Jaya Algorithm Research Articles

Optimum Design of Tuned Mass Dampers for Structures with Torsional Irregularity via Jaya Algorithm

Optimum Design of Tuned Mass Dampers for Structures with Torsional Irregularity via Jaya Algorithm

Optimizing of Discrete Time-cost Trade-off Problem in Construction Projects Using Advanced Jaya Algorithm

This paper proposes a novel algorithm to optimize the discrete time-cost trade-off problem (DTCTP) in construction projects. As DTCTP is assumed NP-hard, the new metaheuristic models are investigated to contribute for decision-making of project managers. DTCTP can be modelled as multi-mode to represent real-life problems more practical. According to the model, the project activities have at least two or more durations and cost alternative modes. For solving this problem effectively, a novel optimization metaheuristic method named Advanced Jaya Algorithm (A-JA) is proposed, which is generated from Jaya Algorithm (JA). The benchmark function tests are applied to verify the model with other well-known metaheuristic methods. The key weakness of the base algorithm JA is that, it has unstable solution accuracy and low likelihood of escaping local optimums. According to the results, A-JA considerably improves these areas. Two case studies of DTCTP are carried out after verification to demonstrate the effectiveness and efficiency of the proposed algorithm in comparison with JA and three well-known methods. The results of A-JA are found to be more powerful than the base algorithm JA and the benchmark algorithms. The proposed method achieves the Pareto fronts to help decision makers to make trade-off between the objectives and choose the optimum solution considering on their preference.

Enhanced ANN Predictive Model for Composite Pipes Subjected to Low-Velocity Impact Loads

This paper presents an enhanced artificial neural network (ANN) to predict the displacement in composite pipes impacted by a drop weight having different velocities. The impact response of fiber-reinforced polymer composite pipes depends on several factors including thickness, stacking sequence, and the number of layers. These factors were investigated in an earlier study using sensitivity analysis, and it was found that they had the most prominent effect on the impact resistance of the composite pipes. In this present study, composite pipes with a diameter of 54 mm are considered to explore the damages induced by low-velocity impact and the influence of these damages on their strength. To evaluate the effect of low-velocity, the pipes were exposed to impacts at different velocities of 1.5, 2, 2.5, and 3 m/s, and preliminary damage was initiated. Next, we used Jaya and E-Jaya algorithms to enhance the ANN algorithm for good training and prediction. The Jaya algorithm has a basic structure and needs only two requirements, namely, population size and terminal condition. Recently, Jaya algorithm has been widely utilized to solve various problems. Due to its single learning technique and limited population information, Jaya algorithm may quickly be trapped in local optima while addressing complicated optimization problems. For better prediction, an enhanced Jaya (E-Jaya) algorithm has been presented to enhance global searchability. In this study, ANN is enhanced based on the influential parameters using E-Jaya to test its effectiveness. The results showed the effectiveness of the E-Jaya algorithm for best training and prediction compared with the original algorithm.

Hybrid JAYA algorithm for workflow scheduling in cloud

Workflow scheduling and resource provisioning are two of the most critical issues in cloud computing. Developing an optimal workflow scheduling strategy in the heterogeneous cloud environment is extremely difficult due to its NP-complete nature. Various optimization algorithms have been used to schedule the workflow so that users can receive Quality of Service (QoS) from cloud service providers as well as service providers can achieve maximum gain but there is no such model that can simultaneously minimize execution time and cost while balancing the load among virtual machines in a heterogeneous environment using JAYA approach. In this article, we employed the hybrid JAYA algorithm to minimize the computation cost and completion time during workflow scheduling. We considered the heterogeneous cloud computing environment and made an effort to evenly distribute the load among the virtual machines. To achieve our goals, we used the Task Duplication Heterogeneous Earliest Finish Time (HEFT-TD) and Predict Earliest Finish Time (PEFT). The makespan is greatly shortened by HEFT-TD which is based on the Optimistic Cost Table. We used a greedy technique to distribute the workload among Virtual Machines (VMs) in a heterogeneous environment. Greedy approach assigns the upcoming task to a VM which have lowest load. In addition, we also considered performance variation, termination delay, and booting time of virtual machines to achieve our objectives in our proposed model. We used Montage, LIGO, Cybershake, and Epigenomics datasets to experimentally analyze the suggested model in order to validate the concept. Our meticulous experiments show that our hybrid approach outperforms other recent algorithms in minimizing the execution cost and makespan, such as the Cost Effective Genetic Algorithm (CEGA), Cost-effective Load-balanced Genetic Algorithm (CLGA), Cost effective Hybrid Genetic Algorithm (CHGA), and Artificial Bee Colony Algorithm (ABC).

