID-19. The big data clustering is detected by using the LeNet model, which is tuned by the JAVO algorithm. Here, the JAVO algorithm is the integration of the Jaya algorithm and AVOA algorithm. Further, using the Deep Embedded Clustering (DEC) technique for partitioning the smaller data to improve the performance of big data classification. The classification process for COVID-19 is established in the MapReduce (MR) framework. For each mapper, the pre-processing is completed based on Min-Max normalisation and the feature fusion is performed based on the Hellinger distance, which is measured with a deep residual network (DRN). Finally, the classification process for COVID-19 is done by considering the fused data acquired from each mapper with LeNet, which is tuned by the JAVO algorithm. Furthermore, the analysis of the proposed method attains the testing accuracy of 0.948, specificity of 0.939 and sensitivity of 0.950.

Ankylosing spondylitis prediction using fuzzy K-nearest neighbor classifier assisted by modified JAYA optimizer

The diagnosis of ankylosing spondylitis (AS) can be complex, necessitating a comprehensive assessment of medical history, clinical symptoms, and radiological evidence. This multidimensional approach can exacerbate the clinical burden and increase the likelihood of diagnostic inaccuracies, which may result in delayed or overlooked cases. Consequently, supplementary diagnostic techniques for AS have become a focal point in clinical research. This study introduces an enhanced optimization algorithm, SCJAYA, which incorporates salp swarm foraging behavior with cooperative predation strategies into the JAYA algorithm framework, noted for its robust optimization capabilities that emulate the evolutionary dynamics of biological organisms. The integration of salp swarm behavior is aimed at accelerating the convergence speed and enhancing the quality of solutions of the classical JAYA algorithm while the cooperative predation strategy is incorporated to mitigate the risk of convergence on local optima. SCJAYA has been evaluated across 30 benchmark functions from the CEC2014 suite against 9 conventional meta-heuristic algorithms as well as 9 state-of-the-art meta-heuristic counterparts. The comparative analyses indicate that SCJAYA surpasses these algorithms in terms of convergence speed and solution precision. Furthermore, we proposed the bSCJAYA-FKNN classifier: an advanced model applying the binary version of SCJAYA for feature selection, with the aim of improving the accuracy in diagnosing and prognosticating AS. The efficacy of the bSCJAYA-FKNN model was substantiated through validation on 11 UCI public datasets in addition to an AS-specific dataset. The model exhibited superior performance metrics—achieving an accuracy rate, specificity, Matthews correlation coefficient (MCC), F-measure, and computational time of 99.23 %, 99.52 %, 0.9906, 99.41 %, and 7.2800 s, respectively. These results not only underscore its profound capability in classification but also its substantial promise for the efficient diagnosis and prognosis of AS.

Sliding Mode Control Based on Jaya Algorithm of Phase Shift for Two-Rotor Vibration Unit

In this paper, a sliding mode control based on the Jaya algorithm o f phase shift for a two-rotor vibration unit is presented. A two-rotor vibration unit is a highly nonlinear system that has many applications in practical systems. The control problem for this model is to design controllers to ensure phase shift according to the desired value and synchronize the speed of the two rotors. To solve this problem, the authors use the PI controllers for synchronizing the speed of the two rotors and the sliding mode controller for the phase shift. PI control is an effective method to design controllers for practical systems. Besides, Sliding Mode Control (SMC) is one of the most effective methods for designing robust controllers for nonlinear systems affected by external disturbances. However, defining the coefficients of PI controllers and sliding mode controllers is the challenge for researchers. In this study, the Jaya algorithm is used to find the coefficients of these controllers that guarantee the desired phase shift of two rotors, the synchronizing speed of two rotors, and the limitation of control laws. The simulation results are illustrated to show the effectiveness of the proposed method.

Comparison of the PSO Algorithm and JAYA Algorithm for Economic Load Dispatch

The electrical power industry has undergone the significant changes, leading to a competitive market and the need for optimal economic dispatch solutions. As energy resources becoming scarce, prices rise, environmental concerns intensify, and electricity consumption increases, classical optimization techniques struggle to handle these complex issues. This project compares particle swarm optimization (PSO) and JAYA optimization method for economic load dispatch (ELD) problems in the electrical power industry. PSO has gained recognition as an effective solution for ELD problems in the last decade. The aim of this study is to show that Jaya optimization is the best optimization methodology for ELD problems. By comparing the performance, convergence speed, and accuracy of PSO and JAYA optimization, this paper aims to provide insights into the superiority of JAYA optimization. Key Words: Economic Load dispatch, Optimization method, PSO algorithm, JAYA algorithm, Incremental fuel cost

