Jaya Algorithm Research Articles

A JAYA algorithm based on normal clouds for DNA sequence optimization

A JAYA algorithm based on normal clouds for DNA sequence optimization

Jaya-AEO: An Innovative Hybrid Optimizer for Reactive Power Dispatch Optimization in Power Systems

In the domain of electric power systems, ensuring stable voltage and efficient transmission is crucial. Optimal reactive power dispatch (ORPD) is a vital step toward improving system stability, enhancing economic feasibility, and increasing overall efficiency. Various optimization techniques are employed to minimize real power loss and voltage deviation in the network. This article proposes a hybrid algorithm called Enhanced Jaya and Artificial Ecosystem-Based Optimization (EJAEO) to address the ORPD problem. The algorithm aims to determine the optimal values of variables such as reactive compensation, generators’ voltages, and transformers’ tape ratio. Two single objective functions are tested on standard IEEE 30-Bus and IEEE 57-Bus systems, and the suggested modification of the Artificial Ecosystem-Based Optimization (AEO) improves population effectiveness in achieving optimal solutions. The simulation findings of the EJAEO algorithm demonstrated its superiority over three recent algorithms (AEO), Turbulent Flow of Water-Based Optimization, and Jaya algorithm), as well as certain previously published ORPD methods used for the same problem. In the case of the IEEE 30-Bus system, the proposed EJAEO algorithm achieved the best results in terms of power losses (4.944805) and voltage deviation (0.121196). Similarly, in the IEEE 57-Bus system, our EJAEO algorithm outperformed others, yielding the lowest power losses (23.33052) and voltage deviation (0.579286). The EJAEO algorithm outperforms other algorithms in terms of accuracy, speed of convergence, and robustness. These findings highlight the potential of EJAEO as a promising technique for solving the ORPD problem in power systems.

A Backstepping Control Strategy for Power System Stability Enhancement

Secure power system operation relies extensively on the analysis of transient stability and control. The dynamics involved in power system control are often complex and nonlinear. Most of the currently existing works approach these frequent problems with nonlinear control techniques, leading to a requirement for specific controller parameter adjustments. In these veins, this paper proposes a new method for stabilizing electric power systems, using nonlinear backstepping control by optimizing the controller’s parameters. The Jaya algorithm and Genetic algorithm are utilized as a powerful meta-heuristic optimization technique to search parameters of an optimal controller. Improvement in system damping, transient stability, and voltage regulation has been achieved by minimizing the integral time absolute error (ITAE) as the objective function. Numerical simulations on an SMIB power system under different fault conditions showed that the proposed method outperforms classical power system stabilizer (PSS) methods, reducing overshoots and settling times and eliminating steady-state errors. These findings highlight the effectiveness of the proposed approach and its potential contribution to the development of advanced nonlinear control techniques for electric power systems. The suggested optimization methods demonstrate superior performance, compared to classical methods, and achieve a reduction of 27.5% in overshoot and 87% in transient time in addition to complete elimination of static error.

Least Mean Mixed Norm Square/Fourth Adaptive Algorithm With Optimized FOPID Gains for Voltage Power Quality Mitigation

The conventional adaptive Least Mean Square (LMS) algorithm employs Mean Square Error (MSE) as a cost function based on second-order power error. This limits the extraction of fundamental signals from the noise and suffers from performance degradation in certain dynamic scenarios. The proposed approach addresses this problem with a mixed norm-based step size adaptive algorithm which is devised from the second and fourth-order error optimization. The proposed Mixed-Norm Constraint-based Improved Proportionate Normalized Least Mean Square Fourth (MNC-IPNLMS/F) control strategy extracts the fundamental weight quantity from the polluted grid voltage and generates the reference load voltage. This method exploits the enhancement of Dynamic Voltage Restorer (DVR) performance in terms of convergence and stability. The DC and AC-link voltage is regulated by Fractional Order Proportional Integral Derivative (FOPID) controller. The coefficients of FOPID are self-tuned by several optimizations namely, JAYA algorithm, Accelerated Particle Swarm Optimization (APSO), and Biogeography Based Optimization (BBO). This reduces manual tuning and computational complexity. The major improvements of the proposed approach are less overshoot (6.6%), undershoot (3.3%), and fast settling time (0.14s). The comparative study reveals that the FOPID control based on JAYA Optimization (JO) outperforms the others. The efficacy of the MNC-IPNLMS/F is investigated through the performance results.