A New Procedure for High Frequency Decentralized Modeling Parameters Identification of a Transformer Winding Construction Using Frequency Response Analysis Based on Logistic Chaotic JAYA Algorithm

A New Procedure for High Frequency Decentralized Modeling Parameters Identification of a Transformer Winding Construction Using Frequency Response Analysis Based on Logistic Chaotic JAYA Algorithm

Multi-objective optimization of composites sandwich containing multi-layer honeycomb considering load-bearing capacities and EM absorbing characteristics

This paper proposed a composite sandwich structure containing multilayer honeycombs, and a multi-objective optimization model containing its load-bearing and wave-absorbing properties, which were correlated with the geometric parameters of the honeycomb, was developed. The critical elastic buckling load was performed as an indicator to characterize the load-bearing capacity, and the bandwidth of the sandwich was employed as an indicator to characterize the wave-absorbing characteristics. This multi-objective optimization model was optimized based on JAYA algorithm and the results showed that the optimized structure had a significant improvement in both load-bearing and wave-absorbing capacities compared to the original structure.

Task Scheduling Optimization in Cloud Computing by Jaya Algorithm

Cloud computing provides resources to its consumers as a service. The cloud computing paradigm offers dynamic services by providing virtualized resources via the internet for enabling applications, and these services are provided by large-scale data centers known as clouds. Cloud computing is entirely reliant on the internet to provide its services to consumers. Cloud computing offers several advantages, including the fact that users only pay for what they use weekly, monthly, or yearly, that anybody with an internet connection may use the cloud, and that there is no need to purchase resources, hardware, or software on their own. This paper proposes an efficient task scheduling algorithm based on the Jaya algorithm for the cloud computing environment. We evaluate the performance of our method by applying it to three instances. The recommended technique produced the optimal solution in makespan, speedup, efficiency, and throughput, according to the findings.

Effective Utilization of Distributed Power Sources under Power Mismatch Conditions in Islanded Distribution Networks

The integration of distributed generation (DG) into a power distribution network allows the establishment of a microgrid (MG) system when the main grid experiences a malfunction or is undergoing maintenance. In this case, the power-generating capacity of distributed generators may be less than the load demand. This study presents a strategy for the effective utilization of deployed active and reactive power sources under power mismatch conditions in the islanded distribution networks. Initially, the DGs’ and capacitors’ optimal placement and capacity were identified using the Jaya algorithm (JA) with the aim to reduce power losses in the grid-connected mode. Later, the DG and capacitor combination’s optimal power factor was determined to withstand the islanded distribution network’s highest possible power demand in the event of a power mismatch. To assess the optimal value of the DG–capacitor pair’s operating power factor (pfsource) for the islanded operation, an analytical approach has been proposed that determines the best trade-off between power losses and the under-utilization of accessible generation. The test results on 33-bus and 69-bus IEEE distribution networks demonstrate that holding the islanded network’s load power factor (pfload) equal to pfsource during the power imbalance conditions allows the installed distributed sources to effectively operate at full capacity. As expected, the proposed strategy will assist the utility companies in designing efficient energy management or load shedding schemes to effectively cope with the power mismatch conditions.

Performance prediction of a textile reverse logistics system using DEA and ANFIS hybrid models

Due to the growing call to embrace environmentally responsible and sustainable business practices, textile reverse logistics (TRL) and recovery practices, such as reusing, remanufacturing, or recycling, are gaining prominence. Textile recycling companies can simultaneously obtain economic and environmental benefits via more efficient RL practices. However, a system for measuring these efficiencies is paramount, as it is impossible to run a reverse logistics system efficiently without the ability to measure its performance. Studies on performance measurement of TRL firms are completely lacking, and those of the general RL literature use manual systems that require longer time and participation of many workers to complete. In this study, we develop a performance prediction model based on DEA and ANFIS. Data for the ANFIS were derived from the DEA computation. To enhance the model, PSO, GA, and Jaya algorithms were introduced to tweak the ANFIS parameters. Results from the ANFIS hybrid models reveal ANFIS-Jaya to have a better prediction accuracy with R2 of 0.9832 and 0.9851 in training and testing datasets, respectively. This study contributes to the RL performance management literature and the limited research on used clothing collection, textile recycling, and RL performance management measurement.