Optimizing fog device deployment for maximal network connectivity and edge coverage using metaheuristic algorithm

Fog computing emerged to address the limitations and challenges of traditional Cloud computing, particularly in handling real-time, heterogeneous, and latency-sensitive applications. However, the spread of Fog computing devices across the network introduces various challenges, especially concerning device connectivity and ensuring sufficient coverage to fulfil users’ requests. To maintain network operability, Fog Device Deployment (FDD) must effectively consider two crucial factors: connectivity and coverage. Network connectivity relies on FDD, determining the physical network topology, while coverage determines the accessibility of the Internet of Things (IoT) or edge devices. Both these objectives significantly impact the network performance and guarantee the network's Quality of Service (QoS). However, determining an optimal FDD method that reduces computation and communication overhead, and provides high network connectivity and coverage, is challenging. In this work, we propose an FDD algorithm that effectively connects the Fog devices for internal communication and covers maximum edge devices to entertain the requests. Firstly, FDD is formulated as a multi-objective optimization problem and then, an emerging metaheuristic Jaya Algorithm (JA) is applied to optimize the multi-objective function. The suitability of the JA, for the FDD problem, is substantiated by its rapid convergence and better computational complexity when contrasted with other contemporary population-based algorithms. In conclusion, the performance of the proposed method is assessed across a spectrum of benchmark-generated instances, each reflecting distinct Fog scenarios. The experimental outcomes showcase the proposed method's remarkable promise, especially when compared against state-of-the-art methodologies.

Optimal design of adaptive model predictive control based on improved GWO for autonomous vehicle considering system vision uncertainty

The tuning issue of the parameters and the system uncertainty represent big challenges in most engineering applications. In this regard, a new tuning approach is developed for adaptive model predictive control (AMPC) of autonomous vehicles (AVs). The proposed control strategy tackles the uncertainty issue of the vision system due to the variation of the velocity, road curvature, time delay, and look ahead distance. The proposed AMPC is designed depending on a new improved grey wolf optimizer (IGWO) algorithm with different learning procedures and fitness-distance balance (FDB) approach to equilibrium the exploration and exploitation manners of the original GWO. This development of GWO based on FDB can increase the capability of the GWO for global search and avoid trapping in local states. Furthermore, the proposed IGWO does not require more adjustable parameters that can enhance the convergence to the best global solution rapidly. The new IGWO is compared with recent optimization algorithms such as the mayfly optimization algorithm (MA), chimp optimization algorithm (COA), dynamic arithmetic optimization algorithm (DAOA), whale optimization algorithm (WOA), jaya algorithm (JA), archimedes optimization algorithm (AOA), and equilibrium optimizer (EO) as well as the original GWO. Besides, the performance of the designed AMPC is compared with the fuzzy logic proportional integral (FLPI) controller and the traditional MPC. Various test scenarios are performed to assert the robustness and effectiveness of the designed AMPC against the variation of the velocity, road curvature, time delay, and look ahead distance. The results emphasize that the proposed AMPC can perform the best damping response with an overshoot of around 1.3 m and a settling time of around 1.12 s less than the FLPI controller and the traditional MPC.

Optimized convolutional neural network for land cover classification via improved lion algorithm

AbstractDependable land cover data are required to aid in the resolution of a broad spectrum of environmental issues. Land cover classification at a broad scale has been carried out using data from traditional ground‐based information from the Advanced Very High‐Resolution Radiometer. From the merits as well as demerits of the existing works discussed in the literature, this article seeks to establish a novel technique for automatic, fast, as well as precise land cover classification deploying remote sensing data. The proposed approach follows feature extraction and classification stages. From input information, the statistical characteristics are extracted as well as they are subjected to classification via optimized deep convolutional neural network. Particularly, the convolutional layers are optimized for by means of a new Proposed Lion Algorithm with a new Cub pool Update (PLACU) approach. The established model is the advanced level of the standard lion algorithm. The superiority of the established technique is determined by the extant techniques regarding positive and negative metrics. The accuracy of the work that is being presented (PLACU) is superior to the existing methods like Dragonfly algorithm, Jaya algorithm, sea lion optimization, and lion algorithm techniques by 20%, 15%, 112%, and 10%, respectively.

