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Wild Horse Optimizer-Based Spiral Updating for Feature Selection

IEEE ACCESS 2022年 10卷 106258-106274页

作者： Ewees, Ahmed A. Ismail, Fatma H. Ghoniem, Rania M. Univ Bisha Coll Comp & Informat Technol Dept Informat Syst Bisha 61922 Saudi Arabia Damietta Univ Dept Comp Dumyat 34517 Egypt Misr Int Univ Fac Comp Sci Cairo 11341 Egypt Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 11671 Saudi Arabia

Feature selection (FS) is a vital and challenging process in several domains, including data mining, data clustering, text mining, education, biology, medicine, public health, machine learning, image processing, and so on. The greedy and comprehensive algorithm methods cannot identify the best subset amid the rising number of features. Thus, swarm-based algorithms are becoming more popular for identifying the best group of features. This study relies on the spiral-updated position of the whale optimization algorithm (WOA) to propose an improved version of the Wild Horse Optimizer (WHO). This improvement enhances the WHO's ability to update solutions and explore various possibilities in the search domain. The proposed method (WHOW) was assessed using two experiments to confirm the efficacy of the improved optimizer. The first experiment was global optimization using CEC 2019 benchmark functions, whereas the second was an FS by testing 20 benchmark datasets. The results obtained using the proposed WHOW method were compared to some popular algorithms over the benchmark datasets of global optimization and FS. The experimental results reflect the superiority of WHOW in the solutions to different optimization problems and its ability to select prominent features over most benchmark datasets. These results are due to the implementation of bubble nets in the WHO using spiral movement, which promotes flexibility and performance.

关键词： Convergence Behavioral sciences Statistics Social factors Benchmark testing Metaheuristics whale optimization algorithms Feature extraction Wild horse optimizer whale optimization algorithm spiral updating position global optimization feature selection

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Operational Carbon Change in Commercial Buildings under the Carbon Neutral Goal: A LASSO-WOA Approach

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BUILDINGS 2022年第1期12卷 54页

作者： Xiang, Xiwang Ma, Xin Ma, Zhili Ma, Minda Chongqing Univ Sch Management Sci & Real Estate Chongqing 400045 Peoples R China Southwest Univ Sci & Technol Sch Sci Mianyang 621010 Sichuan Peoples R China Chongqing Univ Sch Architecture & Urban Planning Chongqing 400045 Peoples R China Chongqing Univ Key Lab New Technol Construct Cities Mt Area Minist Educ Chongqing 400045 Peoples R China Tsinghua Univ Dept Earth Syst Sci Beijing 100084 Peoples R China

The rapid growth of energy consumption in commercial building operations has hindered the pace of carbon emission reduction in the building sector in China. This study used historical data to model the carbon emissions of commercial building operations, the LASSO regression was applied to estimate the model results, and the whale optimization algorithm was used to optimize the nonlinear parameter. The key findings show the following: (1) The major driving forces of carbon emissions from commercial buildings in China were found to be the population size and energy intensity of carbon emissions, and their elastic coefficients were 0.6346 and 0.2487, respectively. (2) The peak emissions of the commercial building sector were 1264.81 MtCO(2), and the peak year was estimated to be 2030. Overall, this study analyzed the historical emission reduction levels and prospective peaks of carbon emissions from China's commercial buildings from a new perspective. The research results are helpful for governments and decision makers to formulate effective emission reduction policies and can also provide references for the low-carbon development of other countries and regions.

关键词： carbon dioxide mitigation commercial building operations LASSO regression whale optimization algorithm carbon emissions peak

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Power and energy system oscillation damping using multi-verse optimization

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SN APPLIED SCIENCES 2021年第3期3卷 1-18页

作者： Devarapalli, Ramesh Bhattacharyya, Biplab Indian Inst Technol Dept Elect Engn Indian Sch Mines Dhanbad Jharkhand India

Power system oscillations are the primary threat to the stability of a modern power system which is interconnected and operates near to their transient and steady-state stability limits. Power system stabilizer (PSS) is the traditional controller to damp such oscillations, and flexible AC transmission system (FACTS) devices are advised for the improved damping performance. This paper suggests a technique for controller parameters tuning of PSS and a shunt connected FACTS device to be operated in coordination. A static synchronous compensator (STATCOM) connected in a two-machine system is considered as a test power system for the system studies. A recent meta-heuristic algorithm, Multi-Verse optimizer (MVO) has been suggested and compared with the other state-of-the-art algorithms. Improvement in system damping has been achieved by minimizing the oscillating nature of the system states by framing the objective function as a function of damping ratio and location of poles of the system. The Phillips-Heffron model of the test system has been designed by considering the system dynamics. The coordinated system behavior under the perturbation in system parameters has been observed satisfactory with the tuned controller parameters obtained from the suggested algorithm.