Optimal power flow-based approach for grid dispatch problems through Rao algorithms

In order to fulfill the load demand and operational restrictions, the grid dispatch problem's optimization goal is to reduce fuel cost through variation of active and reactive power of the generators within fixed limits and to uplift the security of the power plants during voltage instability through proper allocation of static voltage compensator to ensure minimum power loss. This paper proposes a collaborative optimization of economic load dispatch and reactive power dispatch using three metaphor-less algorithms called Rao algorithms, where the current solution will interact with the finest and inferior solutions in the population, as well as a randomly selected solution. These strategies are free from any specific control parameters. These algorithms are formulated on the IEEE 30 bus system. The outcomes of the suggested techniques are compared with those of existing optimization approaches such as Bat algorithm (BOA), Modified Harmonic search algorithm (MSG-HS), Root tree optimization (RTO), Adaptive Gravitational search optimization (AGSO), Whale optimization algorithm (WOA), Fruit fly optimization (MOFOA), Crow search algorithm (CSA), Quasi-Oppositional Teaching learning based optimization (QOTLBO), and Jaya algorithm (JA) which demonstrates that the proposed strategies have great convergence characteristics and are the most systematic way to addressing these complicated power system issues. They are unable to converge on a local optimal solution prematurely while maintaining a high level of precision.

Optimization of reinforced concrete frame structures and matrix displacement method

In this study, reinforced concrete frame system is generated, and all structural elements which are beam and columns are optimized according to the applied distributed loads and different concrete classes by using Matlab program. Jaya algorithm which is a Metaheuristic Algorithm that enables to optimization process and finds the best cross sections, reinforcement area as well as cost of the system, is proposed. It is observed that cross-section, reinforced area as well as cost of the system are changed when concrete classes are used differently. After finding the optimum design values for frame system, the matrix displacement method is utilized to specify the system displacements and all nodes forces. Furthermore, columns and beam displacement results are not similar, and also internal forces are different for nodes. TS500 (2000) (Reinforced concrete structures design and construction rules) and TBDY (2018) (Turkey Building Earthquake Regulation) are used together to specify variables, constraints and also necessity values. The proposed method is feasible for frame structures consisting of different members.

Micromachining of Al7075 alloy using an in-situ ultrasonicated µ-ECDM system

ABSTRACT Al7075 is a lightweight metal alloy essentially used in various engineering sectors possessing applications in aerospace, military, missile, etc. Miniaturized machining operations have placed a great deal of pressure on the conventional machining capabilities of Al7075 alloys as they possess certain challenges due to their ductile and unique adhesive nature, which must be overcome. The present study focuses on generating through-holes on Al7075 alloy using an electrolyte ultrasonication-assisted µ-ECDM system. The FCC-RSM factorial-based design is chosen at three levels to carry out experimentation with process characteristics voltage (V), concentration (wt%), and duty factor (%DF). The material removal rate (MRR), top hole overcut (TOC), bottom hole overcut (BOC), and circularity (CIR) are the machining responses. JAYA algorithm, a multi-objective optimization is performed, and optimal process parameters are obtained using the R method. Further, RSM based desirability approach is also used to obtain optimal process parameters and compared them with results obtained from R-method and found to be relatively close.

Optimal design and control loop selection for a STATCOM wide-area damping controller considering communication time delays

Wide-area damping controllers (WADC) have been shown to be effective in damping interarea oscillation modes. The varying nature of power system operating point requires the design of an adaptive WADC. Majority of the existing control design techniques rely on empirical approaches at one or more of their design stages, which makes it difficult to implement in adaptive control techniques. Therefore, the purpose of this study is to provide a systematic procedure based entirely on numerical approaches for designing a STATCOM-based WADC. The proposed procedure involving control loop selection, control design and adaptive time delay compensation, can then be implemented in adaptive WADC schemes. Geometric analysis has been used to choose the STATCOM location. Then, a novel optimization-based signal selection methodology is proposed. The proposed methodology employs a combination of multiple input signals to optimally generate a signal with the required modal objectives like enhancing observability and reducing interaction between different control loops. After that, another optimization problem is formulated to tune the controller parameters using integral of time multiplied by absolute error (ITAE) as the optimization objective. Both optimization problems have been evaluated using two optimization techniques: Particle swarm optimization (PSO) and self-adaptive multi-population elitist Jaya algorithm (SAMPE-JAYA). Finally, an adaptive delay compensator is designed to mitigate the effects of communication time delays. The proposed control strategy is applied on the IEEE 68-bus system and the controller performance is evaluated by modal analysis and multiple time-domain simulation scenarios.