A data-driven exploration of multi pad bidirectional adjustable bearings and deep learning model-based optimization

The evolution of journal bearings, from fixed profiles to adaptive configurations, signifies a captivating progression in journal bearing technology. A notable innovation in adaptive bearings is Multi-pad Bidirectional Adjustable Bearing (MBAB), which can control film thickness in radial and circumferential directions. This paper introduces a novel data-driven approach, utilizing machine learning to model and optimize the static performance of MBABs with asymmetric bearing element adjustments. The study uses a parameter-independent Jaya algorithm coupled with machine learning models to identify optimal combinations of adjustments. Results highlight the significance of negative radial adjustments and asymmetric profiles for optimal performance. This research contributes to Industry 4.0 by bridging the physical-digital gap, offering a data-driven solution to enhance the performance of these advanced bearings.

Optimized Proportional Integral Derivative Based Power System Stabilizer Using Jaya Algorithm for Angular Stability Enhancement

This study presents an optimized Proportional Integral Derivative Based Power System Stabilizer (PIDPSS) using Jaya Algorithm for angular stability enhancement. Jaya algorithm introduced by Rao, is an optimization technique with few control parameters which is used to minimize the objective function F(K). The modeling and simulation were done using Matlab /Simulink software version R2021b on IEEE 14-Bus system and Single Machine Infinite Bus (SMIB). A three-phase fault was introduced into the network at system runtime of 5s with a fault clearing time of 0.1s. The result of the simulation of the IEEE 14 Bus system showed a 74% and 24% reduction in overshoot time of speed deviation for generators 1 and 2, with settling times of 2.5s and 4s, respectively, in the presence of PIDPSS. The load angle experienced a 14% and 19% reduction in overshoot with settling times of 2s and 2.5s, respectively in the presence of PIDPSS for generators 1 and 2, respectively. The Electrical Power result showed 27% and 6% reduction in overshoot time as well as settling times of 2.5s and 4s, respectively, for generator 1 and 2 in the presence of PIDPSS. The result of the simulation of SMIB system also showed a 25% reduction in overshoot time in relation to deviation speed at a settling time of 4s in the presence of PIDPSS. The Load Angle showed- a 13% decrease in overshoot time at 2s settling time in the presence of PIDPSS. Also, the Electrical Power result highlighted a 15% drop-in overshoot time and settles within 2s. These results affirms that the PIDPSS introduced improved overall system stability.

Machine learning-based model for moment capacity prediction and reliability analysis of PSC beams

This study aims to develop an accurate and reliable Natural Gradient Boosting (NGB) model to predict the moment capacity of the prestressed concrete (PSC) beams. For this purpose, a database of 188 experimental results is first collected from the tests in the literature. The base learner of the NGB model is optimized using the Jaya Algorithm (JA), Moth Flame Optimizer (MFO), Sine Cosine Algorithm (SCA), and Sparrow Search Algorithm (SSA). Accordingly, the optimal base learner is implemented in the NGB model to get the final results. The performance of the NGB model is compared with those of the recently popular and efficient ML algorithms, such as Random Forest (RF), Adaptive Gradient Boosting (AGB), Gradient Boosting (GB), and eXtreme Gradient Boosting (XGB) models and the design approach. The comparative results show that the NGB models with the SSA-DT base learner (R2 = 0.981, A10 = 0.897, RMSE = 5.040 kN.m, MAE = 4.132 kN.m in the test phase) outperform others. A resistance reduction factor (ϕ) value of 0.8 to 0.925 is recommended for the developed NGB model to achieve a specific reliability index (βT) value of 2.5 to 3.5 via reliability analysis. Finally, a web application is developed to facilitate the moment capacity prediction without time consumption and cost.

Temperature Profile Estimation Downstream to T-Junction for Thermal Mixing of Fluids

The thermal mixing of hot and cold streams at T-junctions causes temperature fluctuations which may result in thermal fatigue. Hence for a T-junction, the knowledge of the temperature profile developed by the mixing of two fluids at different temperatures is essential. In this paper, the temperature profile downstream to the T- junction for steady laminar mixing of fluids at different temperatures is estimated by the enhanced conjugate gradient method (ECGM) algorithm. The ECGM is the combination of the recently developed Jaya algorithm and the traditional conjugate gradient method (CGM). Different test cases like the number of sensors, location of sensors and errors in the measurements have been considered. The performance of algorithms is determined by the root mean square (RMS) error. The estimation of the temperature profile by the ECGM algorithm is found to be better (RMS = 6.56 K) than CGM (RMS = 14.12 K).

