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Enhanced Gaussian bare-bones grasshopper optimization: Mitigating the performance concerns for feature selection

EXPERT SYSTEMS WITH APPLICATIONS 2023年 212卷

作者： Xu, Zhangze Heidari, Ali Asghar Kuang, Fangjun Khalil, Ashraf Mafarja, Majdi Zhang, Siyang Chen, Huiling Pan, Zhifang Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Wenzhou Business Coll Sch Informat Engn Wenzhou 325035 Peoples R China Zayed Univ Coll Technol Innovat Abu Dhabi U Arab Emirates Birzeit Univ Dept Comp Sci Birzeit 72439 Palestine Wenzhou Med Univ Affiliated Hosp 1 Wenzhou 325000 Peoples R China King Abdulaziz Univ Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia

As a recent meta-heuristic algorithm, the uniqueness of the grasshopper optimization algorithm (GOA) is to imitate the biological features of grasshoppers for single-objective optimization cases. Despite its advanced optimization ability, the basic GOA has a set of shortcomings that pose challenges in numerous practical scenarios. The GOA core limit is its early convergence to the local optimum and suffering from slow convergence. To mitigate these concerns, this study adopts the elite opposition-based learning and bare-bones Gaussian strategy to extend GOA's global and local search capabilities and effectively balance the exploration and exploitation inclinations. Specifically, elite opposition-based learning can help find better solutions at the early stage of exploration, while the bare-bones Gaussian strategy has an excellent ability to update the search agents. To evaluate the robustness of the proposed Enhanced GOA (EGOA) based on global constrained and unconstrained optimization problems, a straight comparison was made between the proposed EGOA and other meta-heuristics on 30 IEEE CEC2017 benchmark tasks. Moreover, we applied it experimentally to structural design problems and its binary version to the feature selection cases. Findings demonstrate the effectiveness of EGOA and its binary version as an acceptable tool for optimization and feature selection purposes.

关键词： grasshopper optimization algorithm Gaussian bare -bones strategy Elite opposition -based learning Structural design problems Feature selection

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Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis diagnosis

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 143卷 105206-105206页

作者： Xia, Jianfu Wang, Zhifei Yang, Daqing Li, Rizeng Liang, Guoxi Chen, Huiling Heidari, Ali Asghar Turabieh, Hamza Mafarja, Majdi Pan, Zhifang Shanghai Univ Dept Gen Surg Wenzhou Cent Hosp Affiliated Hosp 2 Wenzhou 325000 Zhejiang Peoples R China Zhejiang Prov Peoples Hosp Dept Hepatobiliary Pancreat & Minimally Invas Sur Hangzhou 310014 Peoples R China Wenzhou Polytech Dept Informat Technol Wenzhou 325035 Peoples R China Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Taif Univ Coll Comp & Informat Technol Dept Informat Technol POB 11099 Taif 21944 Saudi Arabia Birzeit Univ Dept Comp Sci Birzeit 72439 Palestine Wenzhou Med Univ Affiliated Hosp 1 Wenzhou 325000 Peoples R China

Preoperative differentiation of complicated and uncomplicated appendicitis is challenging. The research goal was to construct a new intelligent diagnostic rule that is accurate, fast, noninvasive, and cost-effective, distinguishing between complicated and uncomplicated appendicitis. Overall, 298 patients with acute appendicitis from the Wenzhou Central Hospital were recruited, and information on their demographic characteristics, clinical findings, and laboratory data was retrospectively reviewed and applied in this study. First, the most significant variables, including C-reactive protein (CRP), heart rate, body temperature, and neutrophils discriminating complicated from uncomplicated appendicitis, were identified using random forest analysis. Second, an improved grasshopper optimization algorithm-based support vector machine was used to construct the diagnostic model to discriminate complicated appendicitis (CAP) from uncomplicated appendicitis (UAP). The resultant optimal model can produce an average of 83.56% accuracy, 81.71% sensitivity, 85.33% specificity, and 0.6732 Matthews correlation coefficients. Based on existing routinely available markers, the proposed intelligent diagnosis model is highly reliable. Thus, the model can potentially be used to assist doctors in making correct clinical decisions.

