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Simultaneous Feature Selection and Support Vector Machine optimization Using the grasshopper optimization algorithm

COGNITIVE COMPUTATION 2018年第3期10卷 478-495页

作者： Aljarah, Ibrahim Al-Zoubi, Ala M. Faris, Hossam Hassonah, Mohammad A. Mirjalili, Seyedali Saadeh, Heba Univ Jordan Business Informat Technol Dept King Abdullah II Sch Informat Technol Amman Jordan Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia Univ Jordan Dept Comp Sci King Abdullah II Sch Informat Technol Amman Jordan

Support vector machine (SVM) is considered to be one of the most powerful learning algorithms and is used for a wide range of real-world applications. The efficiency of SVM algorithm and its performance mainly depends on the kernel type and its parameters. Furthermore, the feature subset selection that is used to train the SVM model is another important factor that has a major influence on it classification accuracy. The feature subset selection is a very important step in machine learning, specially when dealing with high-dimensional data sets. Most of the previous researches handled these important factors separately. In this paper, we propose a hybrid approach based on the grasshopper optimisation algorithm (GOA), which is a recent algorithm inspired by the biological behavior shown in swarms of grasshoppers. The goal of the proposed approach is to optimize the parameters of the SVM model, and locate the best features subset simultaneously. Eighteen low- and high-dimensional benchmark data sets are used to evaluate the accuracy of the proposed approach. For verification, the proposed approach is compared with seven well-regarded algorithms. Furthermore, the proposed approach is compared with grid search, which is the most popular technique for tuning SVM parameters. The experimental results show that the proposed approach outperforms all of the other techniques in most of the data sets in terms of classification accuracy, while minimizing the number of selected features.

关键词： SVM Support vector machine grasshopper optimization algorithm GOA Optimisation Feature selection Metaheuristics

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Hybrid grasshopper optimization algorithm and differential evolution for global optimization

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第5期37卷 6899-6910页

作者： Jia, Heming Li, Yao Lang, Chunbo Peng, Xiaoxu Sun, Kangjian Li, Jinduo Northeast Forestry Univ Coll Mech & Elect Engn Harbin Heilongjiang Peoples R China

grasshopper optimization algorithm (GOA) is proposed for imitating grasshopper's behavior in nature, which has the disadvantages of slow convergence speed and unbalanced exploration and exploitation, etc. Therefore, an algorithm called GOA_jDE, which combines GOA and jDE is proposed to improve the optimization performance. Firstly, the adaptive strategy is introduced into DE to improve the global search ability in the proposed algorithm. Secondly, the combination of jDE and GOA greatly improves the convergence efficiency while maintaining the population diversity. Finally, it can be observed in the work that the proposed algorithm improves the convergence speed and calculation precision. In the subsequent experiments, 14 well-known test benchmark functions are used to compare the advantages of GOA_jDE. The experimental results illustrate that the performance of proposed algorithm has significant improvement, which also proves the feasibility and effectiveness. Considering the complexity of engineering problems, three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) are used to evaluate the performance of the proposed algorithm. In addition, the classical engineering design results proves the merits of this algorithm in solving real problems with unknown search spaces.

关键词： grasshopper optimization algorithm differential evolution self-adapting based algorithm hybrid optimization functions optimization

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Pipeline leak detection based on multiscale convolution neural network and improved symmetric dot pattern optimized by grasshopper optimization algorithm

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2024年

作者： Zhang, Yong Xing, Pengfei Dong, Hongli Lu, Jingyi Zhou, Xingda Zhou, Yina Liang, Hao Li, Gongfa Northeast Petr Univ Sanya Offshore Oil & Gas Res Inst Daqing Peoples R China Heilongjiang Prov Key Lab Networking & Intelligent Daqing Peoples R China Northeast Petr Univ Sch Phys & Elect Engn Daqing 163318 Heilongjiang Peoples R China Northeast Petr Univ Artificial Intelligence Energy Res Inst Daqing Peoples R China Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Minist Educ Wuhan Peoples R China

