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Spiral Motion Mode Embedded grasshopper optimization algorithm: Design and Analysis

IEEE ACCESS 2021年 9卷 71104-71132页

作者： Xu, Zhangze Gui, Wenyong Heidari, Ali Asghar Liang, Guoxi Chen, Huiling Wu, Chengwen Turabieh, Hamza Mafarja, Majdi Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Natl Univ Singapore Sch Comp Dept Comp Sci Singapore 117417 Singapore Wenzhou Polytech Dept Informat Technol Wenzhou 325035 Peoples R China Taif Univ Dept Informat Technol Coll Comp & Informat Technol At Taif 21944 Saudi Arabia Birzeit Univ Dept Comp Sci Ramallah 72439 Palestine

A new enhanced grasshopper optimization algorithm (GOA) has been developed and successfully applied to feature selection. GOA, as a heuristic algorithm, is proposed by simulating the living habits of grasshoppers in nature. Although GOA has an excellent global optimization capability, it still faces the disadvantage of low efficiency of searching optimization due to its ease of falling into the local optimum. Hence, based on the original GOA, this study integrates new ideas to reduce the defects to obtain a better global optimization ability. Because of the continuous optimization problem, the features of pursuing the best possible individual of spiral motion have been considered. The spiral motion is integrated into the GOA exploitation search stage, which further expands the diversification and intensification trends' capacities and effectively balances the exploration and exploitation procedures. Intuitively speaking, GOA with spiral search method can find better solutions in the exploration movement process, which is more efficient than the original search method. In the experimental comparison, to verify the proposed SGOA's ability in dealing with global unconstrained and constrained optimization problems, we compared it with other 30 IEEE 2017 benchmark tasks in meta-heuristic algorithms. Then, it is adopted to optimize engineering design and feature selection problems. We can know that the proposed SGOA has a good optimization ability in practical application from the experimental results. Spiral motion mode can significantly improve the original GOA's exploitation and exploration ability, and the proposed SGOA is of great assistance in practical fields. More info about this paper can be found on the web services https://***.

关键词： optimization Spirals Heuristic algorithms Feature extraction Gravity Licenses Information technology grasshopper optimization algorithm spiral motion engineering design problems feature selection

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A hybrid grasshopper optimization algorithm with bat algorithm for global optimization

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第3期80卷 3863-3884页

作者： Yue, Shenghan Zhang, Hongbo Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Peoples R China

This paper introduces a hybrid grasshopper optimization algorithm with bat algorithm (BGOA) for global optimization. In the BGOA, the Levy flight with variable coefficient is employed to enhance the exploration capability of the GOA. Then, the local search operation of bat algorithm (BA) is combined to balance the exploration and exploitation. Additionally, the random strategy is introduced and applied to high quality population to improve the exploitation capability in the searching process. The performance of BGOA is evaluated on 23 benchmark test functions, and compares with genetic algorithm (GA), bat algorithm (BA), moth-flame optimization algorithm (MFO), dragonfly algorithm (DA) and basic GOA. The results establish that the BGOA is able to provide better outcomes than the other algorithms.

关键词： grasshopper optimization algorithm Bat algorithm Levy flight Random strategy Global optimization

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Building an efficient intrusion detection system using grasshopper optimization algorithm for anomaly detection

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2021年第3期24卷 1881-1900页

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

Intrusion detection is one of the most crucial activities for security infrastructures in network environments, and it is widely used to detect, identify and track malicious threats. A common approach in intrusion detection systems (IDSs) specifically in anomaly detection is evolutionary algorithm that works as intrusion detector. Still, it has been challenging to design a precise and reliable IDS to determine security threats due to the large capacity of network data which contains redundant and irrelevant features. It does not only decrease the process of classification but also prevents a classifier from making precise decisions. To increase the accuracy and reduce the false alarm rate, in this study integration of ensemble feature selection (EFS) and grasshopper optimization algorithm (GOA), called EFSGOA is developed. Firstly, EFS method is applied to rank the features for selecting the top subset of relevant features. Afterward, GOA is utilized to identify significant features from the obtained reduced features set produced by EFS technique that can contribute to determine the type of attack. Furthermore, GOA utilizes support vector machine (SVM) as a fitness function to obtain the noteworthy features and to optimize penalty factor, kernel parameter, and tube size parameters of SVM for maximizing the classification performance. The experimental results demonstrate that EFSGOA method has performed better and obtained high detection rate of 99.69%, accuracy of 99.98% and low false alarm rate of 0.07 in NSL-KDD and high detection rate of 99.26%, accuracy of 99.89% and low false alarm rate of 0.097 in KDD Cup 99 data. Moreover, the proposed method has succeeded in achieving higher performance compared to other state-of-art techniques in terms of accuracy, detection rate, false alarm rate, and CPU time.

