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5th International Conference on Electrical and Electronics Engineering (ICEEE)

作者： Hekimoglu, Baran Ekinci, Serdar Batman Univ Dept Elect & Elect Engn Batman Turkey Batman Univ Dept Comp Engn Batman Turkey

ISBN: (纸本)9781538663929

A novel design method is presented to determine optimum proportional-integral-derivative (PID) controller parameters of an automatic voltage regulator (AVR) system utilizing the grasshopper optimization algorithm (GOA). The proposed approach is a simple and effective algorithm that is able to solve many optimization problems even those with unknown search spaces effectively. The simplicity of algorithm provides high quality tuning of optimal PID controller parameters. The integral of time weighted squared error (ITSE) is used as the performance index to confirm the performance of the proposed GOA-PID controller. When compared to the other PID controllers based on Ziegler-Nichols (ZN), differential evolution (DE), and artificial bee colony (ABC) tuning methods, the proposed method is found highly effective and robust to improve AVR system's transient response.

关键词： automatic voltage regulator grasshopper optimization algorithm parameter tuning

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grasshopper optimization algorithm for Optimal Sizing of a Stand-Alone Hybrid Energy System 16

Grasshopper Optimization Algorithm for Optimal Sizing of a S...

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16th International Conference on Compatibility, Power Electronics, and Power Engineering (CPE-POWERENG)

作者： Al-Shamma'a, Abdullrahman A. Farh, Hassan M. H. Alkuhayli, Abdulaziz A. Noman, Abdullah M. Abdurraqeeb, Akram M. King Saud Univ Coll Engn Elect Engn Dept Riyadh Saudi Arabia

ISBN: (纸本)9781665496780

Hybrid energy systems (HES) that combine Photovoltaic (PV) arrays with Wind turbines (WTs) are excellent options for powering rural locations. The optimal design of HESs with distributed generations (DGs) (i.e., PV, WT) is still a hot topic due to the uncertainties in solar irradiance and wind speed. Based on a long-term cost analysis, this paper formulates an application of grasshopper optimization algorithm (GOA) in the field of HES design problem. GOA's efficacy is studied, and its performance is compared to that of the genetic algorithm (GA), butterfly optimization algorithm (BOA), and big-bang-big-crunch (BBBC) algorithm. The GOA outperformed the other seven metaheuristic algorithms where it attained the optimal solution of the HES with the lowest ASC. It can be revealed that the GOA has acceptable performance in terms of the global solution capture with less oscillations and the convergence rate.

关键词： Hybrid energy systems (HES) grasshopper optimization algorithm Optimal design Wind turbines Photovoltaic

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grasshopper optimization algorithm based Fuzzy PD-PI Cascade Controller for LFC of Interconnected Power System Coordinate with Renewable sources 6

Grasshopper Optimization Algorithm based Fuzzy PD-PI Cascade...

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6th IEEE Calcutta Conference (CALCON)

作者： Tripathy, Debasis Choudhury, N. B. Dev Behera, Sadasiva Sahu, Binod Kumar NIT Silchar Dept Elect Engn Silchar 788010 Assam India Siksha O Anusandhan Univ Dept Elect Engn Bhubaneswar 751030 India

ISBN: (纸本)9781728142838

The frequency instability issue in an interconnected power system due to small disturbances can be overcome by the load frequency control (LFC) scheme using a suitable secondary controller. So, this article introduces a grasshopper optimization algorithm (GOA) based fuzzy PD-PI cascade controller for the LFC study considering a three area interconnected system. Initially, each area of the interconnected power system comprising of a thermal generating unit and further to show the impact of renewable energy sources ( RES), a combination of wind generating unit and the solar-thermal generating unit is incorporated along with thermal generating unit in area-1. The study begins with the performance verification of the GOA technique over other techniques. Then to get sure about the selection of objective function, the performance of the GOA-PI controller has been verified by considering two mostly used objective functions i.e. integral time multiplied absolute error and integral square error. Lastly, the performances of the proposed controller have been verified over other controllers for the system with/without incorporating RES.