In the engineering of pipeline condition identification, the complexity of pipeline signal components often results in insufficient feature extraction with traditional feature extraction-machine learning methods, thereby affecting the recognition performance. In order to effectively address the aforementioned issues, based on deep learning, we propose a multiscale convolution neural network (MCNN) to effectively identify pipeline conditions by classifying improved symmetry dot pattern (ISDP) images of one-dimensional negative pressure wave signals of pipelines. First, we propose the ISDP transformation method, considering that negative pressure wave signals of pipes with different leakage degrees have different amplitude changes. The ISDP transformation method transforms the negative pressure wave signal of the pipeline from one dimension to two dimensions. Then the grasshopper optimization algorithm (GOA) was employed to optimize the parameters of the ISDP algorithm. Second, we build the MCNN depth network to train and classify the ISDP image. The MCNN can simultaneously learn both the global and local features of an image. The corresponding evaluation indicators show that the proposed method of working condition recognition using MCNN to classify and recognize the ISDP image of pipeline signal has higher accuracy and robustness than traditional machine learning methods and common deep learning methods. The evaluation results prove that the proposed algorithm is effective in pipeline signal classification.

关键词： Symmetry dot pattern improved symmetry dot pattern grasshopper optimization algorithm multiscale convolutional neural network pipeline leakage

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Application of the grasshopper optimization algorithm (GOA) to the Optimal Operation of Hydropower Reservoir Systems Under Climate Change

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WATER RESOURCES MANAGEMENT 2021年第13期35卷 4325-4348页

作者： Rahmati, Kobra Ashofteh, Parisa-Sadat Loaiciga, Hugo A. Univ Qom Dept Civil Engn Qom Iran Univ Calif Santa Barbara Dept Geog Santa Barbara CA 93016 USA

Hydropower is a low-carbon energy source, which may be adversely impacted by climate change. This work applies the grasshopper optimization algorithm (GOA) to optimize hydropower multi-reservoir systems. Performance of GOA is compared with that of particle swarm optimization (PSO). GOA is applied to hydropower, three-reservoir system (Seymareh, Sazbon, and Karkheh), located in the Karkheh basin (Iran) for baseline period 1976-2005 and two future periods (2040-2069) and (2070-2099) under greenhouse gases pathway scenarios RCP2.6, RCP4.5, and RCP8.5. GOA minimizes the shortage of hydropower energy generation. Results from GOA optimization of Seymareh reservoir show that average objective function in baseline is 85 and minimum value of average objective function in 2040-2069 would be under RCP2.6 (equal to 0.278). optimization of Seymareh-reservoir based on PSO shows that average value of objective function in baseline is less (that is, better) than value obtained with GOA (10.953). optimization results for two-reservoir system (Sazbon and Karkheh) based on GOA optimization show that objective function in baseline is 5.44 times corresponding value obtained with PSO, standard deviation is 2.3 times that calculated with PSO, and run-time is 1.5 times PSO's. Concerning three-reservoir systems it was determined that objective function based on PSO had the best value (the lowest energy deficit), especially in future. GOA converges close to the best objective function, especially in future-periods optimization, and convergence to solutions is more stable than PSO's. A comparison of performance of GOA and PSO indicates PSO converges faster to optimal solution, and produces better objective function than GOA.

关键词： grasshopper optimization algorithm Climate change Hydropower multi-reservoir system Particle swarm algorithm

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Improved grasshopper optimization algorithm using opposition-based learning

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 112卷 156-172页

作者： Ewees, Ahmed A. Abd Elaziz, Mohamed Houssein, Essam H. Univ Bisha Bisha Saudi Arabia Damietta Univ Dept Comp Dumyat Egypt Zagazig Univ Fac Sci Dept Math Zagazig Egypt Minia Univ Fac Comp & Informat Al Minya Egypt

This paper proposes an improved version of the grasshopper optimization algorithm (GOA) based on the opposition-based learning (OBL) strategy called OBLGOA for solving benchmark optimization functions and engineering problems. The proposed OBLGOA algorithm consists of two stages: the first stage generates an initial population and its opposite using the OBL strategy;and the second stage uses the OBL as an additional phase to update the GOA population in each iteration. However, the OBL is applied to only half of the solutions to reduce the time complexity. To investigate the performance of the proposed OBLGOA, six sets of experiment series are performed, and they include twenty-three benchmark functions and four engineering problems. The experiments revealed that the results of the proposed algorithm were superior to those of ten well-known algorithms in this domain. Eventually, the obtained results proved that the OBLGOA algorithm can provide competitive results for optimization engineering problems compared with state-of-the-art algorithms. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： grasshopper optimization algorithm Opposition-based learning Benchmark functions Engineering problems optimization