关键词： Classification Feature selection grasshopper optimization algorithm Intrusion detection

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A Hybrid Modified grasshopper optimization algorithm and Genetic algorithm to Detect and Prevent DDoS Attacks

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INTERNATIONAL JOURNAL OF ENGINEERING 2021年第4期34卷 811-824页

作者： Mohammadi, S. Babagoli, M. KN Toosi Univ Technol Dept Ind Engn Tehran Iran

Cyber security has turned into a brutal and vicious environment due to the expansion of cyber-threats and cyberbullying. Distributed Denial of Service (DDoS) is a network menace that compromises victims? resources promptly. Considering the significant role of optimization algorithms in the highly accurate and adaptive detection of network attacks, the present study has proposed Hybrid Modified grasshopper optimization algorithm and Genetic algorithm (HMGOGA) to detect and prevent DDoS attacks. HMGOGA overcomes conventional GOA drawbacks like low convergence speed and getting stuck in local optimum. In this paper, the proposed algorithm is used to detect DDoS attacks through the combined nonlinear regression (NR)-sigmoid model simulation. In order to serve this purpose, initially, the most important features in the network packages are extracted using the Random Forest (RF) method. By removing 55 irrelevant features out of a total of 77, the selected ones play a key role in the proposed model's performance. To affirm the efficiency, the high correlation of the selected features was measured with Decision Tree (DT). Subsequently, the HMGOGA is trained with benchmark cost functions and another proposed cost function that enabling it to detect malicious traffic properly. The usability of the proposed model is evaluated by comparing with two benchmark functions (Sphere and Ackley function). The experimental results have proved that HMGOGA based on NR-sigmoid outperforms other implemented models and conventional GOA methods with 99.90% and 99.34% train and test accuracy, respectively

关键词： DDoS Detection Cyber-security grasshopper optimization algorithm Random Forest

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Glioma grade detection using grasshopper optimization algorithm-optimized machine learning methods: The Cancer Imaging Archive study

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2021年第3期31卷 1670-1677页

作者： Hedyehzadeh, Mohammadreza Maghooli, Keivan MomenGharibvand, Mohammad Islamic Azad Univ Sci & Res Branch Dept Biomed Engn Tehran Iran Islamic Azad Univ Dezful Branch Dept Biomed Engn Dezful Iran Ahvaz Jundishapur Univ Med Sci Dept Radiol Ahvaz Iran

Detection of brain tumor's grade is a very important task in treatment plan design which was done using invasive methods such as pathological examination. This examination needs resection procedure and resulted in pain, hemorrhage and infection. The aim of this study is to provide an automated non-invasive method for estimation of brain tumor's grade using Magnetic Resonance Images (MRI). After pre-processing, using Fuzzy C-Means (FCM) segmentation method, tumor region was extracted from post-processed images. In feature extraction, texture, Local Binary Pattern (LBP) and fractal-based features were extracted using Matlab software. Then using grasshopper optimization algorithm (GOA), parameters of three different classification methods including Random Forest (RF), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were optimized. Finally, performance of three applied classifiers before and after optimization were compared. The results showed that the random forest with accuracy of 99.09% has achieved better performance comparing other classification methods.

关键词： fuzzy C-means GLCM glioma grade grasshopper optimization algorithm local binary pattern

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Integrating mutation operator into grasshopper optimization algorithm for global optimization

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SOFT COMPUTING 2021年第13期25卷 8281-8324页

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

The major purpose of this article is to enhance the performance of GOA algorithm by integrating a new mutation operator to the standard GOA algorithm. A series of six different variants of enhanced GOA is proposed by integrating GOA with six different variants of the mutation operator. The new enhanced metaheuristic optimization method is called EGOAs. EGOA aims to address the problems of slow convergence and trapping into local optima, by achieving a good balance between exploration and exploitation, using a special mutation operator that enhances the diversity of the standard GOA, to find the best solution for global optimization problems. The implementation process for enhancing the GOA algorithm is presented and the effectiveness of the enhanced algorithm is evaluated against 60 of the optimization benchmark functions, and compared to that of the standard GOA, as well as to other metaheuristic optimization algorithms. The performance of EGOAs was compared with the other improved methods based on GOA. Experimental results show that EGOAs is clearly superior to the standard GOA algorithm, as well as to other well-known algorithms, in terms of achieving the best optimal value, convergence speed, and avoiding local minima, which makes EGOAs a promising addition to the arsenal of metaheuristic algorithms.