Solving Biobjective Distributed Flow-Shop Scheduling Problems With Lot-Streaming Using an Improved Jaya Algorithm.

A distributed flow-shop scheduling problem with lot-streaming that considers completion time and total energy consumption is addressed. It requires to optimally assign jobs to multiple distributed factories and, at the same time, sequence them. A biobjective mathematic model is first developed to describe the considered problem. Then, an improved Jaya algorithm is proposed to solve it. The Nawaz-Enscore-Ham (NEH) initializing rule, a job-factory assignment strategy, the improved strategies for makespan and energy efficiency are designed based on the problem's characteristic to improve the Jaya's performance. Finally, experiments are carried out on 120 instances of 12 scales. The performance of the improved strategies is verified. Comparisons and discussions show that the Jaya algorithm improved by the designed strategies is highly competitive for solving the considered problem with makespan and total energy consumption criteria.

A novel hybrid algorithm based optimal planning of solar PV and battery energy storage systems

The adoption of renewable energy sources (RES) such as solar photovoltaic distributed generation (SPVDG) has been at its peak in the present decade owing to their positive impact on the environment and the grid. However, these sources’ ratings and placement need to be optimally estimated before their accommodation in the power distribution system (PDS) to prevent increased power loss and voltage profile aberrations. In this article, SPVDG is integrated with battery energy storage systems (BESS) to compensate for the shortcomings of SPVDG, such as intermittency and uncertainty, and to reduce peak demand. This paper presented a novel hybrid algorithm, which is a combination of the enhanced elephant herding algorithm and the Jaya algorithm. This developed algorithm properly controls the searching algorithm from global exploration to local exploitation to obtain the near-global optimum solution. The problem formulation is centered on the optimal accommodation of SPVDG and BESS to alleviate the power loss and improve the voltage profile of the PDS. Further, the voltage limits, maximum current limits, and BESS charge–discharge constraints are validated throughout the optimization process. Moreover, the hourly variation of SPVDG generation and demand profile with seasonal impact is examined in this study. IEEE 69 bus PDS is tested for the development of the presented work. The suggested algorithm demonstrated its effectiveness and accuracy when compared to different optimization approaches in the literature.

An Improved Dynamic Optimization Control System for the Drilling Rate of Penetration (ROP) and Its Industrial Application

Optimization control of the rate of penetration (ROP) is crucial in the drilling process due to its vital role in improving the drilling efficiency and safety. In this paper, an improved dynamic optimization control system for ROP is proposed and successfully applied to a drilling site. First, a three layers (intelligent optimization layer, basic automation layer, and process monitoring layer) system framework is proposed according to the drilling characteristics. After that, “If-Then” strategy is used as the method to identify the rotary drilling condition from four different working conditions. Moving-window strategy is selected to establish the dynamic ROP model that can better adapt to environmental changes. Moreover, Jaya algorithm, a new meta-heuristic optimization method is introduced to solve the dynamic ROP optimization issue. In the simulations, the optimization performance of the proposed system is better than moving window-extreme learning machine-hybrid bat algorithm with more stable (fewer switch times) drilling operational parameters. Finally, in an industrial application to a drilling process in Dandong area, Northeast China, the ROP was improved by 20.79% compared with manual operation. Both simulation and industrial application results indicated that the proposed system has significantly improved the drilling efficiency and safety.

A Multilevel Image Thresholding Based on Hybrid Jaya Algorithm and Simulated Annealing

A Multilevel Image Thresholding Based on Hybrid Jaya Algorithm and Simulated Annealing

Multi-area automatic generation control of a renewable energy-based hybrid power systems using JAYA optimized model predictive control

This study uses JAYA optimized model predictive control to address a three-area load frequency regulation problem that actively involves stochastic renewable energy sources. Some physical constraints employed in the system model, such as the generation rate constraint, dead band, and time delay by governor-turbine, negatively impact the control performance of the classical MPC technique. As such, a more robust control scheme is required to get rid of this flaw. The JAYA algorithm is adopted to dynamically tune the parameters of MPC, which is a parameter-driven controller for optimal system performance. The performance indices such as the integral of square error (ISE), integral of time multiplied by square error (ITSE), integral of the absolute value of error (IAE), and integral of time multiplied by the absolute value of error (ITAE) have been used to compare the effectiveness of the proposed controller with the classical MPC with constant tuning parameters. The suggested controller is superior to the conventional MPC, as demonstrated by the results from the MATLAB/Simulink environment.