关键词： Support vector machine Feature selection grasshopper optimization algorithm Appendicitis diagnosis Opposition-based learning

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UAV-Borne LiDAR Crop Point Cloud Enhancement Using grasshopper optimization and Point Cloud Up-Sampling Network

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REMOTE SENSING 2020年第19期12卷 3208-3208页

作者： Chen, Jian Zhang, Zichao Zhang, Kai Wang, Shubo Han, Yu China Agr Univ Coll Engn Beijing 100083 Peoples R China China Agr Univ Coll Water Resources & Civil Engn Beijing 100083 Peoples R China

Because of low accuracy and density of crop point clouds obtained by the Unmanned Aerial Vehicle (UAV)-borne Light Detection and Ranging (LiDAR) scanning system of UAV, an integrated navigation and positioning optimization method based on the grasshopper optimization algorithm (GOA) and a point cloud density enhancement method were proposed. Firstly, a global positioning system (GPS)/inertial navigation system (INS) integrated navigation and positioning information fusion method based on a Kalman filter was constructed. Then, the GOA was employed to find the optimal solution by iterating the system noise variance matrix Q and measurement noise variance matrix R of Kalman filter. By feeding the optimal solution into the Kalman filter, the error variances of longitude were reduced to 0.00046 from 0.0091, and the error variances of latitude were reduced to 0.00034 from 0.0047. Based on the integrated navigation, an UAV-borne LiDAR scanning system was built for obtaining the crop point. During offline processing, the crop point cloud was filtered and transformed into WGS-84, the density clustering algorithm improved by the particle swarm optimization (PSO) algorithm was employed to the clustering segment. After the clustering segment, the pre-trained Point Cloud Up-Sampling Network (PU-net) was used for density enhancement of point cloud data and to carry out three-dimensional reconstruction. The features of the crop point cloud were kept under the processing of reconstruction model;meanwhile, the density of the crop point cloud was quadrupled.

关键词： UAV-borne LiDAR scanning system grasshopper optimization algorithm GPS INS integrated navigation point cloud up-sampling network (PU-net) clustering segmentation 3-dimensional reconstruction

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A novel application of integrated grasshopper optimization heuristics for attenuation of noise interferences

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AIN SHAMS ENGINEERING JOURNAL 2022年第2期13卷 101536-101536页

作者： Khan, Wasim Ullah Raja, Muhammad Asif Zahoor He, Yigang Chaudhary, Naveed Ishtiaq Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan COMSATS Univ Islamabad Dept Elect & Comp Engn Attock Campus Attock 43600 Pakistan Int Islamic Univ Dept Elect Engn Islamabad Pakistan

Novel application of integrated swarming intelligence computing paradigm is exploited for reliable treatment of nonlinear active noise control (ANC) systems using global search capacity of grasshopper optimization algorithm (GOA) combined with local search efficacy of sequential quadratic programming (SQP), i.e., GOA-SQP. The designed optimization mechanism GOA-SQP is applied to minimize the cost function of ANC controller incorporating the nonlinear Volterra filtering having linear/nonlinear primary/secondary paths in case of different noise interferences of sinusoidal, random, and complex random type signals. The comparison of the results through statistical observations in terms of accuracy, convergence and complexity indices reveals that the GOA-SQP based ANC controllers are operative, resilient and viable.(c) 2021 The Authors. Production and hosting by Elsevier B.V. on behalf of Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Active noise control grasshopper optimization algorithm Sequential quadratic programming System identification Volterra filtering

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A novel meta-matching approach for ontology alignment using grasshopper optimization

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KNOWLEDGE-BASED SYSTEMS 2020年 201卷 106050-106050页

作者： Lv, Zhaoming Peng, Rong Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

Ontology alignment is a fundamental task to support information sharing and reuse in heterogeneous information systems. Optimizing the combination of matchers by evolutionary algorithms to align ontology is an effective method. However, such methods have two significant shortcomings: weights need to be set manually to combine matchers, and a reference alignment is required during the optimization process. In this paper, a meta-matching approach GSOOM for automatically configuring weights and threshold using grasshopper optimization algorithm (GOA) has been proposed. In this approach, the ontology alignment problem is modeled as optimizing individual fitness of GOA. A fitness function is proposed, which includes two goals: maximizing the number of matching and the similarity score. Since it does not require an expert to provide a reference alignment, it is more suitable for real-world scenarios. To demonstrate the advantages of the approach, we conduct exhaustive experiments tasks on several standard datasets and compare its performance to other state-of-the-art methods. The experimental results illustrate that our approach is more efficiently and is significantly superior to other metaheuristic-based methods. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Ontology matching grasshopper optimization algorithm Ontology alignment evaluation initiative