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Enhanced grasshopper optimization algorithm with extreme learning machines for motor-imagery classification

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ASIAN JOURNAL OF CONTROL 2023年第4期25卷 3015-3028页

作者： Balmuri, Kavitha Rani Madala, Srinivasa Rao Divakarachari, Parameshachari Bidare de Prado, Rocio Perez Frnda, Jaroslav CMR Tech Campus Dept Informat Technol Hyderabad Telangana India PACE Inst Technol & Sci Dept Comp Sci & Engn Ongole Andhra Pradesh India Nitte Meenakshi Inst Technol Dept Elect & Commun Engn Bengaluru Karnataka India Univ Jaen Telecommun Engn Dept Jaen Spain Univ Zilina Dept Quantitat Methods & Econ Informat Zilina Slovakia Nitte Meenakshi Inst Technol Dept Elect & Commun Engn Bengaluru 560064 Karnataka India

In Brain Computer Interface (BCI), achieving a reliable motor-imagery classification is a challenging task. The set of discriminative and relevant feature vectors plays a crucial role in classification. In this article, an enhanced optimization technique is implemented for selecting active feature vectors to enhance motor-imagery classification using Electroencephalography (EEG) signals. After collecting the input EEG signals from BCI competition III-4a and IV-2a databases, the 6th-order butter-worth filter is employed for eliminating base-line wander noise from the raw EEG signals. Further, the Variational Mode Decomposition technique is applied for separating the important signal components from the composite EEG signals, and then, the Higher Order Statistic, kurtosis, skewness, standard deviation, and entropy are utilized for feature extraction. The high-dimensional feature values are given to the Enhanced grasshopper optimization algorithm for optimum feature selection, which are given to the Extreme Learning Machines (ELM) classifier for motor-imagery classification. Finally, in the resulting section, the optimized ELM model achieved 99.48% and 99.12% of accuracy on the BCI competition III-4a and IV-2a databases, where the achieved results are maximum compared to the traditional deep learning models.

关键词： brain computer interface electroencephalography extreme learning machines grasshopper optimization algorithm variational mode decomposition

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A novel approach for optimal design of digital FIR filter using grasshopper optimization algorithm

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ISA TRANSACTIONS 2021年 108卷 196-206页

作者： Yadav, Suman Yadav, Richa Kumar, Ashwni Kumar, Manjeet Bharati Vidyapeeth Coll Engn Dept Elect & Commun Engn New Delhi 110063 India Indira Gandhi Delhi Tech Univ Women IGDTUW Dept Elect & Commun Engn New Delhi 110006 India Delhi Technol Univ DTU Rohini Dept Elect & Commun Engn Delhi 110042 India

The idea behind designing digital filters is to compute the optimal filter coefficients such that the magnitude response of the designed matches the ideal frequency response using optimization algorithms. The proposed work employed a recently proposed swarm-based optimization technique, namely, a grasshopper optimization algorithm (GOA) to design a linear phase finite impulse response (FIR) low pass, high pass, band pass , and band stop filters. This proposed algorithm models the behaviour of grasshoppers while seeking food sources to solve optimization problems. For the designing of the FIR filter, an absolute error difference fitness function is used, which is minimized using GOA to obtain optimal filter coefficients. The performance comparison of the proposed work is done with already existing algorithms such as cuckoo search, particle swarm optimization, artificial bee colony to prove its superiority and consistency. It is found that GOA based filter meets the objective efficiently with reduced ripples in pass band and higher attenuation in stop band with least execution time. (c) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Digital filter design Filter response grasshopper optimization algorithm Bio-inspired algorithms

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grasshopper optimization algorithm for diesel engine fuelled with ethanol-biodiesel-diesel blends

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CASE STUDIES IN THERMAL ENGINEERING 2022年 31卷

