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artificial raindrop algorithm-Based Load Frequency Control in PV-Thermal-Thermal Interconnected Power System

ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年

作者： Revathi, D. Kumar, G. Mohan SNS Coll Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India Pk Coll Engn Technol Dept Mech Engn Coimbatore Tamil Nadu India

Nowadays Grid connected photovoltaic (PV) systems increases its influence in Indian power system. The current rise in load and demand means that linked power networks are essential. The efficiency of a load is significantly impacted by power quality issues, especially when it comes to frequency. The power system's frequency and tie-line power should be as close as possible to the anticipated specifications. This article proposes artificial raindrop method with proportional- integral (PI) controller, to manage the load frequency control (LFC) of a PV-thermal-thermal interconnected three-area power system. A meta-heuristic method that takes inspiration from natural rainfall is the proposed artificial raindrop algorithm. A comparison is made between the suggested system and standard PI controller. A number of loads are simulated using Matlab to assess the full system. To validate the performance of proposed system, it is compared with the LFC using PI and fuzzy gain scheduling controller. Matlab is used to evaluate the entire system with varying loads. Take into account area 1 in 10% load perturbation: FGS reduces frequency deviation undershoot by 22% compared to the PI controller, while the ARD algorithm reduces it by 32%, where PI results in an undershoot of 0.1781 Hz.

关键词： Load frequency control tie-line power PV-thermal-thermal interconnected power system PI artificial raindrop algorithm

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Intrusion Detection System using Long Short Term Memory Classification, artificial raindrop algorithm and Harmony Search algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第12期13卷 95-103页

作者： Raj, Meghana G. Pani, Santosh Kumar Kalinga Inst Ind Technol Deemed Univ Dept Sch Comp Engn Bhubaneswar India

Nowadays, various technological advancements in Intrusion Detection Systems (IDS) detects the malicious attacks and reinstate network security in the cloud platform. Cloud based IDS designed with hybrid elements combining Machine Learning and Computational Intelligence algorithms have been shown to perform better on parameters, such as Detection Rate, Accuracy, and the False Positive Rate. Machine Learning algorithms provide effective techniques for classification and prediction of network attacks, by analyzing existing IDS datasets. The main challenge is selection of appropriate data dimensions to be used for detection of attacks, out of the high number of data dimensions available. For the selected data dimensions, Computational Intelligence algorithms provide effective techniques for hyper-parameter tuning, by optimizing on reiterative basis. The main challenge is selection of appropriate algorithm which offers optimal performance results. In this research, Hybrid Meta-heuristic approach, which combines a Long Short Term Memory (LSTM) classification model in dimension selection, with the application of artificial raindrop algorithm-Harmony Search algorithm (ARA-HSA) for hyper -parameter tuning, in order to achieve a high performance IDS in cloud environment. The performance validation of the hybrid LSTM-ARA-HSA algorithm has been carried out using a benchmark IDS data set and the comparative results for this algorithm along with other recent hybrid approaches has been presented.

关键词： artificial raindrop algorithm cloud computing harmony search algorithm hybrid meta-heuristic algorithms hyper-parameter tuning intrusion detection system long short term memory classification model

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ARAE-SOM plus BCO: An enhanced artificial raindrop algorithm using self-organizing map and binomial crossover operator

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NEUROCOMPUTING 2018年 275卷 2716-2739页

作者： Jiang, Qiaoyong Wang, Lei Hei, Xinhong Cheng, Jiatang Lu, Xiaofeng Lin, Yanyan Yu, Guolin Xian Univ Technol Fac Comp Sci & Engn Xian 710048 Shaanxi Peoples R China Honghe Univ Engn Coll Mengzi 661199 Peoples R China Beifang Univ Nationalities Inst Appl Math Yinchuan 750021 Peoples R China

artificial raindrop algorithm (ARA) has been shown to be an effective methodology for solving optimization problems over continuous space. To further enhance the search performance of ARA, self-organizing map (SOM) and binomial crossover operator (BCO) are integrated into ARA. The purpose of the former is to enhance the diversity of population by dynamically dividing the current population into several sub-populations. The latter is to make the function landscape be pseudo-separable by covariance matrix learning, which efficiently directs the evolution of the population toward a global optimum. By incorporating the SOM and BCO into ARA, an enhanced ARA variant, which is called ARAE-SOM+BCO for short, is presented in this paper. For the comparison purposes, the forehand fourteen CEC2005 contest benchmark functions are selected as the test suite to evaluate the performance of the proposed ARAE-SOM+BCO. The experiment results show that the performance of ARAE-SOM+BCO significantly outperforms ARA and its extension variant, and is also competitive with other state-of-the-art evolutionary algorithms in most test functions. (C) 2017 Elsevier B.V. All rights reserved.

