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A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India

ENERGY 2018年 145卷 710-720页

作者： Barman, Mayur Choudhury, N. B. Dev Sutradhar, Suman Natl Inst Technol Dept Elect Engn Silchar 788010 Assam India

In today's restructuring electricity market, short-term load forecasting (STLF) is an essential tool for the electricity utilities to predict future scenario and act towards a profitable policy. The electric load demand is highly influenced by the thermal inertia due to the climatic factors. These influential climatic factors are different in different regions. Therefore, it is necessary to have a region specific STLF model for load forecasting under regional climatic conditions. This paper proposes a regional hybrid STLF model utilizing SVM with a new technique, called grasshopper optimization algorithm (GOA), to evaluate its suitable parameters. This study is carried out in Assam, a state of India and proposed GOA-SVM model is targeted for forecasting the load under local climatic conditions. The proposed model uses the similar day approach (SDA) to satisfy the regional climatic requirements. The results of the proposed model show better accuracy comparing to results generated with classical STLF model of incorporating temperature universally as the only climatic factor. To further affirm the efficacy of the proposed model, same inputs are delivered in two alternative hybrid models, namely GA-SVM (GA with SVM) and PSO-SVM (PSO with SVM). The results indicate that the proposed model outperforms the other hybrid models. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Short-term load forecasting Regional climatic requirement grasshopper optimization algorithm Support vector machine Similar day approach

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Artificial neural network tuned PID controller for LFC investigation including distributed generation

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2020年第5期33卷

作者： Debnath, Manoj K. Agrawal, Ramachandra Tripathy, Smruti Rekha Choudhury, Shreeram Siksha O Anusandhan Deemed Be Univ Dept Elect Engn Bhubaneswar 751030 Odisha India

To facilitate the frequency regulation, here an adaptive artificial neural network (ANN) tuned proportional-integral-derivative (PID) controller is suggested for load frequency control (LFC) investigation in a system with distributed generation (DG) resources. The various DG resources include wind turbine generators (WTG), battery energy storage system (BESS), aqua electrolyzer (AE), diesel engine generators (DEG), and fuel cell (FC). Initially, an isolated thermal generating system is considered with DG. Then an interconnected two-area thermal power system with DG is considered for LFC analysis. The implemented PID controller parameters are achieved using two methodologies. In the first case, the PID controller parameters are tuned by a recent optimization technique known as grasshopper optimization algorithm (GOA). In the second case, the PID controller parameters are tuned by an ANN. The dynamic behavior of the two categories of the system is inspected with GOA tuned PID controller and ANN tuned PID controller and it is established that ANN tuned PID controller exhibits superior performance as compared to GOA tuned PID controller in terms of time-based performance evaluative factors such as minimum undershoots, settling time and maximum overshoots. Also, the robustness of the recommended ANN tuned PID controller is verified by applying random loading in the system.

关键词： artificial neural network distributed generation grasshopper optimization algorithm load frequency control PID controller renewable sources

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Incorporating evolutionary computation for securing wireless network against cyberthreats

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JOURNAL OF SUPERCOMPUTING 2020年第11期76卷 8691-8728页

作者： Dwivedi, Shubhra Vardhan, Manu Tripathi, Sarsij NIT Raipur Dept Comp Sci & Engn Raipur Chhattisgarh India

Due to the rapid growth of internet services, the demand for protection and security of the network against sophisticated attacks is continuously increasing. Nowadays, in network security, an intrusion detection system (IDS) plays an important role to detect intrusive activity. With the purpose of reducing the search dimensionality and enhancing classification performance of IDS model, in the literature several hybrid evolutionary algorithms have been investigated to tackle anomaly detection problems, but they have few drawbacks such as poor diversity, massive false negative rate, and stagnation. To resolve these limitations, in this study, we introduce a new hybrid evolutionary algorithm combining the techniques of grasshopper optimization algorithm (GOA) and simulated annealing (SA), called GOSA for IDS that extracts the most noteworthy features and eliminates irrelevant ones from the original IDS datasets. In the proposed method, SA is integrated into GOA, while utilizing it to increase the solution quality after each iteration of GOA. Support vector machine is used as a fitness function in the proposed method to select relevant features which can help to classify attacks accurately. The performance of the proposed method is evaluated on two IDS datasets such as NSL-KDD and UNSW-NB15. From experimental results, we observe that the proposed method outperforms existing state-of-the-art methods and attains high detection rate as 99.86%, an accuracy as 99.89%, and low false alarm rate as 0.009 in NSL-KDD and high detection rate as 98.85%, an accuracy as 98.96%, and low false alarm rate as 0.084 in UNSW-NB15.

