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A novel method to solve visual tracking problem: hybrid algorithm of grasshopper optimization algorithm and differential evolution

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EVOLUTIONARY INTELLIGENCE 2022年第1期15卷 785-822页

作者： Reddy, K. Narsimha Bojja, Polaiah Koneru Lakshmaiah Educ Fdn ECE Dept Hyderabad 522502 Andhra Pradesh India

To provide effective resolutions for complex real-life problems and other optimization problems, abundant, various procedures have been presented in the last few decades. This paper proposes a simple but efficient hybrid evolutionary algorithm called GOA-DE for solving visual tracking problems. In the proposed hybrid algorithm, grasshopper optimization algorithm (GOA) operates in refining the vector. In contrast, the Differential Evolution (DE) algorithm is used for transforming the decision vectors based on genetic operators. The improvement in maintaining the balance between exploration and exploitation abilities is made by incorporating genetic operators, namely, mutation and crossover in GOA. The success of GOA-DE is estimated by 23 classical benchmark functions, CEC05 functions, and CEC 2014 functions. The GOA-DE algorithm results prove that it is very viable associated with the metaheuristic up-to-date procedures. Similarly, visual tracking problems are resolved by the GOA-DE algorithm as a real challenging case study. Visual tracking several objects robustly in a video stream with complex backgrounds and objects are beneficial in subsequent generation computer vision structures. But, in exercise, it is problematic to plan an effective video-based visual tracking scheme owing to the fast-moving objects, probable occlusions, and diverse light circumstances. Investigational outcomes indicate that the GOA-DE-based tracker can energetically track a random target in many thought-provoking cases.

关键词： Global optimization grasshopper optimization algorithm Differential evolution Visual tracking GOA-DE

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A hybrid model of ARIMA and MLP with a grasshopper optimization algorithm for time series forecasting of water quality

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SCIENTIFIC REPORTS 2024年第1期14卷 1-21页

作者： Su, Jie Lin, Ziyu Xu, Fengwei Fathi, Gholamreza Alnowibet, Khalid A. Chinese Res Inst Environm Sci Basin Res Ctr Water Pollut Control Beijing 100012 Peoples R China Chinese Res Inst Environm Sci Key Lab Estuarine & Coastal Environm Beijing 100012 Peoples R China Ankara Yildirim Beyazit Univ AYBU Dept Ind Engn TR-06010 Ankara Turkiye King Saud Univ Coll Sci Stat & Operat Res Dept Riyadh 11451 Saudi Arabia

Water quality monitoring of rivers is necessary in order to properly manage their basins so that steps can be taken to control the amount of pollutants and bring them to the allowable level. The ARIMA (autoregressive integrated moving average) model does not consider nonlinear patterns in modeling water quality components. Also, in modeling using the MLP (Multilayer Perceptrons) model, both linear and nonlinear pattern are not controlled equally. Therefore, in the present study, linear time series models (ARIMA), MLP model, and a hybrid model of MLP and ARIMA optimized by a grasshopper optimization algorithm are used to predict water quality components in the statistical period of 2011-2019. In the proposed hybrid method, the ability of the ARIMA and the MLP model are exploited. Observational water quality data for forecasting time series in the hybrid method include dissolved oxygen, water temperature, and boron over 108 months. Since, the hybrid model is capable of realizing the nonlinear essence of complicated time series, it makes more reliable forecasts. In the hybrid model, the correlation coefficients between the observational data and the predicted values are 0.9 for dissolved oxygen, 0.91 for water temperature, and 0.91 for boron. To compare the three ARIMA, MLP, and hybrid models, the accuracy indices of each model are calculated. The results show that the hybrid model's higher accuracy compared with the other two models.