A new methodology to predict the sequence of GFRP layers using machine learning and JAYA algorithm

In this paper, the best stacking sequence using experimental tests of GFRP composites is investigated. The main objective of this work is to determine the main specification of GFRP composite material, which is represented by its physics-mechanical properties, weight, and cost, before performing a series of experimental tests based on various stacking sequences. Our methodology is divided into three stages. The first stage is characterized by extracting the bending data from mechanical tests of some GFRP composites. In the second stage, the validated numerical model is used to simulate numerous cases of stacking sequences. In the last stage, the extracted data is used to determine the parameters for different stacking sequences using an inverse technique based on ANN and JAYA algorithm. The results provide a good prediction of parameters as well as a good orientation to make decisions on the best GFRP stacking sequence to be used, according to the required specifications of the manufacturer. The experemental data analysis can be found at https://github.com/Samir-Khatir/Sequence-GFRP

A Novel Many-Objective Sine–Cosine Algorithm (MaOSCA) for Engineering Applications

In recent times, numerous innovative and specialized algorithms have emerged to tackle two and three multi-objective types of problems. However, their effectiveness on many-objective challenges remains uncertain. This paper introduces a new Many-objective Sine–Cosine Algorithm (MaOSCA), which employs a reference point mechanism and information feedback principle to achieve efficient, effective, productive, and robust performance. The MaOSCA algorithm’s capabilities are enhanced by incorporating multiple features that balance exploration and exploitation, direct the search towards promising areas, and prevent search stagnation. The MaOSCA’s performance is evaluated against popular algorithms such as the Non-dominated sorting genetic algorithm-III (NSGA-III), the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) integrated with Differential Evolution (MOEADDE), the Many-objective Particle Swarm Optimizer (MaOPSO), and the Many-objective JAYA Algorithm (MaOJAYA) across various test suites, including DTLZ1-DTLZ7 with 5, 9, and 15 objectives and car cab design, water resources management, car side impact, marine design, and 10-bar truss engineering design problems. The performance evaluation is carried out using various performance metrics. The MaOSCA demonstrates its ability to achieve well-converged and diversified solutions for most problems. The success of the MaOSCA can be attributed to the multiple features of the SCA optimizer integrated into the algorithm.

Optimal coordinated energy management strategy for standalone solar photovoltaic system with hybrid energy storage

Energy storage devices and renewable resources, especially rooftop photovoltaic (PV), are vital to the operation of standalone systems. In this study, an energy management strategy (EMS) for battery energy storage systems (BESS), PV, and supercapacitors (SC) is presented. The proposed control strategy is designed to optimize the BESS flow rate, discharge, and charge cycles of the energy system using the Meta-heuristic Jaya algorithm by properly coordinating SC and PV. SC is employed in HESS to fulfill the transient energy mismatches and reduce the transitory high charge/discharge current rate impacts on BESS using EMS. The proposed controller also has the benefit of keeping the battery's SoC within limitations for a long period. To extend the life of BESS, the EMS is aimed at minimizing deep charging and discharging by reducing PV generation, particularly under light load (no-load) situations and when battery and SC's SoC≥SoCmax or charging. Similarly, the optional load shedding is employed when battery and SC's SoC≤SoCmin to avoid deep discharge. The proposed EMS is validated in both simulation environment and hardware-prototype under different perturbations and operating conditions. The charge and discharge rate performance of BESS and SC under different charge and discharge time durations, i.e., 1 s, 2 s, and 3 s, were evaluated in both simulation and hardware, and compared to conventional charge and discharge methods for HESS, whose charge and discharge time durations are nearly 0.3 s to 0.4 s. As a result, battery stress is significantly reduced. Furthermore, the economic analysis of the complete system is carried out. In this, it is observed that standalone PV and HESS systems with the proposed EMS are cost-effective and economical compared with PV and BESS systems when operated for a long horizon beyond 6 years.

