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Performance analysis of GA, PSO and JA for determining the optimal parameters in friction drilling process

ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2022年 35卷

作者： Chityal, Nitin Sapkal, Sagar Walchand Coll Engn Dept Mech Engn Sangli 416415 Maharashtra India

The friction drilling process, a technique for making sheet metal holes, differs from the conventional dril-ling process. Instead of cutting the workpiece, this process forms bush by using high heat generated at the contact region of the rotating conical tool and work material. The surface quality of the bush is not like a shiny and smooth surface due to the generation of high heat energy. Experimental investigation of fric-tion drilling process has been done for optimization of surface roughness and bushing length by various researchers. This research work attempts application of meta-heuristics such as Genetic algorithm (GA), Particle Swarm Optimization (PSO) and jaya algorithm (JA) and compares the results in order to evaluate their performances. GA, PSO and JA are applied to minimize the surface roughness of the friction drilled holes which depends on parameters viz rotational speed, friction angle and workpiece thickness. Also, these algorithms are applied to maximize bushing length by considering the significant parameters viz friction angle, friction contact area ratio, feed rate and spindle speed. The results are compared for surface roughness value and bushing length signal to noise ratio. The comparative analysis shows that the jaya algorithm performs more efficiently with quick convergence to the solution compared to other two algo-rithms. The comparison of PSO with GA shows that, the PSO performs better in terms of convergence to the solution for both the objectives. Further, it has been found that JA shows the robustness in terms of consistency of results for different population sizes and number of iterations.(c) 2022 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Friction drilling Surface roughness Bushing length Genetic algorithm Particle swarm optimization jaya algorithm

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Optimizing Power Management in Distribution Networks: A Mathematical Modeling Approach for Coordinated Directional Over-Current Relay Control

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第10期14卷 397-408页

作者： Kaur, Simardeep Ralhan, Shimpy Singh, Mangal Singh, Mahesh Shri Shankaracharya Tech Campus Dept EE Bhilai Chhattisgarh India Symbiosis Int Symbiosis Inst Technol E&TC Dept Pune Maharashtra India

Optimizing Power Management in Distribution Networks through Coordination of Directional Over-Current Relays summarizes a study or project focused on enhancing the management of power in distribution networks by optimizing the coordination of directional over-current relays. Directional over-current relays are critical components of power distribution systems, designed to safeguard the network against over-current faults while maintaining operational stability. Proper coordination of these relays is vital to ensure that faults are isolated and cleared efficiently without causing extensive disruptions. In this paper, a mathematical modeling approach is employed to address the optimization of power management in distribution networks. This approach likely encompasses the development of mathematical models and algorithms that consider factors such as fault types, fault locations, network topology, and relay settings to improve the coordination of directional over-current relays. Here, different optimization algorithms have been implemented to optimize the operating time of relays & hence power management. Cuckoo Search algorithm (CSA), Fire-Fly algorithm (FFA), Harmony Search algorithm (HSA), and jaya algorithm are employed to solve the coordination problem for directional over-current relays (DOCRs) with different test systems. The outcomes of this research may have practical applications in power distribution systems, potentially leading to more resilient and responsive networks that better manage power distribution and reduce disruptions during faults and outages.

关键词： Optimization Cuckoo Search algorithm (CSA) Fire-Fly algorithm (FFA) Harmony Search algorithm (HSA) jaya algorithm directional over-current relays

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Reducing catastrophic forgetting problem in streaming data by hybrid shark smell with jaya optimization-based deep neural networks

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INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING 2022年第4期13卷

作者： Singh, Maisnam Niranjan Khaiyum, Samitha VTU Dayananda Sagar Coll Engn Bengaluru Karnataka India Dayananda Sagar Coll Engn MCA Dept Bengaluru Karnataka India

Catastrophic forgetting is a well-known characteristic of diverse parameterized supervised learning models. Artificial Neural Networks (ANN) face severe catastrophic forgetting or catastrophic interference in the sequential learning of information that is intolerable to both engineering applications and the human memory model. This problem can be solved through the machine learning community. The main intent of this paper is to implement a novel continuous learning model that could overcome the problem of catastrophic forgetting. The stream data from diverse benchmark sources are used for experimenting. The proposed model adopts the Deep Neural Network (DNN) with testing weight updates. When new data are given, the hybrid meta-heuristic algorithm helps to update the weight that manages to prevent the learning from catastrophic forgetting. The weight update strategy for solving the catastrophic forgetting is done by the hybridization of Shark Smell Optimization (SSO) and jaya algorithm (JA) that is named as Hybrid Shark Smell with jaya Optimization (HSS-JO). A multi-objective function concerning the parameters associated with catastrophic forgetting like accuracy and remembering is used for framing the proposed continuous learning. The proof of the proposed model over conventional models on three publicly available datasets is given for final validation. From the analysis, in Table 3, the accuracy of the proposed HSS-JODNN is 38%, 0.5%, 0.55%, 0.14%, 0.98%, and 0.5% superior to DT, NB, KNN, SVM, NN, and RNN, respectively. For dataset 2, the FNR of the proposed HSS-JO-DNN is 90% superior to DT, 89% improved than NB, KNN, and NN, 94% improved than SVM, and 24% improved than RNN. Similarly, the proposed HSS-JO performance is higher in all the results. Thus, the proposed HSS-JO performs better than all the other traditional methods.