关键词： ARIMA MLP Water quality grasshopper optimization algorithm Forecasting

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A grasshopper optimization algorithm-based movie recommender system

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第18期83卷 54189-54210页

作者： Ambikesh, G. Rao, Shrikantha S. Chandrasekaran, K. Natl Inst Technol Surathkal 575025 Karnataka India

A movie recommendation system functions as a specialized information system, providing users with personalized suggestions aligned with their movie preferences. Employing advanced algorithms and data analysis methods, these systems scrutinize variables such as users' viewing history and preferences to formulate personalized recommendations. Our proposed methodology, termed GOA-k-means, amalgamates the grasshopper optimization algorithm (GOA) with k-means clustering to navigate the dynamic nature of user preferences. Facilitating real-time calibration, GOA-k-means yields recommendations that adapt to users' shifting interests. We developed our model utilizing a dataset of one million records from Movielens, pre-processed via z-score normalization and subjected to Principal Component Analysis (PCA) for feature extraction. In comparison to conventional techniques, GOA-k-means demonstrated superior performance in metrics such as precision, recall, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), establishing itself as a valuable tool for augmenting user engagement in the entertainment industry.

关键词： grasshopper optimization algorithm Recommender Systems Filtering K-means Movie

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Predicting Surface Ozone Levels in Eastern Croatia: Leveraging Recurrent Fuzzy Neural Networks with grasshopper optimization algorithm

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WATER AIR AND SOIL POLLUTION 2024年第10期235卷 655页

作者： Braik, Malik Sheta, Alaa Kovac-Andric, Elvira Al-Hiary, Heba Aljahdali, Sultan Elashmawi, Walaa H. Awadallah, Mohammed A. Al-Betar, Mohammed Azmi Suez Canal Univ Fac Comp & Informat Dept Comp Sci Ismailia Egypt Misr Int Univ Fac Comp Sci Dept Comp Sci Cairo Egypt Al Balqa Appl Univ Dept Comp Sci Salt Jordan Southern Connecticut State Univ Dept Comp Sci New Haven CT USA Univ JJ Strossmayer Dept Chem Osijek Croatia Al Balqa Appl Univ Dept Comp Informat Syst Salt Jordan Taif Univ Comp Sci Dept Taif Saudi Arabia Al Aqsa Univ Dept Comp Sci Gaza Palestine Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates

Urban air pollution, a combination of industry, traffic, forest burning, and agriculture pollutants, significantly impacts human health, plants, and economic growth. Ozone exposure can lead to mortality, heart attacks, and lung damage, necessitating the creation of complex environmental safety regulations by forecasting ozone concentrations and associated pollutants. This study proposes a hybrid method, RFNN-GOA, combining recurrent fuzzy neural network (RFNN) and grasshopper optimization algorithm (GOA) to estimate and forecast the daily ozone (O3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_3$$\end{document}) in specific urban areas, specifically Kopa & ccaron;ki Rit and Osijek city in Croatia, aiming to improve air quality, human health, and ecosystems. Due to the intricate structure of atmospheric particles, modeling of O3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_3$$\end{document} likely poses the biggest challenge in air pollution today. The dataset used by the proposed RFNN-GOA model for the prediction of O3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_3$$\end{document} concentrations in each explored area consists of the following air pollutants, NO, NO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document}, CO, SO2\docu

关键词： Recurrent fuzzy neural network grasshopper optimization algorithm Multiple linear regression Auto-regressive integrated moving average

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Digital Watermarking using grasshopper optimization algorithm

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OPEN COMPUTER SCIENCE 2021年第1期11卷 330-336页

作者： Sharma, Satender Chauhan, Usha Khanam, Ruqaiya Singh, Krishna Kant Galgotias Univ Sch Elect & Elect Commun Engn Greater Noida India Jain Deemed Univ Fac Engn & Technol Bengaluru India