A decomposition-based multi-objective Jaya algorithm for lot-streaming job shop scheduling with variable sublots and intermingling setting

This study examined the lot-streaming job shop scheduling problem (LSJSP) with variable sublots and intermingling setting, which has rarely been considered in the literature and is beneficial to real industry settings. Upon variable sublots and intermingling setting, a multi-objective mixed-integer linear programming model (MILP) is formulated to achieve a tradeoff between the shortest tardiness and the minimum number of transferred sublots. Considering the NP-hard characteristic of the problem, the MILP model would get restricted in solving medium and large scale instances. Thus building an efficient multi-objective optimization algorithm is desirable. Motivated by the Jaya algorithm and decomposition idea, we propose a new algorithm framework called a decomposition based multi-objective Jaya algorithm (MOJA/D) to achieve a balance between exploration and exploitation. This MOJA/D framework can provide some guidance in algorithm construction for other multi-objective optimization problems. Following the MOJA/D framework, a specific MOJA/D metaheuristic is further designed for coping with the proposed problem. First, a novel forward and backward decoding strategy is designed to implement coevolution among different sub-problems and enhance the population diversity. Second, the Jaya updating mechanism is designed based on problem knowledge to guide the solutions moving towards the best solution and away from the worst solution. Moreover, considering the tradeoff between two conflicting objectives, the problem-specific local search strategies are designed and integrated with the neighborhood based local search strategies to enhance exploitation in the search process. The influence of parameter setting on MOJA/D is investigated by using design of experiments. The statistical results from extensive computational experiments demonstrate the effectiveness of the specific algorithm designs and show that the MOJA/D metaheuristic is superior to the existing algorithms in solving the proposed problem.

Hybridized Dragonfly and Jaya algorithm for optimal sensor node location identification in mobile wireless sensor networks

Hybridized Dragonfly and Jaya algorithm for optimal sensor node location identification in mobile wireless sensor networks

An Efficient Fault Diagnosis Framework for Digital Twins Using Optimized Machine Learning Models in Smart Industrial Control Systems

In recent times, digital twins (DT) is becoming an emerging and key technology for smart industrial control systems and Industrial Internet of things (IIoT) applications. The DT presently supports a significant tool that can generate a huge dataset for fault prediction and diagnosis in a real-time scenario for critical industrial applications with the support of powerful artificial intelligence (AI). The physical assets of DT can produce system performance data that is close to reality, which delivers remarkable opportunities for machine fault diagnosis for effective measured fault conditions. Therefore, this study presents an intelligent and efficient AI-based fault diagnosis framework using new hybrid optimization and machine learning models for industrial DT systems, namely, the triplex pump model and transmission system. The proposed hybrid framework utilizes a combination of optimization techniques (OT) such as the flower pollination algorithm (FPA), particle swarm algorithm (PSO), Harris hawk optimization (HHO), Jaya algorithm (JA), gray wolf optimizer (GWO), and Salp swarm algorithm (SSA), and machine learning (ML) such as K-nearest neighbors (KNN), decision tree (CART), and random forest (RF). The proposed hybrid OT–ML framework is validated using two different simulated datasets which are generated from both the mechanized triplex pump and transmission system models, respectively. From the experimental results, the hybrid FPA–CART and FPA–RF models within the proposed framework give acceptable results in detecting the most relevant subset of features from the two employed datasets while maintaining fault detection accuracy rates exemplified by the original set of features with 96.8% and 85.7%, respectively. Therefore, the results achieve good and acceptable performance compared to the other existing models for fault diagnosis in real time based on critical IIoT fields.

Comparison of different regulations and metaheuristic algorithms in beam design

In this first study, the rectangular reinforced concrete beam’s costs and cross-section sizes are found by using Harmony Search (HS), Differential Evolution Algorithm (DE), Jaya Algorithm, Teaching- Learning Based Algorithm (TLBO), Hybrid algorithm (Jaya-TLBO) and Flower Pollination Algorithm (FPA) separately by using ACI 318 building code. In addition, in order to better see how successful the algorithms are, the standard deviation of the algorithms used in the project in a certain number of iterations, price changes and in which iteration the minimum cost is compared. As a result of running different algorithms 5, 10, 15 and 20 times, separate values are recorded, and the average number of iterations of the algorithms for each is shown by finding the standard deviation values. Furthermore, Hybrid Algorithm reached the objective function in fewer iterations and their standard deviations reached 0 earlier. In the second study, the beam design is made according to the ACI 318, TS500 and Eurocode 2 regulations under certain loads by using a Hybrid Algorithm with different concrete classes. Optimization of this design is made using the Matlab program, and comparisons are made between regulations. Eurocode and TS500 design costs are roughly the same; however, ACI 318’s design is the cheapest.

