Modified Jaya Algorithm Research Articles

Optimal Economic Dispatch of Thermal-Wind Power Systems with Valve-Point Effects using A Modified JAYA Algorithm

In this paper, a modified JAYA algorithm isapplied to solve the optimization problem of the economic load dispatch (ED). The valve-point effects as well as thesystem power losses are taken into account in the solution.The optimization problem is formulated to include windpower within the generation system. The consideration ofwind power is motivated by the globally increasing interestin the renewable energy sources in light of theenvironmental aspects. The JAYA algorithm is a recentlyintroduced optimization technique which has beenemployed to solve small-scaled optimization problems.Nevertheless, the algorithm’s convergence behaviour hasnot shown satisfying characteristics for large scaleconstrained optimization problems. To overcome thedrawbacks associated with the complex nonlinearities andhigh dimensionalities, some enhancements to thealgorithm’s features are embedded. The proposed algorithmis validated using some well-known test systems. Acomparison of the results with other algorithms is carriedout considering the effects of valve point as well as power losses.

Reliable renewable energy forecasting for climate change mitigation

Accurate prediction of electricity generation from diverse renewable energy sources (RES) plays a pivotal role in optimizing power schedules within RES, contributing to the collective effort to combat climate change. While prior research often focused on individual energy sources in isolation, neglecting intricate interactions among multiple sources, this limitation frequently leads to inaccurate estimations of total power generation. In this study, we introduce a hybrid architecture designed to address these challenges, incorporating advanced artificial intelligence (AI) techniques. The hybrid model seamlessly integrates a gated recurrent unit (GRU) and a ResNext model, and it is tuned with the modified jaya algorithm (MJA) to capture localized correlations among different energy sources. Leveraging its nonlinear time-series properties, the model integrates meteorological conditions and specific energy source data. Additionally, principal component analysis (PCA) is employed to extract linear time-series data characteristics for each energy source. Application of the proposed AI-infused approach to a renewable energy system demonstrates its effectiveness and feasibility in the context of climate change mitigation. Results reveal the superior accuracy of the hybrid framework compared to more complex models such as decision trees and ResNet. Specifically, our proposed method achieved remarkable performance, boasting the lowest error rates with a normalized RMSE of 6.51 and a normalized MAPE of 4.34 for solar photovoltaic (PV), highlighting its exceptional precision in terms of mean absolute errors. A detailed sensitivity analysis is carried out to evaluate the influence of every element in the hybrid framework, emphasizing the importance of energy correlation patterns. Comparative assessments underscore the increased accuracy and stability of the suggested AI-infused framework when compared to other methods.

A novel modified JAYA algorithm for heat exchanger optimization

A novel modified JAYA algorithm for heat exchanger optimization

Loss minimization based optimal frequency selection for low frequency transmission lines using telegrapher’s method

PurposeThe purpose of this study is to obtain the optimal frequency for low-frequency transmission lines while minimizing losses and maintaining the voltage stability of low-frequency systems. This study also emphasizes a reduction in calculations based on mathematical approaches.Design/methodology/approachTelegrapher’s method has been used to reduce large calculations in low-frequency high-voltage alternating current (LF-HVac) lines. The static compensator (STATCOM) has been used to maintain voltage stability. For optimal frequency selection, a modified Jaya algorithm (MJAYA) for optimal load flow analysis was implemented.FindingsThe MJAYA algorithm performed better than other conventional algorithms and determined the optimum frequency selection while minimizing losses. Voltage stability was also achieved with the proposed optimal load flow (OLF), and statistical analysis showed that the proposed OLF reduces the frequency deviation and standard error of the LF-HVac lines.Originality/valueThe optimal frequency for LF-HVac lines has been achieved, Telegrapher’s method has been used in OLF, and STATCOM has been used in LF-HVac transmission lines.

Optimization of distribution network operating parameters in grid tied microgrid with electric vehicle charging station placement and sizing in the presence of uncertainties

ABSTRACT The power generated by renewable sources is prone to uncertainties which encourages to perform effective modelling to ensure the reliable operation of the grid-connected microgrid (MG) system. This work focuses on the uncertainty modelling of primary sources of solar power, wind power and electric vehicle (EV) load considering three factors battery capacity, state of charge (SoC) and type of EVs charged by employing Monte-Carlo Simulation (MCS) to minimize the distribution system's voltage stability, reliability and power loss (VRP) index. The objective function is tested on the modified IEEE-33 bus distribution system under three diverse scenarios with optimum sizing and placement of RESs and EVCS. To obtain optimal solutions for the proposed problem in a reasonable computation time, modified version of teaching and learning-based optimization (TLBO) and the JAYA algorithm are applied as the rate of convergence is superior to other existing methods in the literature and does not require any precise control parameters. For all the scenarios, it can be seen that the modified JAYA algorithm outperforms TLBO and other existing approaches. The findings of the results reveal the efficacy of uncertainty modelling in a proposed grid-connected DC MG to curtail the VRP index.