关键词： Artificial Neural Networks Deep Neural Network Shark Smell Optimization jaya algorithm Hybrid Shark Smell with jaya Optimization

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Spectral approach to localize information spread in a network using Rao's metaheuristic variants

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SOCIAL NETWORK ANALYSIS AND MINING 2023年第1期13卷 32-32页

作者： Mohapatra, Debasis Parala Maharaja Engn Coll Berhampur 761003 India

Understanding different aspects of information spread have been a crucial direction of research in information and social sciences. Recent studies are found to be focused on accelerating and controlling the spread of information. One such study is to control the spread of information by manipulating the Principal Eigen Vector (PEV) of the network adjacency matrix. In this paper, Inverse Participation Ratio (IPR) is used to measure the localization of PEV. The studies say that maximum IPR leads to a high localization state of the network. In literature, IPR maximization is shown to be a hard problem. Therefore, we use approximation approach of solution to this problem by employing metaheuristic algorithms. In this direction, we propose graph versions of three Rao's metaheuristic variants: Rao's algorithm, jaya algorithm, and Teaching-Learning-Based Optimization (TLBO) algorithm for IPR maximization. Four evaluation parameters: Average Optimal IPR, Factor Improvement (IPR), Average Modification %, and Average Execution Time are used for comparison among the algorithms. The results show that TLBO outperforms the other two variants. In TLBO, the factor of improvement over the initial IPR value is found to be 1.5 in KC dataset with an average modification of 25%, 5.9 in GD 99 dataset with an average modification of 32%, 12.1 in GD 01 dataset with an average modification of 19%, 2.1 in USAL dataset with an average modification of 17%, and 15.1 in CL dataset with an average modification of 18%. Also, TLBO reports a better Average Optimal IPR value than the existing Random Perturbation (RP) approach.

关键词： Inverse participation ratio Rao's algorithm jaya algorithm TLBO algorithm

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Enhanced jaya optimization for improving multilayer perceptron neural network in urban air quality prediction

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JOURNAL OF INTELLIGENT SYSTEMS 2024年第1期33卷

作者： Abu Doush, Iyad Sultan, Khalid Alsaber, Ahmad Alkandari, Dhari Abdullah, Afsah Amer Univ Kuwait Coll Engn & Appl Sci POB 3323Block 2 Safat 13034 Kuwait Yarmouk Univ Comp Sci Dept Irbid 21163 Jordan Amer Univ Kuwait Coll Business & Econ Dept Management Safat 13034 Kuwait Kuwait Univ Shadadiya Campus Dept Earth & Environm Sci Kuwait 12422 Kuwait Amer Univ Kuwait Off Res & Grants Safat 13034 Kuwait

The multilayer perceptron (MLP) neural network is a widely adopted feedforward neural network (FNN) utilized for classification and prediction tasks. The effectiveness of MLP greatly hinges on the judicious selection of its weights and biases. Traditionally, gradient-based techniques have been employed to tune these parameters during the learning process. However, such methods are prone to slow convergence and getting trapped in local optima. Predicting urban air quality is of utmost importance to mitigate air pollution in cities and enhance the well-being of residents. The air quality index (AQI) serves as a quantitative tool for assessing the air quality. To address the issue of slow convergence and limited search space exploration, we incorporate an opposite-learning method into the jaya optimization algorithm called EOL-jaya-MLP. This innovation allows for more effective exploration of the search space. Our experimentation is conducted using a comprehensive 3-year dataset collected from five air quality monitoring stations. Furthermore, we introduce an external archive strategy, termed EOL-Archive-jaya, which guides the evolution of the algorithm toward more promising search regions. This strategy saves the best solutions obtained during the optimization process for later use, enhancing the algorithm's performance. To evaluate the efficacy of the proposed EOL-jaya-MLP and EOL-Archive-jaya, we compare them against the original jaya algorithm and six other popular machine learning techniques. Impressively, the EOL-jaya-MLP consistently outperforms all other methods in accurately predicting AQI levels. The MLP model's adaptability to dynamic urban air quality patterns is achieved by selecting appropriate values for weights and biases. This leads to efficacy of our proposed approaches in achieving superior prediction accuracy, robustness, and adaptability to dynamic environmental conditions. In conclusion, our study shows the superiority of the EOL-jaya-MLP over tra

关键词： multilayer perceptron neural network air quality prediction training neural network archive technique jaya algorithm