关键词： Global optimization problem grasshopper optimization algorithm Mutation operator Test functions

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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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An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第3期35卷

作者： Srivel, R. Kalaiselvi, K. Shanthi, S. Perumal, Uma Adhiparasakthi Engn Coll Dept CSE Melmaruvathur Tamil Nadu India SRM Inst Sci & Technol Dept Networking & Commun Chennai India Malla Reddy Coll Engn & Technol Dept CSE Hyderabad India Jazan Univ Jazan Saudi Arabia

Due to the technical words employed, which are primarily recognized by medical specialists, information retrieval in the medical area is sometimes described as sophisticated. Because of this, users frequently have trouble coming up with queries utilizing these medical phrases. However, this problem may be readily fixed by an information retrieval system that finds the pertinent terms that fit the user's query and automatically creates a ranking document using these keywords. To enhance the IR performance, the Automatic Query expansion method is applied by appending additional query terms for the medical domain. We propose a novel fuzzy-based grasshopper optimization algorithm (GOA) based on automatic query expansion. This work is mainly focused on filtering the most relevant augmented query by utilizing the synchronization score of IR evidence like normalized term frequency, inverse document frequency, and normalization of document length. The main aim of this work is to identify the medical terms that appropriately match the user's queries. The GOA algorithm ranks the terms based on relevance and then identifies the terms with the maximum synchronization value. The documents formed using the optimal expanded query are classified into three types, namely totally relevant, moderately relevant, and marginally relevant. Besides, the comparison of the proposed work is carried out for different performance metrics like Mean-Average Precision, F-measure, Precision-recall, and Precision rank are evaluated and analyzed by using TREC-COVID, TREC Genomics 2007, and MEDLARs medical datasets for the proposed and some of the state-of-art works. For a total of 60 queries, the proposed model offers an F1-Score of 0.964, 0.959, and 0.968 for the MEDLARS, TREC Genomics, and TREC COVID19 datasets, respectively. The E1-score and Mean Reciprocal Rate (MRR) of the proposed model is 0.8 and 0.9 when evaluated using the TREC COVID19 dataset. Performance analyses show that the proposed app

关键词： accelerated particle swarm optimization COVID fuzzy logic grasshopper optimization algorithm query expansion

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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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Optimal load shedding scheme using grasshopper optimization algorithm for islanded power system with distributed energy resources

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AIN SHAMS ENGINEERING JOURNAL 2023年第1期14卷

作者： Ahmadipour, Masoud Othman, Muhammad Murtadha Salam, Zainal Alrifaey, Moath Ridha, Hussein Mohammed Veerasamy, Veerapandiyan Univ Teknol MARA Coll Engn Sch Elect Engn Shah Alam 40450 Selangor Malaysia Univ Teknol Malaysia Ctr Elect Energy Syst Sch Elect Engn Johor Baharu 81310 Malaysia Univ Putra Malaysia Fac Engn Dept Mech & Mfg Engn Serdang 43400 Selangor Malaysia Univ Putra Malaysia Fac Engn Adv Lightning Power & Energy Res ALPER Serdang Selangor Malaysia Nanyang Technol Univ NTU Sch Elect & Elect Engn Singapore Singapore

In this paper, a new optimal load shedding method using a grasshopper optimization algorithm (GOA) is proposed for maintaining the stability of the islanded power system that comprises distributed energy resources (DER). The GOA is used in conjunction with the method of voltage stability margin (VSM) to handle the multi-objective shedding constraints, namely the generation restrictions, allowable load cur-tailment, and load priority. To assess the effectiveness of the offered method, a comprehensive evaluation study is applied to a system of IEEE 33-bus and four DG units in view of different scenarios. Furthermore, the effectiveness of performance of the GOA-based load shedding is compared in terms of fitness value, voltage stability margin, and percentage of load that should be curtailed with three well-known opti-mization approaches (i.e., the particle swarm optimization (PSO), grey wolf optimization (GW), and genetic algorithm (GA)) under different islanded scenarios. The obtained results show that the perfor-mance of the proposed method is better than other methods with the less value of load curtailed for totally islanded states (i.e., 45.67% for island A, 16.63% for island B, 38.05% for island C, and 27.98% for island D in this study). In addition, the results of the simulation display the efficacy of the proposed scheme to ensure voltage stability with the optimal load quantity to be shed for dissimilar islanding scenarios.& COPY;2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams Uni-versity. This is an open access article under the CC BY-NC-ND license (http://***/licenses/ by-nc-nd/4.0/).

关键词： Optimal load shedding Islanded power system Voltage collapse Voltage stability margin grasshopper optimization algorithm

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