A Reduced Switch Count Multilevel Inverter for PV Standalone System using Modified JAYA Algorithm

ABSTRACT This paper introduces a hybrid multilevel inverter (MLI) with reduced switch count, which can generate higher output voltage level with minimum number of DC input sources. The operation of this proposed MLI is carried out with unequal DC sources to achieve the desired output voltage level. The reduced MLI output voltage is set to minimal total harmonic distortion (THD) with the help of modified JAYA (MJAYA) algorithm. A JAYA algorithm with improved steps by adopting an accelerating parameter has been proposed in this research work to obtain a faster convergence of the objective function. The MJAYA algorithm has provided the suitable switching angles for the proposed three-phase 15-level MLI and reduced the output voltage THD to 2.23%, which satisfies the standard set by IEEE-519. To prove the efficiency of this proposed modified algorithm, the comparative analysis is carried out through MATLAB program and Simulink tool using common JAYA and modified particle swarm optimisation algorithms. The performance and productivity of the proposed MLI have been investigated through simulation and experimental setups.

Modified Jaya algorithm with depth search and perturbation

Modified Jaya algorithm with depth search and perturbation

Modified Jaya algorithm with depth search and perturbation

Modified Jaya algorithm with depth search and perturbation

Optimal Planning of Power Distribution Network by a Novel Modified Jaya Algorithm in Multiobjective Perspective

This article presents a novel improved Elitism oppositional Jaya algorithm for the optimal power distribution network reconfiguration and optimal sizing, siting of multiple distributed generations (DGs) with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> , PQV buses. Here, PQV bus is a voltage-specified PQ type bus controlled by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> bus. In this proposed planning model, the basic Jaya algorithm is modified by incorporating variable inertia weight, vicinity searching capability, elitism property, and blended with an opposition learning-based strategy. This establishes an effective approach between global search and local search in quest of an optimal solution. A multiobjective framework is developed to optimize technical and economic objectives simultaneously. Optimal weights are allocated to each objective using an improved analytical hierarchy process method. Novel indices are proposed to identify the optimal locations of DGs also considering critical loads. During reconfiguration, the line data sequence is rearranged by a smart graph theory mechanism. Furthermore, different schemes have been studied by the suggested method on IEEE 33 and 69 bus distribution test systems. The findings of the study by using the proposed algorithm in comparison with particle swarm optimization (PSO) and enhanced leader PSO show its effectiveness and validity.

Optimized seismic design of planar and spatial steel frames using the hybrid learning based jaya algorithm

In this paper, three powerful metaheuristic algorithms, modified teaching learning based optimization, modified jaya algorithm and hybrid learning based jaya algorithm are presented for optimized seismic design of planar/spatial steel frames. The hybrid learning based jaya algorithm combines the learning phase of teaching learning based optimization and jaya algorithm. The goal is to design steel frames of minimum weight under strength, interstory drift and geometric-size constraints. One planar and two spatial steel frames previously designed for minimum weight by different metaheuristic optimization methods are taken as design examples to test the proposed methods. The methods presented in this paper are compared with other referenced algorithms in terms of optimized weight and computational cost. It is found that the hybrid learning based jaya algorithm outperforms modified teaching learning based optimization, modified jaya algorithm and its other competitors in all test problems.

Structural damage identification considering uncertainties based on a Jaya algorithm with a local pattern search strategy and L0.5 sparse regularization

This study develops an improved Jaya algorithm for structural damage identification considering various uncertainties using vibration data. Most studies consider uncertainties, such as measurement noise, modeling uncertainties, and temperature variations separately but few on their coupling effects. On the other hand, the Jaya algorithm may trap local minimums in optimizing complex objective functions. Therefore, a novel modified Jaya algorithm is developed by integrating the one-step K-means clustering, Hooke–Jeeves pattern search, and linear population reduction strategies. The three modification strategies greatly improve the global optimization performance of the standard Jaya algorithm, which is verified by a series of high-dimension test functions. An L0.5 regularization is applied to improve the ill-posedness of the damage identification problem and ensure the sparsity of the solution. Numerical and experimental studies on two structures show that the proposed algorithm can identify the structural damage accurately, even considering the coupling effects of various types of uncertainties.

Modified JAYA Algorithm for Machinability Assessment and Conflicting Response Optimization During Milling of Nanostructured Carbon Onions Reinforced Epoxy Composites

Modified JAYA Algorithm for Machinability Assessment and Conflicting Response Optimization During Milling of Nanostructured Carbon Onions Reinforced Epoxy Composites

The Synchronization of Hyperchaotic Systems Using a Novel Interval Type-2 Fuzzy Neural Network Controller

This paper proposed a novel interval type-2 fuzzy neural network controller (NT2FC) to synchronize 5-D hyperchaotic systems with noise disturbance and system uncertainties. In the proposed controller, the type 2 fuzzy set is designed with the 3-dimensional Gaussian membership functions (3DGMFs) to increase the system’s ability to respond to uncertainty. The parameters of the NT2FC controller are updated online via adaptation laws, which are built based on the gradient descent approach. The system stability is ensured through the Lyapunov stability analysis. In addition, the modified Jaya algorithm (MJA) is applied to optimize the learning rates in adaptation laws. Finally, the efficiency of the proposed NT2FC is examined by the numerical simulation of the hyperchaotic system’s synchronization.