Research on height prediction of water-conducting fracture zone in coal mining based on intelligent algorithm combined with extreme boosting machine

The development of water-conducting fracture zones (WCFZ) caused by coal mining is a destructive phenomenon of mining damage. The development height of the WCFZ is an important reference index for the prevention and control of safety hazards such as water infiltration or sand intrusion in mines and for program optimization. In this work, a combinatorial optimization model for height prediction of WCFZ is established by combining the extreme gradient boosting machine and several commonly used intelligent algorithms: genetic algorithm, particle swarm optimization, jaya algorithm, sparrow search algorithm. Accordingly, 142 sets of WCFZ composed of 4 main parameters were selected as the input independent variables, and the maximum height (H) was selected as the output dependent variable. In addition, to demonstrate the validity of the proposed combinatorial optimization models, this study used the following four models (Random Forest, Bagging, XGBoost and AdaBoost) for comparison. Model performance evaluation criteria including the coefficient of determination, root mean square error, mean absolute error, and variance accounted for are used to evaluate the model. In this work, 142 sets of cases to the WCFZ analyzed and the method of Shapley Additive Explanations was used to explain the importance and contribution of features to maximum height prediction. The research results show that, compared with other machine learning models, the combinatorial optimization model put forward in this work can improve the predicted accuracy and reliability of height prediction of WCFZ. The research in this work may help researchers use machine learning models to predict and research the height development of WCFZ.

A hybrid evolutionary JAYA algorithm based on global optimization for 5G e-commerce logistics

With the increasing demand for logistics in modern society, how to achieve low-cost and efficient logistics delivery has become an urgent research topic. A hybrid evolutionary JAYA algorithm (H-JAYA) based on global optimization was designed to address the complex path planning problem of electric vehicles. This algorithm introduces a reverse learning mechanism to calculate the current optimal and worst individuals, while using differential perturbation mechanism and sine cosine operator to update the individual’s position. In addition, the study used the H-JAYA algorithm to construct a corresponding mathematical model for the optimization problem of electric vehicle paths. The results showed that in the three examples, the H-JAYA algorithm tested the optimal curve convergence speed, and it tended to stabilize after about 30 iterations. Meanwhile, in the RCDP5001 example, the total cost of the H-JAYA algorithm reached the lowest value of 623 yuan. The H-JAYA algorithm has significant advantages in solving the distribution path problem of electric vehicles, and can be well applied to practical logistics distribution, providing effective technical support for modern e-commerce logistics planning.

BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification

A significant issue in computer-aided diagnosis (CAD) for medical applications is brain tumor classification. Radiologists could reliably detect tumors using machine learning algorithms without extensive surgery. However, a few important challenges arise, such as (i) the selection of the most important deep learning architecture for classification (ii) an expert in the field who can assess the output of deep learning models. These difficulties motivate us to propose an efficient and accurate system based on deep learning and evolutionary optimization for the classification of four types of brain modalities (t1 tumor, t1ce tumor, t2 tumor, and flair tumor) on a large-scale MRI database. Thus, a CNN architecture is modified based on domain knowledge and connected with an evolutionary optimization algorithm to select hyperparameters. In parallel, a Stack Encoder–Decoder network is designed with ten convolutional layers. The features of both models are extracted and optimized using an improved version of Grey Wolf with updated criteria of the Jaya algorithm. The improved version speeds up the learning process and improves the accuracy. Finally, the selected features are fused using a novel parallel pooling approach that is classified using machine learning and neural networks. Two datasets, BraTS2020 and BraTS2021, have been employed for the experimental tasks and obtained an improved average accuracy of 98% and a maximum single-classifier accuracy of 99%. Comparison is also conducted with several classifiers, techniques, and neural nets; the proposed method achieved improved performance.

A novel congestion mitigation key of power system via generator rescheduling using Jaya algorithm

ABSTRACT In the contemporary power system, congestion is a big hazard to system security. Congestion reduces the capability of the power system to work under secure limits of thermal and voltage profiles. Implementing FACTS devices or rescheduling the generator’s active power output are the most prevalent ways to mitigate congestion in the current scenario and hence this paper presents generator rescheduling for congestion management under the N-1 contingency condition. The generator sensitivity factor (GSF) is used to select the generators’ participation in the rescheduling process. The minimization of active power rescheduled by the selected generators is the key to success. JAYA algorithm is used first time in this work to reduce the congestion cost of the system. Furthermore, the JAYA algorithm is applied to a modified IEEE 30 bus system and the results obtained are validated by comparing them with other popular heuristic algorithms as available in the literature for diverse case studies.