关键词： Intrusion detection system Simulated annealing Evolutionary algorithms grasshopper optimization algorithm

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Modified grasshopper algorithm-Based Multilevel Thresholding for Color Image Segmentation

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IEEE ACCESS 2019年 7卷 11258-11295页

作者： Liang, Hongnan Jia, Heming Xing, Zhikai Ma, Jun Peng, Xiaoxu Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Heilongjiang Peoples R China

Multilevel thresholding is an important approach for image segmentation which has drawn much attention during the past few years. The Tsallis entropy method is implemented for its effectiveness and simplicity. Although it is efficient and gives an excellent result in the case of bi-level thresholding, its evaluation becomes complexity when the number of thresholds increases. To overcome the problem, the metaheuristic algorithms are applied in this search area for searching the optimal thresholds. In this paper, a modified grasshopper optimization algorithm (GOA) is adopted to render multilevel Tsallis cross entropy more practical and reduce the complexity. The Levy flight algorithm is employed to modify the original GOA and balance the exploration and exploitation of the GOA. Experiments are conducted between five state-ofthe-art metaheuristic algorithms and the proposed one. In addition, the proposed approach is compared with thresholding techniques depending on between-class variance (Otsu) method and the Renyi entropy function. Both real life images and plant stomata images are used in the experiments to test the performance of the algorithms involved. Qualitative experimental results show that the proposed segmentation approach has a fewer iterations and a higher segmentation accuracy.

关键词： Multi-threshold color image segmentation Tsallis entropy method grasshopper optimization algorithm Levy flight

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A new hybrid model to predict the electrical load in five states of Australia

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ENERGY 2019年 166卷 598-609页

作者： Wu, Jinran Cui, Zhesen Chen, Yanyan Kong, Demeng Wang, You-Gan Queensland Univ Technol Sch Math Sci Brisbane Qld 4001 Australia Changzhi Univ Dept Comp Sci Changzhi 046000 Shanxi Peoples R China Taobao China Software Co Ltd Hangzhou 311121 Zhejiang Peoples R China Lanzhou Univ Sch Math & Stat Lanzhou 730000 Gansu Peoples R China

Short-term electrical load forecasting is an important part in the management of electrical power because electrical load is an extreme, complex non-linear system. To obtain parameter values that provide better performances with high precision, this paper proposes a new hybrid electrical load forecasting model, which combines ensemble empirical mode decomposition, extreme learning machine, and grasshopper optimization algorithm for short-term load forecasting. The most important difference that distinguishes this electrical load forecasting model from other models is that grasshopper optimization can search suitable parameters (weight values and threshold values) of extreme learning machine, while traditional parameters are selected randomly. It is applied in Australia electrical load prediction to show its superiority and applicability. The simulation studies are carried out using a data set collected from five main states (New South Wales, Queensland, Tasmania, South Australia and Victoria) in Australia from February 1 to February 27, 2018. Compared with all considered basic models, the proposed hybrid model has the best performance in predicting electrical load. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Electrical load Forecast Ensemble empirical mode decomposition Extreme learning machine grasshopper optimization algorithm

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A novel formulation for predicting the shear strength of RC walls using meta-heuristic algorithms

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NEURAL COMPUTING & APPLICATIONS 2024年第15期36卷 8727-8756页

作者： Parsa, Payam Naderpour, Hosein Ezami, Nima Semnan Univ Fac Civil Engn Semnan Iran Toronto Metropolitan Univ Dept Civil Engn Toronto ON Canada Univ Toronto Dept Civil & Mineral Engn Toronto ON Canada GEI Consultants Inc Markham ON Canada