作者： Veza, Ibham Karaoglan, Aslan Deniz Ileri, Erol Kaulani, S. A. Tamaldin, Noreffendy Latiff, Z. A. Said, Mohd Farid Muhamad Anh Tuan Hoang Yatish, K., V Idris, M. Univ Teknikal Malaysia Melaka Fac Mech Engn Durian Tunggal 76100 Melaka Malaysia Balikesir Univ Dept Ind Engn TR-10145 Balikesir Turkey Natl Def Univ Army NCO Vocat HE Sch Dept Automot Sci TR-10110 Balikesir Turkey Univ Teknol Malaysia Fac Engn Sch Mech Engn Johor Baharu 81310 Johor Malaysia Univ Teknol Malaysia Automot Dev Ctr Inst Vehicle Syst & Engn Johor Baharu 81310 Malaysia HUTECH Univ Inst Engn Ho Chi Minh City Vietnam Jain Univ Ctr Nano & Mat Sci Bangalore 562112 Karnataka India PT PLN Persero Engn & Technol Div Jakarta Indonesia

A recently invented algorithm known as the grasshopper optimization algorithm (GOA) was employed to optimize diesel engine performance and emission operated with ternary fuel (ethanol-biodiesel-diesel) blends. Using the regression modelling over these experimental results;the mathematical equations between the factors i.e., ethanol ratio (vol%), biodiesel ratio (vol%), engine load (Nm)) and the responses i.e., BSFC (g/kWh), BTE (%), HC (ppm), CO2 (%), NOx (ppm), CO (%) were calculated. grasshopper optimization algorithm was then run through these regression equations to calculate the optimum factor levels. The confirmation results suggested that the BTE was maximized and the other responses were minimized successfully. For the ANOVA results, under the 95% confidence level with alpha = 5% (=0.05), the p-value for all the regression models was less than 0.05, which indicated the significance of the regression models. In terms of the performance tests of the models, the regression models good fit the given observations with a low prediction error. The grasshopper optimization algorithm showed that ethanol-biodiesel-diesel blend in the ratio of 10%, 7.5%, 82.5% run at 7 Nm engine load gave the optimum results for diesel engine performance and emission characteristics. These findings have important implications for the potential of grasshopper optimization algorithm to improve engine performance and emission characteristics.

关键词： grasshopper optimization algorithm Ethanol Biodiesel Diesel engine Performance Emission

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Enhanced grasshopper optimization algorithm using elite opposition-based learning for solving real-world engineering problems

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ENGINEERING WITH COMPUTERS 2022年第5期38卷 4207-4219页

作者： Yildiz, Betul Sultan Pholdee, Nantiwat Bureerat, Sujin Yildiz, Ali Riza Sait, Sadiq M. Bursa Uludag Univ Dept Elect & Energy TR-16059 Gorukle Turkey Khon Kaen Univ Dept Mech Engn Fac Engn Sustainable Infrastruct Res & Dev Ctr Khon Kaen 40002 Thailand Bursa Uludag Univ Dept Automot Engn TR-16059 Gorukle Turkey King Fahd Univ Petr & Minerals Dept Comp Engn Dhahran Saudi Arabia

Optimizing real-life engineering design problems are challenging and somewhat difficult if optimum solutions are expected. The development of new efficient optimization algorithms is crucial for this task. In this paper, a recently invented grasshopper optimization algorithm is upgraded from its original version. The method is improved by adding an elite opposition-based learning methodology to an elite opposition-based learning grasshopper optimization algorithm. The new optimizer, which is elite opposition-based learning grasshopper optimization method (EOBL-GOA), is validated with several engineering design probles such as a welded beam design problem, car side crash problem, multiple clutch disc problem, hydrostatic thrust bearing problem, three-bar truss, and cantilever beam problem, and finally used for the optimization of a suspension arm of the vehicles. The optimum results reveal that the EOBL-GOA is among the best algorithms reported in the literature.

关键词： grasshopper optimization algorithm Elite opposition-based learning Welded beam Vehicle crashworthiness Multi-clutch disc Hydrostatic thrust bearing design Three-bar truss Cantilever beam suspension arm

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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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