The advancement in computer science technology has led to some serious concerns about the piracy and copyright of digital content. Digital watermarking technique is widely used for copyright protection and other similar applications. In this paper, a technique for digital watermarking based on Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and grasshopper optimization algorithm (GOA) is proposed. The method computes the DWT of the cover image to obtain the sub-components of the image. The subcomponent is converted to frequency domain using DCT. The challenge is to find the optimal scaling factor to be used for watermarking. The authors have designed a GOA based technique that finds the optimized scaling factor and the coefficient for embedding the watermark. GOA makes the watermark undetectable and is invisible in the cover image. The watermark image is embedded in the cover image using these coefficients. The extraction of watermark from the cover image is done by using inverse DCT and DWT. The proposed method is compared with the other state of the art methods. The effectiveness of the proposed method is computed using Peak Signal to Noise Ratio (PSNR), Normalized Cross Correlation (NCC) and Image Fidelity (IF). The proposed method out-performs the other methods and can be effectively used for practical digital watermarking.

关键词： Digital Watermarking grasshopper optimization algorithm Discrete Cosine Transform Discrete Wavelet Transform

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Using the grasshopper optimization algorithm for Fuzzy Classifier Design

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AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS 2023年第6期57卷 333-349页

作者： Ostapenko, R. O. Hodashinsky, I. A. Shurygin, Yu. A. Tomsk State Univ Control Syst & Radioelect Tomsk Russia

The paper describes three stages in the construction of a fuzzy classifier. The first refers to the formation of fuzzy rules, the second stage is feature selection, and the third stage is optimization of membership functions parameters. The influence of clustering methods on the efficiency of the formed fuzzy classifier rules was estimated by three different fitness functions. These functions were total variance, the Davies-Bouldin index, and the Calinski-Harabasz index. The grasshopper optimization algorithm was binarized using S- and V-shaped transformation functions for feature selection. The constructed classifiers have been tested on datasets from the KEEL repository.

关键词： fuzzy classifier metaheuristics grasshopper optimization algorithm rule database feature selection

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A new fuzzy clustering-based recommendation method using grasshopper optimization algorithm and Map-Reduce

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2022年第5期13卷 2698-2709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, an eminent approach of recommendation, finds the similarity in the data records to recommend the items. The performance of collaborative based recommendation depends on the effectiveness of clustering algorithm used for finding the similarity, especially in big data environment. This paper presents a novel fuzzy clustering based recommendation method using improved grasshopper optimization and MapReduce. The proposed method employs an improved variant of grasshopper optimization algorithm to find the optimal cluster centroids. Moreover, the proposed method runs in the MapReduce environment to handle big dataset. To experimentally validate the proposed variant, twenty-three benchmark functions are considered and compared against four other existing methods in terms of mean fitness value, standard deviation and Friedman test. Further, clustering efficacy of proposed method is also vindicated on four large-scale benchmark clustering dataset. Finally, to test the recommendation ability, the proposed method is tested on MovieLens dataset and results are validated using three performance parameters over the different number of clusters. The simulation clearly indicates that the proposed method can be effectively utilized to recommendations of items in a big data environment.

关键词： Collaborative recommendation MapReduce Fuzzy Clustering Big Data grasshopper optimization algorithm

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Optimal Scheduling of Active Distribution Networks with High Penetration of Plug-in Electric vehicles and Renewables Using grasshopper optimization algorithm 29

Optimal Scheduling of Active Distribution Networks with High...

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29th Iranian Conference on Electrical Engineering (ICEE)

作者： Mousavi, Seyyed Hadi Janatifar, Varahram Abdolahi, Arya Sarhangzadeh, Mitra Islamic Azad Univ Tabriz Branch Dept Elect Engn Tabriz Iran Azarbaijan Shahid Madani Univ Dept Elect Engn Tabriz Iran

ISBN: (纸本)9781665433655

With the development use of the plug-in hybrid electric vehicles (PHEV) as small moving power plants, energy management of these types of power plants requires more researches. So, the charging and discharging capability of PHEVs can be used in active distribution networks (ADN). This paper suggests an optimal approach for charging and discharging scheduling of PEVs in the ADNs to minimize the operating costs of system. The influence of PEVs on the operating costs and some technical characteristics of system like power losses and voltage profile has been investigated under PEVs limits, distributed generation units. In this work, the grasshopper optimization algorithm (GOA) is applied to solve this complicated mixed-integer non-linear programming problem with the goal of minimizing the operating costs of the system and find the optimal solutions. In the end, an illustrative case study is provided to acknowledge the sufficiency and proficiency of the presented model by implementing it on the modified IEEE 69-bus system.