Solution of optimal power flow problem using sine-cosine mutation based modified Jaya algorithm: a case study

ABSTRACT For planning the economic operation and control of an existing electricity grid and its future extension planning, the optimal power flow (OPF) results are essential. In this paper, a hybrid algorithm is proposed, which is based on a sine-cosine mutation operator and a modified Jaya (SCM-MJ) algorithm to solve the OPF problems. The efficacy of the SCM-MJ algorithm is primarily evaluated using thirteen (unimodal and multimodal) mathematical benchmark functions. Later, the SCM-MJ algorithm is applied on the Algerian 59-bus system and IEEE 118-bus test system to handle the OPF problems. The SCM-MJ has successfully achieved a minimum value of an objective function over several runs than other modern meta-heuristic optimization approaches in all the thirteen mathematical benchmark functions as well as in OPF case studies. For example, in the Algerian 59-bus system, the quadratic fuel cost obtained by SCM-MJ is 1688 $/hour, with a saving of 13.12% of the initial fuel cost. This benefit increases further with the size of the system. The SCM-MJ algorithm has provided high-quality solutions for mathematical benchmark functions and OPF problems quickly and efficiently. The comparison of numerical outcomes demonstrates that the suggested SCM-MJ algorithm dominates over other approaches for solving the OPF problem.

Modified Jaya Algorithm for Optimal Design of Water Distribution Network. (Dept. C)

For the first time, the recently proposed Jaya metaheuristic optimization algorithm (JA) is used for optimal design of water distribution networks (WDN’s). This algorithm has no control parameters, which eliminates the exhausting computational effort required to carry out the essential tune up step for these parameters. A new variant of the algorithm, named free sensitivity analysis Jaya algorithm (FSAJA), is suggested to be free from even the common metaheuristic algorithms parameters, i.e., population size, number of generations and penalty function. Six different variants of the proposed algorithm are investigated to settle the best one using a recently developed two performance criteria. Three famous benchmarks WDN’s and a national one, are solved to examine the algorithm. Comparing the performance of FSAJA, with JA and the various evolutionary algorithms available in the literature shows the promising effectiveness, efficiency, and robustness of the proposed algorithm. A new global minimum is achieved for the national WDN.

Analyses of plane stress and plane strain through energy minimization

The stable equilibrium state of a structural system corresponds to the relative minimum total potential energy state. In order to analyze the structures by energy principle, energy minimization is addressed as an optimization problem. In early studies, few structural problems are investigated with the aid of energy minimization; flat and curved plates, bending plates, sandwich plates, cylindrical shell elements, truss elements, hyper-elastic membranes. The aim of this paper is to investigate the analysis of plane stress/strain elements through energy minimization. For this purpose, three state-of-art parameter-less metaheuristic algorithms were used to find the relative minimum total potential energy; Teaching–Learning Based Optimization (TLBO), Elitist Teaching–Learning Based Optimization (ETLBO), and Jaya Algorithm (JA). Additionally, a modification of Jaya Algorithm that named The Modified Jaya Algorithm (MJA) is proposed to analyze the plane stress/strain elements. According to the analysis results of the benchmark problems, TLBO and MJA are robust for the plane stress/strain analysis. In addition, MJA is more suitable algorithm in respect of computational efficiency from the other compared metaheuristic algorithms.

Modified JAYA algorithm for solving the flexible job shop scheduling problem considering worker flexibility and energy consumption

This paper investigates a flexible job shop scheduling problem with worker flexibility and energy consumption. A modified JAYA algorithm (MJAYA) is developed to minimise the total energy consumption. In the MJAYA, three improvement strategies are used to improve the algorithm's performance, such as Modified Individual Updating (MIU) method, Adaptive Mutation Operator (AMO) and Local Search Strategy (LSS). The MIU is developed to improve the exploration ability by adding a random term to the original updating equation. The AMO is used to keep the population diversity. In addition, The LSS is employed to enhance the local search capacity. Finally, extensive simulations are performed to validate the effectiveness of the proposed MJAYA algorithm. Experimental data show that the MJAYA algorithm is effective for solving the considered problem.

Modified Jaya algorithm with chaos

A simple and new optimisation algorithm for solving constrained and unconstrained optimisation problems (Jaya) is a recently developed optimisation algorithm, which is a new optimisation algorithm designed to solve optimisation related problems, it has two random parameters in equations. In the study of this paper, we will introduce chaos into Jaya so as to increase non-repeatability and ergodicity for global optimisation. Here, four different chaotic maps are utilised to control random parameters in Jaya. The results show that some chaotic maps can outperform the random parameters in the high-dimensional function and the result of the two-dimensional function is almost the same.

Modified Jaya algorithm with chaos

Modified Jaya algorithm with chaos

Modified JAYA algorithm for solving the flexible job shop scheduling problem considering worker flexibility and energy consumption

Modified JAYA algorithm for solving the flexible job shop scheduling problem considering worker flexibility and energy consumption