关键词： artificial raindrop algorithm Self-organizing map Binomial crossover operator Covariance matrix learning

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artificial raindrop algorithm-Based Design of Wideband IIR Fractional Order Digital Integrators

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2017年第3期41卷 165-173页

作者： Mahata, Shibendu Saha, Suman Kumar Kar, Rajib Mandal, Durbadal NIT Dept Elect & Commun Engn Durgapur India NIT Dept Elect & Telecommun Engn Raipur Madhya Pradesh India

A new technique to design accurate, stable, and wideband infinite impulse response, fractional order digital integrators (FODIs) for the fractional order integrators based on a metaheuristic optimization technique called artificial raindrop algorithm (ARA) is proposed in this paper. ARA is inspired by the process of natural rainfall, and achieves the global optimal solution by identifying the raindrop which occupies the lowest altitude. To investigate the efficiency of the proposed approach, comparisons have been carried out with real coded genetic algorithm, particle swarm optimization, and differential evolution-based FODIs. Simulation results demonstrate that ARA-based FODIs consistently accomplish better magnitude responses in the least number of iteration cycles. The proposed FODIs also outperform all the recently reported designs.

关键词： Fractional order integrators artificial raindrop algorithm Optimization Nature-inspired algorithms

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An efficient multi-objective artificial raindrop algorithm and its application to dynamic optimization problems in chemical processes

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APPLIED SOFT COMPUTING 2017年 58卷 354-377页

作者： Jiang, Qiaoyong Wang, Lei Lin, Yanyan Hei, Xinhong Yu, Guolin Lu, Xiaofeng Xian Univ Technol Fac Comp Sci & Engn Xian 710048 Peoples R China Beifang Univ Nationalities Inst Appl Math Yinchuan 750021 Peoples R China

Recently, a new meta-heuristic approach, known as artificial raindrop algorithm (ARA), was proposed. This approach is inspired by the natural rainfall phenomenon, and has been observed to be a powerful tool in solving single-objective optimization problems. In this paper, a multi-objective variant of ARA (MOARA) is developed, which primarily combines the search mechanism of ARA and the non-dominated sorting technique, in an attempt to demonstrate the potential of ARA in tackling multi-objective optimization problems (MOPs). To improve the exploratory ability, the center point sampling strategy (CPSS) together with simulated binary crossover (SBX) is integrated into MOARA. The primary role of SBX is to accelerate the filling of the Pareto front (PF) by recombining diverse solutions, whereas CPSS serves as the domain knowledge of the MOP for guiding other points toward the target PF. For performance evaluation and comparison purposes, the proposed approach has been applied to two sets of benchmark MOPs, and compared with eight state-of-the-art multi-objective evolutionary algorithms based on the non-dominated sorting. The experimental results have indicated its improved efficiency over the other compared approaches. Furthermore, the contributions of SBX and CPSS to the entire algorithm have been experimentally studied. Finally, the proposed technique is applied to dynamic optimization problems in chemical processes, and promising results show the method's potential in real-world applications. (C) 2017 Published by Elsevier B.V All rights reserved.

关键词： Multi-objective optimization artificial raindrop algorithm Nondominated sorting Center point sampling strategy Simulated binary crossover

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Blood Glucose Analysis Utilizing artificial raindrop algorithm and Capacitance Method

Blood Glucose Analysis Utilizing Artificial Raindrop Algorit...

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International Conference on Computer and Applications (ICCA)

作者： Cuello, Arcy Dizon, Kathlynne Fortu, Mitz Louis Paguia, John Grayson Tanola, Anna Victoria Velasco, Jessica Aquino, Aaron Valenzuela, Ira Technol Univ Philippines Manila Philippines

ISBN: (纸本)9781538627525

Controlling blood glucose is a way to avoid complications of diabetes. Blood glucose monitoring is a way to test the concentration of glucose in the blood. This study aims to determine the suitability of artificial raindrop algorithm (ARA) and capacitance method in measuring the blood glucose. ARA is a new search algorithm that produces very promising results on the nonlinear function approximation. Blood samples were obtained from 65 different individuals. The sample was placed in a glass slide and the blood glucose is measured using the developed prototype. The results were compared to the laborotary result. The developed device showed 96.92% accuracy.