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Performance optimization of Support Vector Machine with Adversarial grasshopper optimization for Heart Disease Diagnosis and Feature Selection

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第8期15卷 117-128页

作者： Tang, Nan Wang, Lele Li, Kangming Liu, Zhen Dai, Yanan Hao, Ji Zhang, Qingdui Sun, Huamei Qi, Chunmei Xuzhou Med Univ Affiliated Hosp 2 Dept Cardiol Xuzhou 221000 Jiangsu Peoples R China

The World Health Organization reports that cardiac disorders result in approximately 1.02 million deaths. Over the last years, heart disorders, also known as cardiovascular diseases, have significantly influenced the medical sector due to their immense global impact and high level of danger. Unfortunately, accurate prognosis of heart problems or CD, as well as continuous monitoring of the patient for 24 hours, is unattainable due to the extensive expertise and time required. The management and identification of cardiac disease pose significant challenges, particularly in impoverished or developing nations. Moreover, the absence of adequate medical attention or prompt disease management can result in the individual's demise. This study presents a novel optimization technique for diagnosing cardiac illness utilizing Support Vector Machine (SVM) and grasshopper optimization algorithm (GOA). The primary objective of this approach is to identify the most impactful characteristics and enhance the efficiency of the SVM model. The GOA algorithm, which draws inspiration from the natural movements of grasshoppers, enhances the search for features in the data and effectively reduces the feature set while maintaining prediction accuracy. The initial stage involved pre-processing the ECG data, followed by its classification using several algorithms such as SVM and GOA. The findings demonstrated that the suggested approach has markedly enhanced the effectiveness and precision of heart disease diagnosis through meticulous feature selection and model optimization. This approach can serve as an efficient tool for early detection of heart disease by simplifying the process and enhancing its speed.

关键词： Heart disease predictions Support Vector Machine grasshopper optimization algorithm feature selection

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A hybrid feed-forward neural network with grasshopper optimization for observing pattern of scour depth around bridge piers

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ARABIAN JOURNAL OF GEOSCIENCES 2021年第22期14卷 2352-2352页

作者： Kaveh, Keivan Dang Nguyen Mai Quoc Bao Pham Duong Tran Anh Tech Univ Munich Inst Hydraul & Water Resources Engn Arcisstr 21 D-80333 Munich Germany Thuyloi Univ Ctr Int Educ 175 Tay Son Hanoi Vietnam Thu Dau Mot Univ Inst Appl Technol Thu Dau Mot City Binh Duong Prov Vietnam Ho Chi Minh City Univ Technol HUTECH 475A Dien Bien Phu St Ho Chi Minh City Vietnam

This paper uses the recently proposed grasshopper optimization algorithm (GOA) to develop a new hybrid, stochastic training algorithm for the feed-forward neural networks (FFNN). The state-of-the-art hybrid model is then applied to study observing pattern of scour depth which is a challenging problem in hydraulic engineering. In order to verify and control the accuracy, stability, and efficiency of the proposed model and its computational process, the model results are compared to networks trained by three different training algorithms. To achieve this, backpropagation, backpropagation with momentum, and Levenberg-Marquardt learning algorithms, which are widely used for various hydraulic problems, are chosen. The results of the model indicated that the proposed model has high stability and performance in solving regression problems. The comparison of prediction accuracy and convergence curves represented that the model could predict the maximum scour depth with the higher convergence speed. Applying the proposed model improves the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) values by approximately 6%, 30%, and 18%, respectively.

关键词： Feed-forward neural networks grasshopper optimization algorithm Pier scour

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Dental radiography segmentation using expectation-maximization clustering and grasshopper optimizer

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第29-30期79卷 22027-22045页

作者： Qaddoura, Raneem Al Manaseer, Waref Abushariah, Mohammad A. M. Alshraideh, Mohammad Aref Philadelphia Univ Informat Technol Amman Jordan Univ Jordan King Abdullah II Sch Informat Technol Dept Comp Sci Amman Jordan Univ Jordan King Abdullah II Sch Informat Technol Dept Comp Informat Syst Amman Jordan

Image segmentation is a popular technique that is used for extracting information from images, which has also gained a lot of interest lately due to its importance in different scientific fields such as the medical field. This paper proposes a novel image segmentation technique using Expectation-Maximization (EM) clustering algorithm and grasshopper Optimizer algorithm (GOA). The proposed technique and the concept of image segmentation are effectively applied on dental radiography datasets that are collected from 120 patients with an age between 6 to 60 years old. To validate the proposed technique, a comparison in terms of purity and entropy measures is conducted against K-means, X-means, EM, and Farthest First algorithms. Based on our experimental results, the proposed technique using EM and GOA achieved the best results compared to other algorithms for all three datasets in terms of entropy and purity. The best results were obtained using the second dataset, which achieved purity value of 0.7126 and entropy value of 0.3083. Further, the proposed technique also outperforms U-net and Random Forest algorithms for the selected datasets.