Design of optimal PI controller for torque ripple minimization of SVPWM-DTC of BLDC motor

Conventional direct torque control (CDTC) for brushless DC motor (BLDCM) using proportional integral (PI) controller suffers from torque ripple minimization and speed regulation issues. A novel method in fusion with space vector pulse width modulation (SVPWM) with DTC using optimal PI controller is developed to minimize these issues. In this method, SVPWM replaces switching table and hysteresis controllers in CDTC. To get better performance in steady state, conventional PI controllers are preferred in SVPWM-DTC for BLDCM. However, uncertainty arises due to PI controller tuning as well as load variations. In such a case, optimal PI controller tuned properly can minimize these uncertainties. Here, JAYA algorithm is used to tune controller gain parameters. Simulations of proposed PI controller of SVPWM-DTC for BLDCM are carried away in Simulink. To appreciate the performance of proposed optimal controller of SVPWM-DTC for BLDCM, the simulation results are compared with Conventional PI controller and particle swarm optimization (PSO) technique based tuned PI controller. This proposed technique reduces the toque ripple by 63.1% when compared to conventional PI and 58.5% when compared to PSO-PI controller. It also improves the settling time by 43.9% when compared to conventional PI and 46.79% when compared to PSO-PI controller.

Optimal placement and sizing of custom power devices using APSO and JAYA optimization in radial distribution network

With the recent advancement in the field of power electronics devices and an increasing number of nonlinear loads in the power system, the maintenance of good power quality at the consumers’ end is one of the major concerns of today’s power distribution systems. The introduction of a large number of power electronics devices in the system introduces power quality issues like voltage flicker, voltage distortions, harmonic distortions, voltage sag-swell, etc. Due to this, the maintenance of good power quality at the consumer end becomes a major challenge for distribution companies. The Custom power devices (CPD) have been used in the power system to address these issues and improve power quality. However, optimal placement and sizing of CPD is a challenge itself. In this paper, the optimal location and rating of CPD (STATCOM and APLC) have been determined using the APSO and JAYA optimization techniques,both of these techniques are known for their robustness. The main objective of the paper is to minimize the total harmonic distortion, total CPD size, harmonic transmission line loss, telephone influence factor, and motor load loss. Further, the performances of allocated CPD in controlling the voltage quality and reactive power are evaluated. The performanced from the APSO and JAYA algorithm on an IEEE-16 bus and 69 bus distribution system has been compared to each other.

Meta Heuristic Algorithm Based Multi Objective Optimal Planning of Rapid Charging Stations and Distribution Generators in a Distribution System Coupled with Transportation Network

The application of Electric Vehicles (EVs) is increasing in many countries, causing many researchers to focus on EV Rapid Charging Station (RCS) related issues. The optimal planning of RCS considering only distribution networks is not a reliable approach. Moreover, the RCS location should be convenient to the EV user in a given EV driving range and the performance of the distribution system. In this paper, a multi-objective approach for optimal planning of RCS and Distributed Generators (DG) in a distributed system coupled with a transportation network is analyzed. The proposed optimal planning method aims to achieve reduced active power loss, EV user costs, and voltage deviation for effective RCS and DG planning. The approach includes the analysis of the test system with the base case, solo planning of RCS, planning of DGs with fixed RCS, and simultaneous optimal planning of RCS and DGs. Daily load variation at buses and hourly charging probability of EVs have been used in the analysis. IEEE 33 bus distribution system superimposed with a 25-node transportation network is considered the test system. Rao 3 algorithm is applied for optimization, and the results have been compared with PSO and JAYA algorithms.

An energy-efficient topology-aware virtual machine placement in Cloud Datacenters: A multi-objective discrete JAYA optimization