Reinforced concrete (RC) shear walls play a pivotal role in resisting seismic and lateral loads within structural frameworks. A thorough examination of the existing literature was undertaken, covering a range of experimental and theoretical studies related to the design of RC shear walls. It was emphasized that comprehending shear failure behavior and precisely predicting the shear strength of RC walls holds considerable significance. To address this, the study proposes two models that integrate the support vector regression method with meta-heuristic optimization algorithms (Bat and GOA), utilizing 228 sets of experimental data. In identifying the parameters influencing the shear strength of RC shear walls, the study focused on eight influential factors. The comparison of the two proposed models in the current research with existing models and experimental data demonstrated their commendable accuracy, surpassing the performance of suggested empirical formulations. The prediction errors associated with the proposed models, when compared to experimental data, were notably low. An innovative approach was introduced in the research, presenting a novel method for predicting shear strength using the support vector regression method and the Bat optimization algorithm. A notable advantage of this formulation lies in its capacity to predict the shear strength across various configurations, including squat, cylindrical, and thin RC shear walls. Unlike some existing equations for predicting shear strength, this formulation exhibits no limitations. Through a comparative analysis with established equations, the computational framework's results suggest its successful applicability in building codes and construction practices. The proposed method contributes to the accurate prediction of shear strength in diverse RC shear wall configurations, offering a valuable tool for structural engineering applications.

关键词： Reinforced concrete (RC) shear walls Shear strength Support vector regression Bat optimization algorithm grasshopper optimization algorithm SVR-Bat SVR-GOA

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Attack detection in medical Internet of things using optimized deep learning: enhanced security in healthcare sector

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DATA TECHNOLOGIES AND APPLICATIONS 2021年第5期55卷 682-714页

作者： Santhi, J. Aruna Saradhi, G. Vijaya KL Univ Guntur Andhra Pradesh India Sri Nidhi Inst Technol Hyderabad India

Purpose This paper tactics to implement the attack detection in medical Internet of things (IoT) devices using improved deep learning architecture for accomplishing the concept bring your own device (BYOD). Here, a simulation-based hospital environment is modeled where many IoT devices or medical equipment are communicated with each other. The node or the device, which is creating the attack are recognized with the support of attribute collection. The dataset pertaining to the attack detection in medical IoT is gathered from each node that is considered as features. These features are subjected to a deep belief network (DBN), which is a part of deep learning algorithm. Despite the existing DBN, the number of hidden neurons of DBN is tuned or optimized correctly with the help of a hybrid meta-heuristic algorithm by merging grasshopper optimization algorithm (GOA) and spider monkey optimization (SMO) in order to enhance the accuracy of detection. The hybrid algorithm is termed as local leader phase-based GOA (LLP-GOA). The DBN is used to train the nodes by creating the data library with attack details, thus maintaining accurate detection during testing. Design/methodology/approach This paper has presented novel attack detection in medical IoT devices using improved deep learning architecture as BYOD. With this, this paper aims to show the high convergence and better performance in detecting attacks in the hospital network. Findings From the analysis, the overall performance analysis of the proposed LLP-GOA-based DBN in terms of accuracy was 0.25% better than particle swarm optimization (PSO)-DBN, 0.15% enhanced than grey wolf algorithm (GWO)-DBN, 0.26% enhanced than SMO-DBN and 0.43% enhanced than GOA-DBN. Similarly, the accuracy of the proposed LLP-GOA-DBN model was 13% better than support vector machine (SVM), 5.4% enhanced than k-nearest neighbor (KNN), 8.7% finer than neural network (NN) and 3.5% enhanced than DBN. Originality/value This paper adopts a hybrid algo

关键词： Medical Internet of things Bring your own device Deep belief network grasshopper optimization algorithm Spider monkey optimization Local leader phase-based grasshopper optimization algorithm

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Comprehensive Technical Review of Recent Bio-Inspired Population-Based optimization (BPO) algorithms for Mobile Robot Path Planning

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IEEE ACCESS 2024年 12卷 20942-20961页

作者： Saleh, Izzati Borhan, Nuradlin Yunus, Azan Rahiman, Wan Univ Sains Malaysia Sch Elect & Elect Engn Engn Campus Nibong Tebal 14300 Pulau Pinang Malaysia CL Solut Sdn Bhd Wisma Rampai Kuala Lumpur 59200 Malaysia Univ Sains Malaysia Cluster Smart Port & Logist Technol COSPALT Nibong Tebal 14300 Malaysia Daffodil Int Univ Dept Comp Sci & Engn Daffodil Robot Lab Dhaka 1216 Bangladesh

