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Novel variants of grasshopper optimization algorithm to solve numerical problems and demand side management in smart grids

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第10期56卷 10679-10732页

作者： Badr, Azzahraa A. Saafan, Mahmoud M. Abdelsalam, Mohamed M. Haikal, Amira Y. Mansoura Univ Fac Engn Comp & Control Syst Engn Dept Mansoura Egypt

The grasshopper optimization algorithm (GOA), which is one of the recent metaheuristic optimization algorithms, mimics the natural movements of grasshoppers in swarms seeking food sources. Some deficiencies have existed in the original GOA such as slow convergence speed, and the original GOA may get quickly stuck into local solutions facing some complex. For tackling these drawbacks of the original GOA, enhanced versions of GOA have been proposed to deal with the optimization problems more effectively. In the current study, two strategies have been integrated into GOA: the grouping mechanism of non-linear 'c' parameters and the mutation mechanism. Moreover, two different groups of non-linear 'c' parameters have been suggested in the grouping mechanism. Incorporating the grouping mechanism into GOA can update the grasshoppers' positions within a limited local area, whereas the diversity of agents can be improved by integrating the mutation mechanism. Eight Novel-Variants GOA (NVGOAs) are proposed to address the deficiencies of the original GOA. Where two variants NVGOA1_1 and NVGOA2_1 represent the impact of each proposed group of 'c' parameters. Another two variants NVGOA3 and NVGOA4 represent the impact of the mutation mechanism with two different values of probability. Moreover, four variants: NVGOA1_2, NVGOA1_3, NVGOA2_2, and NVGOA2_3 represent the combination of the two proposed mechanisms. First, the comparison between the performance of the proposed variants and the original GOA has been conducted. Then, for validation of the efficiency of the proposed NVGOAs, the performance of the best-recorded NVGOA variants has been tested against the 29 CEC-2017 benchmark functions and compared with six state-of-the-art optimization algorithms based on the mean and the standard deviation metrics. Moreover, the Wilcoxon Signed-Rank test has been employed to exhibit the efficiency of the proposed variants. As well comparative analysis with previous enhancements of GOA has b

关键词： grasshopper optimization algorithm Novel Variants Smart electrical grid Metaheuristics optimization Demand side management

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Combining the Wolf Pack algorithm with the Curve-adaptive grasshopper optimization algorithm 5

Combining the Wolf Pack Algorithm with the Curve-adaptive Gr...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Yao, Lejia Hu, Haidong Jiang, Feng Fan, Pu Inner Mongolia Univ Sci & Technol Sch Digital Intelligence Ind Baotou Inner Mongolia Peoples R China

ISBN: (纸本)9798350386783;9798350386776

To address the issues of the grasshopper optimization algorithm (GOA) falling into local optima and achieving low optimization precision, this paper proposes a hybrid algorithm called Combining the Wolf Pack algorithm (WPA) with the Curve-adaptive grasshopper optimization algorithm (WCGOA). Firstly, the population is initialized using Logistic mapping to ensure an optimal initial population, thereby enhancing population diversity. Secondly, the linear weights are replaced with curve-adaptive weights to improve search speed and precision. Thirdly, by incorporating the hierarchical hunting concept from the Wolf Pack algorithm, individual awareness of grasshoppers is developed to enhance global search capabilities. Subsequently, Cauchy mutation is applied to the best individuals to strengthen their ability to escape local optima. Finally, GOA is compared with four other classical algorithms as benchmarks for WCGOA. Statistical analysis and Wilcoxon rank-sum tests conducted on 11 commonly used benchmark functions demonstrate that WCGOA significantly outperforms other algorithms in terms of convergence precision, convergence speed, stability, and optimization success rate.

