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A Binary Grasshopper Optimization Algorithm for Feature Selection

Reyhaneh Yaghobzadeh, Seyyed Reza Kamel, Mojtaba Asgari, Hassan Saadatmand, Azad Mashhad
2020 International Journal of Engineering Research and  
It was concluded that the binary grasshopper optimization algorithm is the best method for feature selection  ...  Methods: In this paper, continuous grasshopper optimization algorithm is converted into binary by using a nonlinear mapping function.  ...  In this paper, the feature selection is discussed by combining these two concepts and using the binary grasshopper optimization algorithm.  ... 
doi:10.17577/ijertv9is030420 fatcat:g7xpjfl5xjbr5amr5zmt6ozh5m

A Hybrid Modified Grasshopper Optimization Algorithm and Genetic Algorithm to Detect and Prevent DDoS Attacks

2021 International Journal of Engineering  
To affirm the efficiency, the high correlation of the selected features was measured with Decision Tree (DT).  ...  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  ...  The vector feature selection method has been applied to the selected dataset (NSL-KDD) to collect an appropriate feature subset and a combination of Lion Optimization Algorithm and Convolutional Neural  ... 
doi:10.5829/ije.2021.34.04a.07 fatcat:v2jzyxx2mjcg7mjrbux2dtkjoa

Optimized grass hopper algorithm for diagnosis of Parkinson's disease

Shallu Sehgal, Manisha Agarwal, Deepak Gupta, Shirsh Sundaram, Arun Bashambu
2020 SN Applied Sciences  
Popular algorithms like Random Forest, Decision Tree and k-Nearest Neighbour classifier were used in judgement on shortlisted aka selected features.  ...  The experiment's outcome revealed that the presented Modified Grasshopper Optimization Algorithm assists in reducing the selected features count and improving the accuracy.  ...  The objective is to develop a more optimized & efficient algorithm, including the following: • To reduce the noise in data by using a feature selection method, in turn decreasing the time complexity. •  ... 
doi:10.1007/s42452-020-2826-9 fatcat:iw52itqzc5btvcbvok4s3wemsu

An Improved Grasshopper Optimization Algorithm for Global Optimization

YAN Yan, MA Hongzhong, LI Zhendong
2021 Chinese journal of electronics  
We proposes an improved grasshopper algorithm for global optimization problems.  ...  Grasshopper optimization algorithm (GOA) is a recently proposed meta-heuristic algorithm inspired by the swarming behavior of grasshoppers.  ...  In the process of feature selection, the continuous value of the grasshopper algorithm position is generally changed to binary value to determine whether to select the feature.  ... 
doi:10.1049/cje.2021.03.008 fatcat:na5wdzeoxvbovb5rg3hjjk4gze

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Yassine Meraihi, Asma Benmessaoud Gabis, Seyedali Mirjalili, Amar Ramdane-Cherif
2021 IEEE Access  
Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature.  ...  It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other  ...  In the work of Zakeri and Hokmabadi [112] , a real-valued GOA (GOFS) was proposed for tackling the feature selection problem.  ... 
doi:10.1109/access.2021.3067597 fatcat:4deeotokfjb3dc2f4egf3qqymy

A Modified Grasshopper Optimization Algorithm Combined with Convolutional Neural Network for Content Based Image Retrieval

2019 International Journal of Engineering  
So, a new search method, modified grasshopper optimization algorithm (MGOA) is proposed to solve modeled problem and to retrieve similar images efficiently, despite of total search in database.  ...  A recent introduced heuristic algorithm is Grasshopper Optimization Algorithm (GOA) which has been proved to be able to solve difficult optimization problems.  ...  Grasshopper Optimization Algorithm (GOA) GOA is a new heuristic optimization algorithm that mimics grasshoppers swarm to solve optimization problem.  ... 
doi:10.5829/ije.2019.32.07a.04 fatcat:seqfwdjy2naf5j3ptrp6xvacye

GOAMLP: Network Intrusion Detection with Multilayer Perceptron and Grasshopper Optimization Algorithm

Shadi Moghanian, Farshid B. Saravi, Giti Javidi, Ehsan O. Sheybani
2020 IEEE Access  
In the proposed method, the Grasshopper Optimization Algorithm (GOA) is used for better and more accurate learning of ANNs to reduce intrusion detection error rate.  ...  The role of the GOAMLP algorithm is to minimize the intrusion detection error in the neural network by selecting useful parameters such as weight and bias.  ...  By applying the optimized grasshopper optimization algorithm, optimal neural network weight and bias are selected.  ... 
doi:10.1109/access.2020.3040740 fatcat:jp5s5eotlbhtlhxaus5re2mp5u

Improved Binary Grasshopper Optimization Algorithm for Feature Selection Problem

Gui-Ling Wang, Shu-Chuan Chu, Ai-Qing Tian, Tao Liu, Jeng-Shyang Pan
2022 Entropy  
The binary grasshopper optimization algorithm (BGOA) is used for binary problems.  ...  In the aspect of the application, this paper selects 23 datasets of UCI for feature selection implementation. The improved algorithm yields higher accuracy and fewer features.  ...  Discussion The binary grasshopper optimization algorithm solves discrete problems such as feature selection.  ... 
doi:10.3390/e24060777 pmid:35741497 pmcid:PMC9223162 fatcat:63vimetkwjcwtaqzwffqdls7ny