关键词： Power system oscillations damping Power system stabilizers STATCOM whale optimization algorithm Gray wolf optimization Multi-verse optimizer

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Energy-Efficient Bi-Objective optimization Based on the Moth-Flame algorithm for Cluster Head Selection in a Wireless Sensor Network

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PROCESSES 2023年第2期11卷 534页

作者： Mistarihi, Mahmoud Z. Salameh, Haythem A. Bany Alsaadi, Mohammad Adnan Beyca, Omer F. Heilat, Laila Al-Shobaki, Raya Yarmouk Univ Hijjawi Fac Engn Technol Irbid 21163 Jordan Liwa Coll Technol Fac Engn Dept Mech & Ind Engn POB 41009 Abu Dhabi U Arab Emirates Al Ain Univ Coll Engn POB 64141 Al Ain U Arab Emirates Istanbul Tech Univ Fac Engn Ind Engn Dept TR-34469 Istanbul Turkiye

Designing an efficient wireless sensor network (WSN) system is considered a challenging problem due to the limited energy supply per sensor node. In this paper, the performance of several bi-objective optimization algorithms in providing energy-efficient clustering solutions that can extend the lifetime of sensor nodes were investigated. Specifically, we considered the use of the Moth-Flame optimization (MFO) algorithm and the Salp Swarm algorithm (SSA), as well as the whale optimization algorithm (WOA), in providing efficient cluster-head selection decisions. Compared to a reference scheme using the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, the simulation results showed that integrating the MFO, SSA or WOA algorithms into WSN clustering protocols could significantly extend the WSN lifetime, which improved the nodes' residual energy, the number of alive nodes, the fitness function and the network throughput. The results also revealed that the MFO algorithm outperformed the other algorithms in terms of energy efficiency.

关键词： moth-flame algorithm bi-objective optimization salp swarm algorithm whale optimization algorithm LEACH protocol

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Secure and Efficient Data Transmission for IoT Devices in Untrusted Environments

Secure and Efficient Data Transmission for IoT Devices in Un...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jain, Upma Smitha, K. Battula, R.S.S. Raju Sarkar, Arindam Martin, Natasha Natrayan, L. Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Adichunchanagiri University Bgs Institute of Technology Department of Artificial Intelligence and Machine Learning India Aditya University Department of Computer Applications India Heritage Institute of Technology Department of Applied Electronics and Instrumentation Engineering Kolkata India Department of Law Symbiosis Law Pune India School of Engineering Simats Department of Mechanical Engineering Saveetha India

ISBN: (纸本)9798350375442

One technological trend that will not go away in the modern world is the Internet of Things (IoT). With the development of the Internet of Things, the world is becoming smarter. The efficacy, flexibility, and simplicity of deployment of IoT make it the preferred choice for most real-time applications. But energy efficiency and security are the big obstacles for the Internet of Things. Presented here are safe, energy-efficient solutions for the Internet of Things (IoT), a problem whose significance this study acknowledges. Since routing is the primary energy consumer, this study presents secure routing strategies that are both energy efficient and reliable. Three distinct safe data transmission techniques for the Internet of Things (IoT) are presented in this paper, all of which aim to reduce energy usage during routing. The first part of this study lays out a clustering-and secure route selection-based architecture for safe and energy-efficient data transmission with the Internet of Things (IoT). The W-HHO technique, which is based on whale optimization, is used to identify the secure routes, and fuzzy-based secure clustering is used for that purpose. Encryption protocols that work in tandem with cypher block chaining ensure the safety of sensitive information. Using industry-standard metrics like packet delivery rate, throughput, energy efficiency, network longevity, etc., we assess how well each solution performs. When compared to the current methods, the outcomes produced by the presented works demonstrate promising performance. © 2024 IEEE.

关键词： clustering data transmission energy efficiency fuzzy clustering harris hawks optimization (HHO) internet of things (IoT) secure routing security whale optimization algorithm

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A new method based on a WOA-optimized support vector machine to predict the tool wear

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第9-10期121卷 6439-6452页

作者： Cheng, Yaonan Gai, Xiaoyu Jin, Yingbo Guan, Rui Lu, Mengda Ding, Ya Harbin Univ Sci & Technol Minist Educ Key Lab Adv Mfg & Intelligent Technol Harbin 150080 Peoples R China