关键词： grasshopper optimization algorithm Wolf pack algorithm curve adaptation Cauchy mutation

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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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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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Design optimization of concrete gravity dams using grasshopper optimization algorithm

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2024年第12期9卷 1-22页

作者： Abbasi, Salim Seifollahi, Mehran Farzaneh, Shahin Daneshfaraz, Rasoul Sume, Veli Sadraei, Naghi Abraham, John Univ Mohaghegh Ardabili Dept Civil Engn Ardebil Iran Univ Tabriz Dept Civil Engn Tabriz Iran Univ Maragheh Dept Civil Engn Maragheh Iran Recep Tayyip Erdogan Univ Dept Civil Engn Rize Turkiye Islamic Azad Univ Dept Civil Engn Ardebil Iran Univ St Thomas Sch Engn St Paul MN USA

The efficiency and sustainability of dams can be significantly improved by structural optimization during the design process. This study aims to optimize geometric dimensions and minimize the concrete volume of three benchmark Concrete Gravity Dams (CGDs) including Pine-Flat, Middle-Fork, and Richard dams subjected to seismic excitations by applying the grasshopper optimization algorithm (GOA). Employing GOA effectively reduces the concrete volume, achieving reductions of 30.88% (399 m3), 12.5% (1705 m3), and 28.09% (241 m3) for Richard, Pine-Flat, and Middle-Fork dams, respectively. These findings highlight that Richard Dam exhibits the maximum optimization value while Pine-Flat Dam demonstrates minimum optimization value and greatest volume reduction due to its initially larger volume. The optimized dams reduce concrete volume, effectively meeting stability requirements and enhancing stability against applied forces. The Safety Factor against Overturning (SOF) improves from 1.62 to 2.23, and the Safety Factor against Sliding (SFF) increases from 1.31 to 1.48. As a result, the dams are more stable and secure against overturning and sliding. The study underscores the efficiency of the GOA in optimizing CGD design process, offering significant implications for cost savings and resource efficiency in dam construction. This study emphasizes the robustness of GOA as a powerful meta-heuristic algorithm and its high potential for application in various optimization scenarios in structural engineering, and it recommends GOA as a highly effective tool for the optimal design of CGDs.

关键词： Seismic force Concrete gravity dam Geometric dimensions grasshopper optimization algorithm

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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年第0期73卷 103437-103437页

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 novel grasshopper optimization algorithm based on swarm state difference and its application

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第6期42卷 5973-5986页

作者： Lv, Zhaoming Peng, Rong Wuhan Univ Sch Comp Sci Wuhan Peoples R China

The grasshopper optimization algorithm (GOA) has received extensive attention from scholars in various real applications in recent years because it has a high local optima avoidance mechanism compared to other meta-heuristic algorithms. However, the small step moves of grasshopper lead to slow convergence. When solving larger-scale optimization problems, this shortcoming needs to be solved. In this paper, an enhanced grasshopper optimization algorithm based on solitarious and gregarious states difference is proposed. The algorithm consists of three stages: the first stage simulates the behavior of solitarious population learning from gregarious population;the second stage merges the learned population into the gregarious population and updates each grasshopper;and the third stage introduces a local operator to the best position of the current generation. Experiments on the benchmark function show that the proposed algorithm is better than the four representative GOAs and other metaheuristic algorithms in more cases. Experiments on the ontology matching problem show that the proposed algorithm outperforms all metaheuristic-based method and beats more the state-of-the-art systems.

关键词： Meta-heuristic algorithms grasshopper optimization algorithm solitarious and gregarious states chemotaxis operator ontology matching

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Blasting pattern optimization using gene expression programming and grasshopper optimization algorithm to minimise blast-induced ground vibrations

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ENGINEERING WITH COMPUTERS 2022年第4期38卷 3341-3350页

作者： Bayat, Parichehr Monjezi, Masoud Mehrdanesh, Amirhossein Khandelwal, Manoj Tarbiat Modares Univ Dept Min Fac Engn Tehran 14115143 Iran Federat Univ Australia Sch Engn Informat Technol & Phys Sci Ballarat Vic 3350 Australia