Multiple Filter-Based Rankers to Guide Hybrid Grasshopper Optimization Algorithm and Simulated Annealing for Feature Selection with High Dimensional Multi-class imbalanced Datasets

Abdulrauf Garba Sharifai, Zurinahni Zainol
2021 IEEE Access  
This paper presents novel hybrid algorithms for feature selection with the high dimensional multi-class imbalanced problem using multiple filter-based rankers (MFR) and hybrid Grasshopper optimization  ...  The Simulated Annealing (SA) algorithm is incorporated into GOA. SA is used to enhance the best solution found by the GOA algorithm.  ...  , the features those satisfied the threshold value are selected and used as input to the wrapper method.  ... 
doi:10.1109/access.2021.3081366 fatcat:jek4bpndznexphy2esewwv5fpa

Parameter optimization of AISI 316 austenitic stainless steel for surface roughness by Grasshopper optimization algorithm

Omkar K. Kulkarni, Samidha Jawade, G. M. Kakandikar
2021 Journal of Mechanical Engineering, Automation and Control Systems  
The Grasshopper optimization algorithm (GOA) is the technique that is the most effective method for real-life applications.  ...  The nature-inspired algorithm is the best way to get the optimal value.  ...  Here grasshopper optimization algorithm proves its usefulness and applicability in real-life problem.  ... 
doi:10.21595/jmeacs.2021.22149 fatcat:wjoegjubzvfxreraf53ljmgosa


Sunil Boro
2021 Zenodo  
Features and constraint uses some capabilities of the algorithm used to modify it dynamically between the nodes and depot.  ...  Few methods such as ant colony optimization and genetic algorithm are considered for the route optimization. We can compare the performance of these methods to solve the VRP.  ...  Since, swarm intelligence optimization algorithmlike the Grasshopper Algorithm is more efficient to find the optimal solution, the contributions to this approach enhances the objective value.  ... 
doi:10.5281/zenodo.4659862 fatcat:l7qvyhc3u5dqnmabbbipegexae

Training Neural Networks by Enhance Grasshopper Optimization Algorithm for Spam Detection System

Sanaa A. A. Ghaleb, M. Mohamad, E. F. Hasan Syed Abdullah, Waheed A. H. M. Ghanem
2021 IEEE Access  
This article proposes a new Spam Detection System (SDS) framework, by using a series of six different variants of enhanced Grasshopper Optimization Algorithm (EGOAs), which are investigated and combined  ...  The results showed that the proposed MLP model trained by EGOAs achieves a higher performance compared to other optimization methods in terms of accuracy, detection rate, and false alarm rate.  ...  These efficient and durable algorithms are used to tackle various problems such as feature selection problems, single-objective problems, multi-objective problems, engineering design problems, and Machine  ... 
doi:10.1109/access.2021.3105914 fatcat:5eyolpsovrbevf6kq7ko3xoc6i

Grasshopper Optimization Algorithm with Crossover Operators for Feature Selection and Solving Engineering Problems

Ahmed A. Ewees, Marwa A. Gaheen, Zaher M. Yaseen, Rania M. Ghoniem
2022 IEEE Access  
Eventually, the obtained results are compared to a number of well-known algorithms over global optimization, feature selection datasets, and six real-engineering problems.  ...  Another promising nature-inspired algorithm is a salp swarm algorithm, denoted as SSA, an SI used to tackle optimization issues. In this paper, two phases are applied to propose cSG method.  ...  The proposed method was applied to solve 29 global optimization problems, feature selection (FS) tasks, and real-engineering problems.  ... 
doi:10.1109/access.2022.3153038 fatcat:ilzwk7bf3vbwlg4izlsdpdcmw4

Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science

Anwer Mustafa Hilal, Hadeel Alsolai, Fahd N. Al-Wesabi, Mohammed Abdullah Al-Hagery, Manar Ahmed Hamza, Mesfer Al Duhayyim
2022 Computers Materials & Continua  
Besides, a novel chaotic grasshopper optimization algorithm (CGOA) based feature selection technique is applied for the optimal selection of features.  ...  Finally, the efficiency of the FLNN model can be improvised by the use of cat swarm optimizer (CSO) algorithm.  ...  In addition, a novel chaotic grasshopper optimization algorithm (CGOA) based feature selection technique is applied for the optimal selection of features.  ... 
doi:10.32604/cmc.2022.021522 fatcat:ozonjmor35flraejzdehrwjyei

Spiral Motion Mode Embedded Grasshopper Optimization Algorithm: Design and Analysis

Zhangze Xu, Wenyong Gui, Ali Asghar Heidari, Guoxi Liang, Huiling Chen, Chengwen Wu, Hamza Turabieh, Majdi Mafarja
2021 IEEE Access  
In this experiment, we used the binary rule for feature selection. '0' indicates the related feature is not selected, while '1' means the feature is retained.  ...  of SGOA feature selection is far beyond other algorithms.  ... 
doi:10.1109/access.2021.3077616 fatcat:l6b2nq2emjf25f5wosnnoh46uy
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