关键词： fuzzy logic controller grasshopper optimization algorithm load frequency control renewable energy sources

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grasshopper optimization algorithm based improved variational mode decomposition technique for muscle artifact removal in ECG using dynamic time warping

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年 73卷

作者： Malghan, Pavan G. Hota, Malaya Kumar Vellore Inst Technol Sch Elect Engn Dept Commun Engn Vellore 632014 Tamil Nadu India

The removal of unwanted noise from electrocardiogram (ECG) recordings is a difficult procedure in biomedical signal analysis. It alters the signal and affects the accurate interpretation of the signal. A major distortion produced while recording is due to muscle artifact (MA) or electromyographic (EMG) noise. To achieve a high quality ECG recording by eliminating this MA noise, our research proposes a novel filtering technique using the grasshopper optimization algorithm (GOA) based variational mode decomposition (VMD) method with the dynamic time warping (DTW) distance concept. GOA is utilized to identify the best optimal parameters for the VMD process, and then the DTW technique is applied to discover the relevant modes that include MA noise. To successfully remove noise, these modes are denoised using the discrete wavelet transform (DWT) method. ECG signal analysis is performed on a real-time MIT-BIH arrhythmia database and is compared with the performance of recent existing techniques using signal characteristics such as signal-to-noise ratio (SNR), mean square error (MSE), correlation coefficient (CC), and so on. As an outcome of these computations, our proposed technique outperforms those existing methods when it comes to denoising MA noise.

关键词： grasshopper optimization algorithm Variational mode decomposition Dynamic time warping Discrete wavelet transform Muscle artifact

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A Multi-strategy Improved grasshopper optimization algorithm for Solving Global optimization and Engineering Problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2024年第1期17卷 1-28页

作者： Liu, Wei Yan, Wenlv Li, Tong Han, Guangyu Ren, Tengteng Shenyang Ligong Univ Coll Informat Sci & Engn Shenyang 110168 Peoples R China

This paper presents a multi-strategy improved grasshopper optimization algorithm (MSIGOA), which aims to address the shortcomings of the grasshopper optimization algorithm (GOA), including its slow convergence, vulnerability to trapping into local optima, and low accuracy. Firstly, to improve the uniformity of the population distribution in the search space, the MSIGOA uses circle mapping for the population initialization. A nonlinear decreasing coefficient is utilized instead of an original linear decreasing coefficient to improve the local exploitation and global exploration capabilities. Then, the modified golden sine mechanism is added during the position update stage to change the single position update mode of GOA and enhance the local exploitation capability. The greedy strategy is added to greedily select the new and old positions of the individual to retain a better position and increase the speed of convergence. Finally, the quasi-reflection-based learning mechanism is utilized to construct new populations to improve population multiplicity and the capability to escape from the local optima. This paper verifies the efficacy of MSIGOA by comparing it with other advanced algorithms on six engineering design problems, CEC2017 test functions, and 12 classical benchmark functions. The experimental results show that MSIGOA performs better than the original GOA and other compared algorithms and has stronger comprehensive optimization capabilities.

关键词： grasshopper optimization algorithm Circle mapping Nonlinear decreasing coefficient Golden sine algorithm Quasi-reflection-based learning

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LAGOA: Learning automata based grasshopper optimization algorithm for feature selection in disease datasets

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第6期13卷 3175-3194页

作者： Dey, Chiradeep Bose, Rajarshi Ghosh, Kushal Kanti Malakar, Samir Sarkar, Ram Jadavpur Univ Dept Elect & Telecommun Engn Kolkata India Jadavpur Univ Dept Comp Sci & Engn Kolkata India Asutosh Coll Dept Comp Sci Kolkata India

In predictive modelling it is important to use any feature selection methods as irrelevant features when used with powerful classifiers can lead to over-fitting and thus create models which fail to perform as good as when these features are not used. Particularly it is important in case of disease datasets where various features or attributes are available through the patients' medical records and many features in these datasets may not be relevant to the diagnosis of some specific disease. Wrong models in this case can be disastrous and lead to wrong diagnosis, and maybe in extreme cases lead to loss of life. To this end, we have used a wrapper based feature selection model for the said purpose. In recent years, grasshopper optimization algorithm (GOA) has proved its superiority over other optimization algorithms in different research areas. In this paper, we propose an improved version of GOA, called (LAGOA), which uses Learning Automata (LA) for adjusting the parameters of GOA in an adaptive way, and two-phase mutation for enhancing exploitation capability of the algorithm. LA is used for adjusting the parameter values of each grasshopper in the population individually. In two-phase mutation the first phase reduces the number of selected features while preserving high classification accuracy, while the second phase adds relevant features which increase the classification accuracy. Proposed method has been applied to Breast Cancer (Wisconsin), Breast Cancer (Diagnosis), Statlog (Heart), Lung Cancer, SpectF Heart and Hepatitis datasets taken from UCI Machine Learning Repository. Experimental results confirm its superiority over state-of-the-art methods considered here for comparison.