关键词： Capacitance Method artificial raindrop algorithm Diabetes Blood Glucose Glucometer

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MOEA/D-ARA plus SBX: A new multi-objective evolutionary algorithm based on decomposition with artificial raindrop algorithm and simulated binary crossover

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KNOWLEDGE-BASED SYSTEMS 2016年 107卷 197-218页

作者： Jiang, Qiaoyong Wang, Lei Hei, Xinhong Yu, Guolin Lin, Yanyan Lu, Xiaofeng Xian Univ Technol Fac Comp Sci & Engn Xian 710048 Peoples R China Beifang Univ Nationalities Inst Appl Math Yinchuan 750021 Peoples R China

In the field of optimization computation, there has been a growing interest in applying intelligent algorithms to solve multi-objective optimization problems (MOPs). This paper focuses mainly on the multi objective evolutionary algorithm based on decomposition, MOEA/D for short, which offers a practical general algorithmic framework of evolutionary multi-objective optimization, and has been achieved great success for a wide range of MOPs. Like most other algorithms, however, MOEA/D has its limitations, which are reflected in three aspects: the problem of balancing diversity and convergence, non-uniform distribution of the Pareto front (PF), and weak convergence of the algorithm. To alleviate these limitations, a new combination of the artificial raindrop algorithm (ARA) and a simulated binary crossover (SBX) operator is first integrated into the framework of MOEA/D to balance the convergence and diversity. Thus, our proposed approach is called MOEA/D with ARA and SBX (MOEA/D-ARA+SBX). On the other hand, the raindrop pool in ARA is further extended to an external elitist archive, which retains only non-dominated solutions and discards all others. In addition, the k-nearest neighbors approach is introduced to prune away redundant non-dominated solutions. In such a way, a Pareto approximate subset with good distribution to the true PF may be achieved. Based on the relevant mathematical theory and some assumptions, it is proven that MOEA/D-ARA+SBX can converge to the true PF with probability one. For performance evaluation and comparison purposes, the proposed approach was applied to 44 multi-objective test problems with all types of Pareto set shape, and compared with 16 other versions of MOEA/D. The experimental results indicate its advantages over other approaches. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization artificial raindrop algorithm Decomposition k-nearest neighbors External elitist archive Convergence analysis

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Parameter identification of chaotic systems using artificial raindrop algorithm

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JOURNAL OF COMPUTATIONAL SCIENCE 2015年 8卷 20-31页

作者： Jiang, Qiaoyong Wang, Lei Hei, Xinhong Xian Univ Technol Fac Comp Sci & Engn Xian 710048 Peoples R China

The knowledge about parameter identification is an important issue for the synchronization and control of chaos. These chaotic systems involve some input parameters which may greatly affect their dynamic behavior. In this paper, a newly developed meta-heuristic method - artificial raindrop algorithm (ARA) inspired from the phenomenon of natural rainfall, is applied to identify the unknown parameters of chaotic system first time in the literature. In order to verify the effectiveness of ARA, numerical experiments are carried on Chen, Lu, Rossler, Lorenz system, Logistic with time-delay system, and Mackey-Glass with time-delay system. The simulation results indicate that ARA can more effectively and accurately identify the parameters for given chaotic systems with respect to other seven state-of-the-art intelligent optimization algorithms. (C) 2015 Elsevier B.V. All rights reserved.

关键词： artificial raindrop algorithm Chaotic systems Parameter identification

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Performance assessment of artificial raindrop MPPT with high gain resonant switched-capacitor converter-based wind energy conversion system

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第12期31卷

作者： Kirubadevi, S. Sutha, S. Univ Coll Engn Dept Elect & Elect Engn Dindigul Tamil Nadu India

This article proposes an artificial raindrop algorithm (ARDA) to enhance maximum power point tracking (MPPT) in a permanent magnet synchronous generator based wind energy conversion system. The ARDA, a recently developed meta-heuristic approach, reproduces the varying procedure of a raindrop derived by the observation of a natural rainfall process. The efficacy of the wind energy conversion system depends on MPPT as well as power electronic converters employed in it. In this article, the ARDA is proposed as MPPT, where the performance of the proposed system is compared with the particle swarm optimisation based MPPT. To offer enriched performance, a high gain resonant switched-capacitor direct current-direct current converter is proposed in this analysis. The entire system is analysed under various wind speeds using Matlab/Simulink.

关键词： artificial raindrop algorithm grid-connected wind energy high gain RSC converter optimal torque control particle swarm optimisation PMSG

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