关键词： Image segmentation Expectation-Maximization algorithm grasshopper optimization algorithm Dental radiography Anatomical segmentation and classification

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A Novel Community Detection algorithm Based on Paring, Splitting and Aggregating in Internet of Things

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IEEE ACCESS 2020年 8卷 123938-123951页

作者： Li, Yizhe Xia, Hui Zhang, Rui Hu, Benxu Cheng, Xiangguo Qingdao Univ Coll Comp Sci & Technol Qingdao 266000 Peoples R China Ocean Univ China Coll Informat Sci & Engn Qingdao 266000 Peoples R China

The explosive growth of Internet of Things (i.e., IoT) terminal equipment makes its topology more complex, which leads to the increasing cost of network research. Recently, the implicit community structure is widely used to improve the efficiency of research. However, most of the non-overlapping community detection algorithms have some weakness, such as the large number of community detected and the obvious scale gap between communities. To address these abovementioned problems, we design a novel non-overlapping community detection algorithm, named as Pairing, Splitting and Aggregating algorithm (i.e., PSA). Firstly, in order to improve the accuracy of community division, a new node similarity index is designed to transform the network into a large number of similar node pairs. Secondly, based on the connected branches composed of similar node pairs, the network is further divided into several similar node sets. Thirdly, to balance the scale gap of different communities, the grasshopper optimization algorithm, (i.e., GOA) is introduced to combine the local attribute (i.e., conductance) and global attribute (i.e., modularity) together to aggregate similar node sets into potential (or final) communities. Finally, the experimental results show that PSA not only controls the difference among communities well, but also outperforms the other four popular algorithms in terms of two metrics. Moreover, we propose a community-based resource discovery method (or scheme), named as Community-assisted Short-distance-query Resource Discovery algorithm (i.e., CSRD) to further verify the efficiency of PSA. The results show that the resource discovery efficiency of CSRD using PSA is better compared with other algorithms.

关键词： Community detection grasshopper optimization algorithm Internet of Things non-overlapping community resources discovery

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Analysis and Design of Auxiliary Array With Intelligent optimization for Interference Suppression of Vehicular Communication

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第6期73卷 8295-8305页

作者： Guo, Yu Wang, Hengfeng Wu, Hao Ge, Songhu Yang, Zhong Meng, Jin Naval Univ Engn Natl Key Lab Electromagnet Energy Wuhan 430000 Peoples R China

The auxiliary array is the key to improving the interference suppression performance in vehicular communication, especially for various operating frequencies of radio communication and unknown directions of interference. In this paper, the effects of the auxiliary array parameters on the interference suppression performance are analyzed, and the maximum acceptable area model is proposed. After deriving and simplifying the analytical solutions of the weights and the signal to interference plus noise ratio (SINR) after interference suppression, the efficient acceptable rule inherits suppression requirements of the communication radio, significantly reducing computational complexity. The proposed model focuses on maximizing the acceptable area in view of this rule. The gain of the auxiliary antenna is also obtained by the proposed model. Based on theoretical analysis, we designed the auxiliary structure, which can fully absorb the advantages of the loading and matching technology and avoid the limitations of the independent strategy. However, the structure parameters, including the topology and components of the antenna, are complex and interactive. Therefore, the intelligent method named the homogeneous grasshopper optimization algorithm was adopted to consider these parameters comprehensively. Finally, it leads to a remarkable improvement in the optimization efficiency for not only the spacing but also the antenna parameters. Experiments based on communication radio demonstrate that the proposed model is very efficient and promising for auxiliary array optimization, and the implemented antenna achieves ideal radiation characteristics and high gain in a wide frequency band.

关键词： optimization Signal to noise ratio Antennas Interference suppression Adaptive arrays Broadband antennas Loaded antennas Auxiliary array interference suppression maximum acceptable area signal to interference noise ratio grasshopper optimization algorithm

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