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A novel dynamic opposite learning enhanced jaya optimization method for high efficiency plate-fin heat exchanger design optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期119卷

作者： Zhang, Lidong Hu, Tianyu Zhang, Linxin Yang, Zhile McLoone, Sean Menhas, Muhammad Ilyas Guo, Yuanjun Northeast Elect Power Univ Jilin 132012 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Guangdong Peoples R China City Univ Macau Fac Data Sci Taipa 999078 Macao Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast North Ireland Mirpur Univ Sci & Technol Dept Elect Engn Mirpur Pakistan

A Plate-fin heat exchanger (PFHE) is a compact and efficient thermal device, whose performance strongly depends on its structural design. However, the design optimization of a PFHE is a mixed-integer optimization problem with a strong nonlinear characteristic, which presents significant challenges for existing optimization algorithms. Meta-heuristic algorithms (MAs) are competitive for solving complex non-linear optimization problems. In this paper, an improved dynamic-opposite learning jaya (DOLjaya) method, the goal is to make the algorithm adaptable to each problem. The results of eighteen unimodal and multi-modal benchmarks and nine hybrid benchmarks demonstrate that the proposed DOLjaya has competitive robustness, efficiency and effectiveness for solving complex nonlinear problems compared to its popular counterparts. At the same time, we selected the optimization of the plate-fin heat exchanger as the industrial test benchmark for optimization, and the results of DOLjaya algorithm have been improved by a maximum average of 108.29% and 7.60% compared with the original jaya, which are also satisfactory.

关键词： Plate-fin heat exchangers jaya algorithm Optimal design Dynamic-opposite learning

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algorithm for DSTATCOM Using Cascaded Delayed Signal Cancellation Effect in Three Wire System

Algorithm for DSTATCOM Using Cascaded Delayed Signal Cancell...

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IEEE International Conference on Energy, Systems and Information Processing

作者： jayadeep Srikakolapu Sabha Raj Arya Rakesh Maurya Department of Electrical Engineering SardarVallabhbhai National Institute of Technology

ISBN: (纸本)9781728104201

In this study a Cascaded αβ delayed signal cancellation based frequency locked loop (Cαβ-DSCFLL) is proposed in the control scheme of the distributed static compensator under disturbed ac main currents with Power Factor Correction mode (PFC). In this algorithm reduced order generalized integrator type FLL has been improved by employing delayed signal cancellation technique. This application of delayed signal cancellation in cascaded mode has improved the THD of supply current. The Cαβ-DSC operators increase the ability of the standard FLL in respect to load disturbance. The PI controller gains have been estimated using an algorithm specific, parameter less type optimization algorithm named jaya optimization algorithm. The error minimization of DC bus voltage has been formulated as an unconstrained optimization problem and gains have been estimated using jaya algorithm. The proposed control scheme, with optimized controller gains has handled issues like reactive power compensation and grid current harmonics mitigation for an uncontrolled three phase full bridge rectifier type of nonlinear load. This paper provides results of d-SPACE based real time implementation of the proposed Cαβ-DSCFLL control method for DSTATCOM in three wire system with diode based nonlinear load.

关键词： Delay factors Delay signal cancellation jaya algorithm DSTATCOM delayed signal algorithms CANCELLATIONS nonlinear load cascades Optimization algorithms dc bus voltage reactive compensation frequency locked loops

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Performance prediction and optimization of nanolubricants for hydrodynamic journal bearings: a data-driven approach for regulating volumetric fraction and aggregate particle size

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COGENT ENGINEERING 2024年第1期11卷

作者： Mahajan, Adwait Ganesha, A. Hariharan, Girish Pai, Raghuvir Manipal Acad Higher Educ Manipal Inst Technol Dept Mech & Ind Engn Manipal 576104 Karnataka India

This study aims to set up a data-driven framework for optimization and performance prediction of a nanolubricant. Isoviscous approximation was applied to simplify analytical results. FDM methods were used to generate 1204 data points. A multilayer perceptron (MLP) was trained using this large dataset to achieve high testing accuracy (R = 1). The least mean squared error was found at 987 epochs for 23 hidden layer neurons. Multiple statistical tools were used to analyze the results. The error values in the load prediction (up to 7.34%) were observed to be higher than the friction coefficient (up to 0.041%) and side leakage (up to 0.05%). The jaya algorithm, a parameter-independent machine learning algorithm was used for zone-specific optimization with the zones being low load region (eccentricity ratio: 0.1 to 0.3), medium load region (eccentricity ratio: 0.3-0.6), high load region (eccentricity ratio: 0.6-0.95) and whole range. The optimized combinations for low, medium, high and whole range of operation gave load capacity of 91 N, 357 N, 1328 N and 124 N, respectively. The optimized results highlight the tendency to minimize the eccentricity ratio and maximize volume fraction and aggregate particle size ratio.

关键词： Nanoparticle aggregation FDM hydrodynamic journal bearing ANN optimization jaya algorithm

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