Over recent decades, the field of mobile robot path planning has evolved significantly, driven by the pursuit of enhanced navigation solutions. The need to determine optimal trajectories within complex environments has led to the exploration of diverse path planning methodologies. This paper focuses on a specific subset: Bio-inspired Population-based optimization (BPO) methodologies. BPO methods play a pivotal role in generating efficient paths for path planning. Amidst the abundance of optimization approaches over the past decade, only a fraction of studies has effectively integrated these methods into path planning strategies. This paper's focus is on the years 2014-2023, reviewing BPO techniques applied to mobile robot path planning challenges. Contributions include a comprehensive review of recent BPO methods in mobile robot path planning, along with an experimental methodology to compare them under consistent conditions. This encompasses the same environment, initial conditions, and replicates. A multi-objective function is incorporated to evaluate optimization methods. The paper delves into key concepts, mathematical models, and algorithm implementations of examined optimization techniques. The experimental setup, methodology, and benchmarking performance results are discussed. Based on the proposed experimental methodology, Improved Sparrow Search algorithm (ISpSA) shows the best cost improvement percentage (7.87%), but suffers in terms of optimization time. On the other hand, Whale optimization algorithm (WOA) has lesser improvement percentage of 6.05% but better optimization time. In conclusion, the standardized approach for benchmarking BPO algorithms provides useful insights into their strengths and challenges in mobile robot path planning.

关键词： optimization methods Path planning Mathematical models Whales Spirals Mobile robots Bio-inspired computing Metaheuristics Particle swarm optimization Whale optimization algorithms grasshopper optimization algorithm grey wolf optimization metaheuristic optimization path planning salp swarm optimization sparrow search optimization whale optimization

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Optimal feed forward neural network based automatic moving vehicle detection system in traffic surveillance system

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第25-26期79卷 18591-18610页

作者： Smitha, J. A. Rajkumar, N. AMC Engn Coll Bangalore 560083 Karnataka India Hindusthan Coll Engn & Technol Coimbatore 641032 Tamil Nadu India

In intelligent transportation system, traffic surveillance is an important topic. One challenging problem for complex urban traffic surveillance system is robust vehicle detection and tracking. Therefore, in this paper, we develop a two-stage approach for moving vehicle detection system. The proposed system mainly consists of two stages such as hypothesis generation (HG) and hypothesis verification (HV). In the first step, we generate the hypotheses using shadows under vehicles is darker than road region concept. In the second step, we verify hypotheses generated in the first step whether correct or not using optimal feedforward neural network (OFFNN). Here, to extract vehicle features, we utilize two types of histogram orientation gradients descriptors (HOG). In training stage, the histogram orientation gradients features are given to the OFFNN classifier. The weights corresponding FFNN is optimally select using improved grasshopper optimization algorithm (IGOA). The experimental results show that the proposed moving vehicle detection system performs better accuracy compare to other methods.

关键词： Traffic surveillance system Moving vehicle detection Tracking Hypothesis generation Hypothesis verification Feedforward neural network grasshopper optimization algorithm

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A novel collaborative filtering based recommendation system using exponential grasshopper algorithm

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EVOLUTIONARY INTELLIGENCE 2023年第2期16卷 621-631页

作者： Singh, Viomesh Kumar Sabharwal, Sangeeta Gabrani, Goldie Netaji Subhas Inst Technol Delhi India Netaji Subhas Univ Technol Delhi India Vivekananda Inst Profess Studies Coll Engn Tech Campus Delhi India

Collaborative filtering recommends items on the principle of similarity. The effectiveness of the similarity is highly dependent on the performance of the clustering algorithm. This paper presents a new clustering-based collaborative filtering method for efficient item recommendation. The new approach introduces a new variant of the grasshopper optimization algorithm to determine optimal similarity patterns. Nine nature-inspired algorithms have been considered and tested against seventeen standard benchmark functions in terms of mean fitness value and the Friedman test to validate the proposed variant experimentally. Further, the efficacy of the new clustering-based collaborative filtering method is tested on the publicly available MovieLens dataset and evaluated in terms of mean absolute error, precision, and recall over the different cluster settings. The obtained results are compared against nine clustering-based collaborative filtering methods. Experiments affirm that the proposed method is a permissive technique for the recommendation on large-scale datasets.

关键词： Recommendation system Collaborative filtering Clustering method grasshopper optimization algorithm

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