Blast-induced ground vibration is considered as one of the most hazardous phenomena of mine blasting, which can even cause casualties and severe damages to the adjacent properties. Measuring peak particle velocity (PPV) is helpful to know the actual vibration level but prediction of blast vibration prior to the blast is a tedious job due to involvement of blast design, explosive and rock parameters. Nowadays, efficient application of intelligent systems has been approved in different branches of science and technology. In this paper, a gene expression programming (GEP) model was developed to predict PPV using various blasting patterns as model inputs, which showed a high level of accuracy for the implemented model. Also, to optimize blast pattern attaining minimum ground vibration during blasting operation, the developed functional GEP model was taken as objective function for grasshopper optimization algorithm (GOA). Construction of GOA model was performed using a trial and error mechanism to find out the best possible pertinent GOA parameters. Finally, it was observed that utilizing GOA technique, PPV can be reduced by 67% with optimized blast parameters including burden of 3.21 m, spacing of 3.75 m, and charge per delay of 225 kg. A sensitivity analysis was also performed to understand the influence of each input parameters on the blast vibrations.

关键词： Ground vibration Meta-heuristic optimization Gene expression programming grasshopper optimization algorithm

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Classification of clothing images based on a parallel convolutional neural network and random vector functional link optimized by the grasshopper optimization algorithm

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TEXTILE RESEARCH JOURNAL 2022年第9-10期92卷 1415-1428页

作者： Zhou, Zhiyu Deng, Wenxiong Wang, Yaming Zhu, Zefei Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou Peoples R China Lashay Univ Hangzhou Peoples R China Hangzhou Jianzi Univ Sch Mech Engn Hangzhou Peoples R China

To improve accuracy in clothing image recognition, this paper proposes a clothing classification method based on a parallel convolutional neural network (PCNN) combined with an optimized random vector functional link (RVFL). The method uses the PCNN model to extract features of clothing images. Then, the structure-intensive and dual-channel convolutional neural network (i.e., the PCNN) is used to solve the problems of traditional convolutional neural networks (e.g., limited data and prone to overfitting). Each convolutional layer is followed by a batch normalization layer, and the leaky rectified linear unit activation function and max-pooling layers are used to improve the performance of the feature extraction. Then, dropout layers and fully connected layers are used to reduce the amount of calculation. The last layer uses the RVFL as optimized by the grasshopper optimization algorithm to replace the SoftMax layer and classify the features, further improving the stability and accuracy of classification. In this study, two aspects of the classification (feature extraction and feature classification) are improved, effectively improving the accuracy. The experimental results show that on the Fashion-Mnist dataset, the accuracy of the algorithm in this study reaches 92.93%. This value is 1.36%, 2.05%, 0.65%, and 3.76% higher than that of the local binary pattern (LBP)-support vector machine (SVM), histogram of oriented gradients (HOG)-SVM, LBP-HOG-SVM, and AlexNet-sparse representation-based classifier algorithms, respectively, effectively demonstrating the classification performance of the algorithm.

关键词： Parallel convolutional neural network random vector functional link grasshopper optimization algorithm classification of clothing

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E-mail Spam Classification Using grasshopper optimization algorithm and Neural Networks

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Computers, Materials & Continua 2022年第6期71卷 4749-4766页

作者： Sanaa A.A.Ghaleb Mumtazimah Mohamad Syed Abdullah Fadzli Waheed A.H.M.Ghanem Faculty of Informatics and Computing Universiti Sultan Zainal AbidinKuala Terengganu22200Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia TerengganuKuala Terengganu21030Malaysia Faculty of Engineering University of AdenAdenYemen Faculty of Education(Aden-Saber) University of AdenAdenYemen

Spam has turned into a big predicament these days,due to the increase in the number of spam emails,as the recipient regularly receives piles of *** only is spam wasting users’time and *** addition,it limits the storage space of the email box as well as the disk ***,spam detection is a challenge for individuals and organizations *** advance spam email detection,this work proposes a new spam detection approach,using the grasshopper optimization algorithm(GOA)in training a multilayer perceptron(MLP)classifier for categorizing emails as ham and ***,MLP and GOA produce an artificial neural network(ANN)model,referred to(GOAMLP).Two corpora are applied Spam Base and UK-2011Web spam for this ***,the finding represents evidence that the proposed spam detection approach has achieved a better level in spam detection than the status of the art.

关键词： grasshopper optimization algorithm multilayer perceptron artificial neural network spam detection approach

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