关键词： Active distribution network Renewable energy sources grasshopper optimization algorithm Plug-in electric vehicle

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Using grasshopper optimization algorithm to Solve 0-1 Knapsack Computation Resources Allocation Problem in Mobile Edge Computing 34

Using Grasshopper Optimization Algorithm to Solve 0-1 Knapsa...

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34th Chinese Control and Decision Conference (CCDC)

作者： Su, Zijun He, Guannan Li, Zhonghua Guangdong Univ Technol Coll Automat Guangzhou Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China Sun Yat Sen Univ Sch Elect & Infomat Technol Guangzhou Peoples R China

ISBN: (纸本)9781665478960

In a mobile edge computing (MEC) system, the MEC server can't provide sufficient computation resources to support the offloading operations of all concurrent tasks from many mobile devices. In this ease, the computation resources of MEC server should he allocated to those important tasks. This paper establishes a computation resources allocation model based on 01 knapsack problem, and proposes a grasshopper optimization algorithm for computation resources allocation (GOA-CRA). On the basis of the novel binary grasshopper optimization algorithm (NBGOA), the proposed GOA-CRA adds a position correction step where three strategies (i.e., random correction, maximum correction and minimum correction) are realized, respectively. A series of numerical experiments are investigated among these three correction strategies. The results indicate that the proposed GOA-CRA is an effective and efficient algorithm for computation resources allocation problem in MEC system.

关键词： Mobile edge computing grasshopper optimization algorithm Computation resources allocation 0-1 knapsack problem

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Multi-Objective Optimal Renewable Distributed Generator Integration in Distribution Systems Using grasshopper optimization algorithm Considering Overcurrent Relay Indices 9

Multi-Objective Optimal Renewable Distributed Generator Inte...

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9th International Conference on Modern Power Systems (MPS)

作者： Belbachir, Nasreddine Zellagui, Mohamed Settoul, Samir El-Bayeh, Claude Ziad Univ Mostaganem Dept Elect Engn Mostaganem Algeria Univ Batna 2 Dept Elect Engn Batna Algeria Mentouri Univ Constantine 1 Dept Electrotech Constantine Algeria Concordia Univ Canada Excellence Res Chairs Team Montreal PQ Canada

ISBN: (纸本)9781665433815

The endless problem of energy supplies is always floating on the surface. As a result, there is a daily improvement to optimize power generators and power system configuration. Renewable Distributed Generators (RDGs) are at the heart of these developments. The optimal placement and sizing of RDG sources is a recent problem in the Distribution Power System (DPS) that is usually solved using various algorithms due to its high complexity. A new optimization algorithm called grasshopper optimization algorithm (GOA) has been implemented to optimize different types of RDG units for obtaining an optimum allocation of multiple Photovoltaic Distributed Generator (PVDG) and Wind Turbine Distributed Generator (WTDG) units in DPS. This paper proposed new Multi-Objective Functions (MOF) that are considered to minimize simultaneous the total of Active Power Loss Index (APLI), the Voltage Deviation Index (VDI), the Operation Time Index (OTI), and enhance the Coordination Time Interval Index (CTII) of the primary and backup overcurrent protection relays installed in DPS. The proposed algorithm is tested on a standard IEEE 33-bus distribution power system. The numerical simulation including comparative studies was presented to demonstrate the performance of the GOA and compared to that of the Particle Swarm optimization (PSO) algorithm, Artificial Bee Colony (ABC) algorithm, and Sine Cosine algorithm (SCA).

关键词： Renewable distributed generation Photovoltaic energy Wind turbine generator Distribution power system Optimal integration Overcurrent relay indices grasshopper optimization algorithm

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