By the advent of cloud computing and the numerous related web applications, data center networks (DCNs) are becoming complex to provide all-to-all communications between underlying devices; it is done by spending a huge amount of electricity consumption. Energy consumption management is the first class concern for cloud providers in this energy hungry devices and also for the green computing goals. In the large DCs, the power management of hundreds or even many thousands idle switches can be a promising approach toward overall cost reduction. The virtual machine placement (VMP) scheme which is aware of both VMs affinity and underlying network topology for co-hosting dependent VMs as physically near as possible and lowering down power state of idle devices can enhance sustainability objectives. On the other hand, resource wastage lowers system utilization which leads more physical server usage causing more power consumption as a consequence. To address the issue, this paper presents an energy-efficient topology-aware VM placement scheme in the cloud DCs which is formulated to a multi-objective optimization problem with power consumption and resource wastage minimization perspective. To deal with this combinatorial problem, an advanced multi-objective discrete version of JAYA (MOD-JAYA) algorithm is presented since the search space of VMP is discrete in nature. The proposed algorithm is validated by intensively variable circumstances and simulations upon conducted scenarios. The simulation results prove the superiority of the proposed advanced MOD-JAYA algorithm in solving VMP problem in comparison with other existing schemes in terms of prominent assessment metrics.

Novel Convolutional Neural Networks based Jaya algorithm Approach for Accurate Deepfake Video Detection

Deepfake videos are becoming an increasing concern due to their potential to spread misinformation and cause harm. In this paper, we propose a novel approach for accurately detecting deepfake videos using the combination of Convolutional Neural Networks (CNNs) with the Jaya algorithm optimization. The approach is evaluated on two publicly available datasets, the DeepFake Detection Challenge (DFDC) dataset and the Celeb-DF dataset, and achieves state-of-the-art performance on both datasets. Our approach achieves an accuracy of 99.3% on the DFDC dataset and 97.6% on the Celeb-DF dataset, with high F1 scores indicating a high precision and recall for detecting deepfake videos. Furthermore, our approach is more robust against adversarial attacks than existing state-of-the-art methods. The combination of CNNs with the Jaya algorithm optimization enables effective capture of the temporal information in the video sequence, while the use of robust evaluation metrics ensures objective measurement and comparison with existing methods. Our proposed approach offers a highly effective solution for detecting deepfake videos, which has the potential to be a valuable tool for media forensics, content moderation, and cyber security.

INVESTIGATION ON THE ELECTRICAL DISCHARGE MACHINING OF Cu-SHAPE MEMORY ALLOY: A STUDY ON MACHINABILITY AND SURFACE TOPOGRAPHY ASPECTS

Shape memory alloys (SMAs) are an excellent material for producing components for a wide range of industrial applications, such as orthopedic implacers, micro-equipment, actuators, fittings, and screening components, as well as military equipment, aerospace components, bio-medical equipment, and fabrication requirements. Despite its remarkable qualities, the production of SMAs is a problem for investigators all over the globe. The purpose of this research is to evaluate the effects of altering the [Formula: see text], [Formula: see text], [Formula: see text], and GV while processing copper-based SMA in an electrical discharge machining process on the material removal rate (MRR) and surface roughness (SR). The major runs were designed using a central composite design. SEM was also utilized to examine the micro-structure of EDM-processed electrode tools and work samples. SEM scans indicated the presence of debris, micro-cracks, craters, and a newly formed recast layer on the electrode tool and workpiece surface. High [Formula: see text] and prolonged [Formula: see text] provide huge spark energy simply at the work sample-tool contact, resulting in debris production. The experimental results reveal that the least and highest MRR values are 10.333 and 185.067[Formula: see text]mm3/min, respectively, while the minimum and maximum SR values are 3.07 and 7.15[Formula: see text][Formula: see text]m. The desirability technique, teacher learning based optimization (TLBO), and the Jaya algorithm were also utilized to optimize the studied solutions (i.e. MRR and SR) on a single and multi-objective basis. The best MRR and SR were determined using the desirability approach, the Jaya Algorithm, and the TLBO to be 152.788[Formula: see text]mm3/min and 4.764[Formula: see text][Formula: see text]m; 240.0256[Formula: see text]mm3/min and 1.637[Formula: see text][Formula: see text]m; and 240.0257[Formula: see text]mm3/min and 1.6367[Formula: see text][Formula: see text]m.

Optimal Power Flow Analysis for Power Loss Reduction using Jaya Algorithm

This paper is engrossed in the usage of metaheuristic optimization methods named the JAYA algorithm and the teacher learning based optimization (TLBO) algorithm, which deal with the objective function of the optimal power flow (OPF) problem. TLBO and Jaya are parameter-free algorithms, which reduce the complexity of algorithms. The objective function of this paper is to reduce the real power losses and maintain the voltages and tap positions within their limits. IEEE-39 bus system data is considered for the practice of the algorithms. The results obtained by using the JAYA algorithm show better progress in objective function reduction. This entire work is done in a MATLAB environment.