A comparative analysis of SAMP-Jaya and simple Jaya algorithms for PFSSP (permutation flow shop scheduling problems)

A comparative analysis of SAMP-Jaya and simple Jaya algorithms for PFSSP (permutation flow shop scheduling problems)

A global-local hybrid strategy with adaptive space reduction search method for structural health monitoring

The difficulty in computational convergence poses challenges of application for traditional heuristic optimization algorithms to solve the optimization-based structural identification problem, especially for the large-scale and complex structural systems where considerable number of unknown parameters and degrees of freedom involved. Unlike the classic identification methods, in this paper, a novel hybrid strategy, coarsely exploring the relatively large search limits with the improved Jaya algorithm and adaptive search space reduction method in the global stage, and then fine-tuning the identified best solution with local optimization methods to the optimum in the local stage, is proposed and evaluated. The improved Jaya algorithm includes three improvements compared to its original version, fuzzy clustering competitive learning, experience learning and Cauchy mutation mechanisms. Gradient based Levenberg-Marquardt method, sequential quadratic programming method and non-gradient based Nelder-Mead simplex method are inserted as local mathematical optimizers to further enhance identification accuracy and efficiency. The superiority of proposed improved Jaya algorithm is validated in optimizing classical and CEC05 benchmark functions by comparing with several state-of-the-art algorithms. Furthermore, the effectiveness of proposed global-local hybrid method is verified by a numerical example of truss structure and an experimental test of the steel grid benchmark structure with incomplete set of noise-polluted measurements. The statistical results show that the improved Jaya algorithm and adaptive search space reduction method combined with sequential quadratic programming can achieve better performance in structural damage identification than other methods.

Multiple Thermal Parameter Inversion for Concrete Dams Using an Integrated Surrogate Model

An efficient and accurate method for concrete thermal parameter inversion is essential to guarantee the reliable and prompt thermal analysis results of dams. Traditional inversion methods either suffer from low analysis efficiency or are limited in accuracy. Thus, this paper presents a method for multiple thermal parameter inversion based on an integrated surrogate model (ISM) and the Jaya algorithm. This method replaces finite element analysis with an ISM incorporating three machine learning algorithms, Kriging, support vector regression (SVR), and radial basis function (RBF), to describe the mapping relationship between thermal parameters and structure temperature responses. The input datasets for model training and testing are generated by a uniform design approach. Subsequently, a simple and efficient global optimization algorithm, Jaya, is used to identify the thermal parameters by minimizing the error between calculated and monitored temperatures. The effectiveness and practicality of this method are verified by applying monitored data of two strength grades of concrete in a dam. The verification results indicate that the proposed approach can obtain more accurate inversion results than the above individual models. Compared with these models, the inversion errors using ISM are reduced by 8.45%, 3.93% and 20.85%, respectively for C35 concrete, and by 6.53%, 23.82% and 44.43%, respectively for C40 concrete. Additionally, this approach maintains the powerful computational efficiency of surrogate-based optimization, and compared to the methods that directly invert using swarm intelligence algorithms, the analysis efficiency is improved by about 111.7 times.

Parallel Global Best-Worst Particle Swarm Optimization Algorithm for solving optimization problems

The range of applications of swarm optimization algorithms is very vast. However, high dimensions and more number of decision variables make these optimization problems more complex. Particle Swarm Optimization (PSO) is the most popular optimizer for performing such types of optimization. PSO is motivated from the movement and intelligence of swarms. However, the primary constraint with the PSO and other swarm algorithms is enormous computational time (CT) due to more number of decision variables in complex problem. The number of steps inside Swarm Intelligence Algorithms (SIAs) also increase the complexity of computation in the process of optimization. Many iterations of the procedure of SIA need more CT since these algorithms are iterative in nature. In this study, a new Global Best-Worst Particle Swarm Optimization (GBWPSO) algorithm has been proposed so as to provide a fully version of parallel algorithm. GBWPSO algorithm is the combination of PSO and Jaya algorithm that provides a refined version of parallel algorithm having more parallelism. The proposed algorithm is executed on three different computational hardware with various combinations of population size and maximum number of iteration on five different standard benchmark functions. The evaluation is done on the basis of performance metrics such as speedup (S), real speedup (RS), maximum speedup (MS), efficiency (E), and scalability. The proposed parallel algorithm (P-GBWPSO) outperforms both parallel version of PSO and Jaya algorithm in terms of less CT and better optimal solution. Based on the results, we found that system 03 (S3) is best on proposed GBWPSO algorithm with an efficiency of 1.2518 compare with system 01 (S1) and system 02 (S2).