关键词： grasshopper optimization algorithm Learning automata Two-phase mutation Biomedical data Feature selection Cancer data

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Detection of anomaly intrusion utilizing self-adaptive grasshopper optimization algorithm

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NEURAL COMPUTING & APPLICATIONS 2021年第13期33卷 7541-7561页

作者： Shukla, Alok Kumar VIT AP Univ Sch Comp Sci & Engn Amaravati Andhra Pradesh India

Due to continued growth in both cyberattacks and network data size, organizations need to develop advanced ways to keep their networks and data secure the dynamic nature of evolving malicious attacks. Nowadays, large number of security mechanisms are installed in the network but it opens the possibility for adversaries to conduct malicious activity in the computer network. To detect potential attacks, intrusion detection systems are important security tools that can help to increase the security posture of computer network. In order to identify new malicious or anomalous attacks, this study developed an opposition self-adaptive grasshopper optimization algorithm based on mutation and perceptive concept. Moreover, reinforcement learning is utilized in support vector machine, named gain actor critic with support vector machine to increase the detection capabilities by identifying new cyberattacks. Extensive experiments are conducted on standard intrusion detection datasets such as NSL-KDD, AWID and CIC-IDS 2017 to measure the performance of the proposed method. It can more reliably detect and classify modern attacks with high accuracy and low false-positive rate. The comparative simulation results demonstrates that the proposed algorithm is more capable than basic grasshopper optimization algorithm and other used evolutionary techniques in terms of detection rate, false-positive rate and accuracy for solving IDS problems. The proposed model has provided high detection rate of 99.71%, accuracy of 99.23% and low false-positive rate of 0.009 in NSL-KDD with six optimal features;in AWID data, high detection rate of 99.11%, accuracy of 99.15% and low false-positive rate of 0.091 with eight optimal features, and high detection rate of 99.61%, accuracy of 99.35% and low false-positive rate of 0.052 in CIC-IDS 2017 data with eight optimal features.

关键词： Evolutionary algorithm grasshopper optimization algorithm Intrusion detection CIC-IDS 2017 Support vector machine

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Optimal design of CDM controller to frequency control of a realistic power system equipped with storage devices using grasshopper optimization algorithm

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ISA TRANSACTIONS 2020年 97卷 202-215页

作者： Heshmati, Mina Noroozian, Reza Jalilzadeh, Saeid Shayeghi, Hossein Univ Zanjan Dept Elect Engn Zanjan Iran Univ Mohaghegh Ardabili Dept Elect Engn Ardebil Iran

This paper presents a new robust load frequency control (LFC) technique based on an optimal design of the coefficient diagram method (CDM) in a three area thermal power system equipped with redox flow batteries (RFB). In order to emphasize on a realistic power system and obtain an accurate insight, important nonlinearities due to generation rate constraint (GRC), governor dead band (GDB) and time delay (TD) were considered. The innovation of the proposed controller in this paper is the use of a hybrid intelligent combination of a decentralized CDM technique and optimization throughout its algebraic equations. In addition, a new algorithm namely grasshopper optimization algorithm (GOA) was used to find the key parameters of the proposed controller in solving the LFC problem for the first time. Furthermore, this study applied a powerful modified objective function by considering the integral of time multiplied squared error (ITSE) criteria for both controller input signal (ACE), to minimize the area control error and output signal (Delta u) to reduce the size of the actuator, settling time (Ts) to have a faster response and a function to increase the minimum damping ratio (MDR) among all eigen values. To demonstrate the effectiveness of the proposed scheme, the studied simulated power system was tested through different cases including a large step and sinusoidal load perturbations and wide uncertainty in dynamic parameters of a nonlinear power system. Comparative results have revealed the superiority of the optimal CDM technique, especially when it is equipped with RFB. In addition to graphical results, by taking into account the MDR of different control strategies, the preference of the proposed controller has become more validated. This newly developed strategy leads to a flexible and accurate controller with a powerful mathematical back up which can successfully cope with GRC, GDB and TD nonlinearities in perturbed uncertain power systems and provide fast, stable