Optimization design of brushless DC motor based on improved JAYA algorithm

Brushless direct current motor is widely used in industrial production because of its simple structure, wide speed range and low noise. To improve the operation efficiency of brushless DC motor and reduce the production and application costs, the optimization of brushless DC motor is analyzed by introducing the JAYA algorithm. This method determines the optimal parameters of a brushless DC motor using the theory of electromagnetic structure parameter selection and efficiency calculation. The population diversity of the JAYA algorithm is improved through an empirical learning strategy, and an adaptive strategy is introduced to balance the development ability and search performance of the algorithm. This ensures population diversity and improves convergence speed. The experiment showcases that the improved JAYA algorithm has a lower rank average in unimodal function operations, demonstrating stronger local development ability and better stability. It exhibits strong search ability in many local optima of multimodal functions. Moreover, the motor's average efficiency after optimization is 94.48%. The algorithm reaches the global optimum after approximately 40 iterations and offers faster convergence speed and higher accuracy. The adaptive JAYA algorithm is stable at around 93% when the number of iterations reaches 90, with a maximum efficiency of 95.3%. It is 5–12 percentage points higher than the other three comparison algorithms. The optimal solution of the motor parameters in the adaptive JAYA algorithm is closest to the theoretical parameter optimization value, meeting both the constraints of variables and the constraints of the model. The stator diameter, tooth magnetic induction, winding current density, air gap magnetic induction, and stator yoke magnetic induction values are 201.5 mm, 1.8 T, 2.049 A/mm2, 0.63 T, and 0.91 T, respectively. The research overcomes the problem of parameter optimization in the optimization design of brushless DC motor, improving their economic value of brushless DC motor in industrial production and application.

Optimal Feature Selection from High-dimensional Microarray Dataset Employing Hybrid IG-Jaya Model

Background: Feature selection (FS) is a crucial strategy for dimensionality reduction in data preprocessing since microarray data sets typically contain redundant and extraneous features that degrade the performance and complexity of classification models. Objective: The purpose of feature selection is to reduce the number of features from highdimensional cancer datasets and enhance classification accuracy. Methods: This research provides a wrapper-based hybrid model integrating information gain (IG) and Jaya algorithm (JA) for determining the optimum featured genes from high-dimensional microarray datasets. This paper's comprehensive study is divided into two segments: we employed the parameterless JA to identify the featured gene subsets in the first stage without filter methods. Various classifiers evaluate JA's performance, such as SVM, LDA, NB, and DT. In the second section, we introduce a hybrid IG-JA model. The IG is used as a filter to eliminate redundant and noisy features. The reduced feature subset is then given to the JA as a wrapper to improve the hybrid model's performance using the classifiers outlined above. Results: We used 13 benchmark microarray data sets from the public repository for experimental analysis. It is noteworthy to state that the hybrid IG-JA model performs better as compared to its counterparts. Conclusion: Tests and statistics show that the suggested model outperforms the standard feature selection method with JA and other existing models. Our proposed model is unable to provide the best accuracy compared to other existing approaches; however, it is quite steady and good. In the future, this work could be implemented with various filter methods and real-time data sets. A multi-filter approach with the Jaya algorithm will be used to check the efficiency of the proposed one. And it would be better to choose any other hybrid model (chaos-based) with Jaya to enhance the feature selection accuracy with a high dimensional dataset.

Optimized robust fuzzy sliding mode control for efficient wastewater treatment: a comprehensive study

Wastewater treatment plants (WWTPs) are plagued by nonlinearities, uncertainties, and disturbances that degrade control performance and may even lead to severe instability. The WWTP control issue has received a lot of research and development during the last several decades. One well-known way of designing a resilient control system is called sliding mode control (SMC). The SMC's greatest strength lies in its innate resistance to disturbances and uncertainty. Incorporating fuzzy SMC would eliminate the chattering effect, the primary drawback of traditional sliding-mode controller, without sacrificing robustness against parametric uncertainties, modeling errors, and variable dynamic loads. This article discusses the hybridization of fuzzy logic with sliding mode control to provide highly excellent stability and accuracy in a control system. As a means of optimizing the fuzzy SMC, the gradient-free optimization technique known as the Jaya algorithm is investigated. By repeatedly altering a population of individual solutions, this population-based method can deal with both limited and unbounded optimization issues.