关键词： Optimal coefficient diagram method RFB LFC grasshopper optimization algorithm GRC GDB Time delay

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A grasshopper optimization algorithm for optimal short-term hydrothermal scheduling

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ENERGY REPORTS 2021年 7卷 314-323页

作者： Zeng, Xie Hammid, Ali Thaeer Kumar, Nallapaneni Manoj Subramaniam, Umashankar Almakhles, Dhafer J. Baoji Univ Arts & Sci Sch Comp Sci Baoji 721000 Peoples R China Univ Pendidikan Sultan Idris Fac Arts Comp & Creat Ind Dept Comp Tanjong Malim Malaysia Imam Jaafar Al Sadiq Univ Fac Informat Technol Comp Engn Tech Dept Baghdad Iraq City Univ Hong Kong Sch Energy & Environm Kowloon Hong Kong Peoples R China Prince Sultan Univ Coll Engn Dept Commun & Networks Renewable Energy Lab Riyadh 12435 Saudi Arabia

The optimal generation for short-term hydrothermal scheduling (OGStHS) with the deliberation of various purposes is a complex non-linear constrained optimization problem. There exist numerous constraints, which make the OGStHS optimization problem more complicated. The considered constraints for this problem are mostly related to energy performance, operational conditions, water, and power infrastructure. All these constraints would generally influence the cost of fuel. In this study, a multi-objective optimization form of OGStHS is suggested to estimate the minimum cost of fuel, which mainly influences industrial operation. The water transfer delays among multi-related reservoirs and the thermal plants' valve-point influences are considered for the accurate formulation of the OGStHS problem. Meantime, a grasshopper optimization algorithm (GOA) is performed to handle the OGStHS problem by getting optimized for both objectives concurrently. A modern approach is shown in this study to get a solution to the OGStHS problem. Furthermore, to deal with the complex restraints efficiently, modern heuristic restriction treatment processes with no drawback impact frames have been offered in this study. Two hydrothermal power systems have illustrated the suggested GOA technique's utility and performance. Compared with other available approaches, the analytical results are admitted that GOA can provide a better understanding by decreasing fuel cost and emission concurrently. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： Thermal power plants Co-ordinated hydrothermal Hydrothermal constraint Water transfer delays Valve-point influences grasshopper optimization algorithm

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Training Neural Networks by Enhance grasshopper optimization algorithm for Spam Detection System

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IEEE ACCESS 2021年 9卷 116768-116813页

作者： Ghaleb, Sanaa A. A. Mohamad, Mumtazimah Fadzli, Syed Abdullah Ghanem, Waheed Ali H. M. Univ Sultan Zainal Abidin Fac Informat & Comp Kuala Terengganu 22200 Malaysia Univ Malaysia Terengganu Fac Ocean Engn Technol & Informat Kuala Terengganu 21030 Malaysia Univ Aden Fac Engn Aden Yemen Univ Aden Fac Educ Aden & Saber Aden Yemen

A significant negative impact of spam e-mail is not limited only to the serious waste of resources, time, and efforts, but also increases communications overload and cybercrime. Perhaps the most damaging aspect of spam email is that it has become such a major tool for attacks of cross-site scripting, malware infection, phishing, and cross-site request forgery, etc. Due to the nature of the adaptive unsolicited spam, it has been weakening the effect of the previous discovery techniques. This article proposes a new Spam Detection System (SDS) framework, by using a series of six different variants of enhanced grasshopper optimization algorithm (EGOAs), which are investigated and combined with a Multilayer Perceptron (MLP) for the purpose of advanced spam email detection. In this context, the combination of MLP and EGOAs produces Neural Network (NN) models, referred to (EGOAMLPs). The main idea of this research is to use EGOAs to train the MLP to classify the emails as spam and non-spam. These models are applied to SpamBase, SpamAssassin, and UK-2011 Webspam benchmark datasets. In this way, the effectiveness of our models on detecting diverse forms of spam email is evidenced. The results showed that the proposed MLP model trained by EGOAs achieves a higher performance compared to other optimization methods in terms of accuracy, detection rate, and false alarm rate.

关键词： Unsolicited e-mail Filtering Classification algorithms optimization Training Sociology Convergence Metaheuristic grasshopper optimization algorithm classification artificial neural network multilayer perceptron spam email spam detection system

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