Tool wear has been a great impact on machining quality and machining efficiency during cutting. The serious tool wear will even lead to workpiece failure and catastrophic equipment failure. Accurate and effective tool wear monitoring is important to evaluate the degree of tool wear, replace tools in time, and promote the intelligent development of the manufacturing industry. To improve the accuracy of online prediction of tool wear, a new method based on whale optimization algorithm (WOA) optimized support vector machine (SVM) is proposed to predict the tool wear. Specifically, the multi-domain features of cutting force and vibration signals are extracted based on the time domain, frequency domain, and time-frequency domain, and the signal sensitive features closely related to tool wear are selected by the Pearson correlation coefficient method. SVM is applied to predict the evolution of tool wear. WOA is used to improve prediction accuracy by optimizing the internal parameters of SVM. By learning the nonlinear correlation between sensitive features and tool wear, a model for predicting tool wear based on WOA-SVM is constructed to predict the change of tool wear value. The effectiveness and prediction performance of the proposed method are verified by milling experiments. Results show that this method can predict tool wear value based on limited historical data information accurately and effectively. Compared with SVM prediction methods optimized by some common optimization algorithms (particle swarm optimization (PSO) and genetic algorithm (GA)), the prediction accuracy and stability are higher and the generalization is stronger. These findings may be of great significance for the improvement of machining quality and efficiency of parts, the stable operation of manufacturing system, and the intelligent development of manufacturing industry.

关键词： Tool wear prediction Sensitive feature selection whale optimization algorithm Support vector machine WOA-SVM model

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Pulmonary Diffuse Airspace Opacities Diagnosis from Chest X-Ray Images Using Deep Convolutional Neural Networks Fine-Tuned by whale Optimizer

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WIRELESS PERSONAL COMMUNICATIONS 2022年第2期124卷 1355-1374页

作者： Wang, Xusheng Gong, Cunqi Khishe, Mohammad Mohammadi, Mokhtar Rashid, Tarik A. Xian Univ Technol Xian 710048 Shaanxi Peoples R China Jining 1 Peoples Hosp Dept Clin Lab Jining 272011 Shandong Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran Lebanese French Univ Coll Engn & Comp Sci Dept Informat Technol Erbil Kurdistan Regio Iraq Univ Kurdistan Hewler Sci & Engn Sch Comp Sci & Engn Dept Erbil KRG Iraq

The early diagnosis and the accurate separation of COVID-19 from non-COVID-19 cases based on pulmonary diffuse airspace opacities is one of the challenges facing researchers. Recently, researchers try to exploit the Deep Learning (DL) method's capability to assist clinicians and radiologists in diagnosing positive COVID-19 cases from chest X-ray images. In this approach, DL models, especially Deep Convolutional Neural Networks (DCNN), propose real-time, automated effective models to detect COVID-19 cases. However, conventional DCNNs usually use Gradient Descent-based approaches for training fully connected layers. Although GD-based Training (GBT) methods are easy to implement and fast in the process, they demand numerous manual parameter tuning to make them optimal. Besides, the GBT's procedure is inherently sequential, thereby parallelizing them with Graphics Processing Units is very difficult. Therefore, for the sake of having a real-time COVID-19 detector with parallel implementation capability, this paper proposes the use of the whale optimization algorithm for training fully connected layers. The designed detector is then benchmarked on a verified dataset called COVID-Xray-5k, and the results are verified by a comparative study with classic DCNN, DUICM, and Matched Subspace classifier with Adaptive Dictionaries. The results show that the proposed model with an average accuracy of 99.06% provides 1.87% better performance than the best comparison model. The paper also considers the concept of Class Activation Map to detect the regions potentially infected by the virus. This was found to correlate with clinical results, as confirmed by experts. Although results are auspicious, further investigation is needed on a larger dataset of COVID-19 images to have a more comprehensive evaluation of accuracy rates.

关键词： COVID-19 whale optimization algorithm Deep convolutional neural networks Chest X-rays

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Joint Association and Power Allocation for Data Collection in HAP-LEO-Assisted IoT Networks 37

Joint Association and Power Allocation for Data Collection i...

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37th International Conference on Information Networking (ICOIN)

作者： Ei, Nway Nway Aung, Pyae Sone Park, Seong-Bae Huh, Eui-Nam Hong, Choong Seon Kyung Hee Univ Dept Comp Sci & Engn Yongin 17104 Gyeonggi Do South Korea

ISBN: (纸本)9781665462686

Low earth orbit (LEO) satellites and high altitude platforms (HAPs) have recently gained popularity due to its seamless connectivity and global coverage. The aerial network comprising high altitude platforms (HAPs) collects the generated data of the ground internet of things (IoT) devices and then transmits the aggregated data to the terrestrial data processing center via the LEO satellites. However, LEO satellites are generally orbiting with high speed and their visibility times are limited according to the elevation angle and orbital height. Moreover, it is challenging for the HAPs to determine the feasible LEO satellite to associate with and decide the optimal transmit power due to their power constraint. Therefore, in this paper, we formulate a joint association and power allocation problem to maximize the data transmitted by the HAPs and minimize the energy consumption by considering the HAP's power budget and QoS requirements as well as the data storage and visibility time of the LEO satellites. To address the formulated mixed-integer non-convex problem, the two sub-problems are introduced, namely, HAP-LEO satellite association and power allocation problems, which are then alternately solved by using the GUROBI optimizer and whale optimization method, respectively. The evaluation results have demonstrated that the proposed approach achieves the superior performance when compared to the random scheme.

关键词： Low earth orbit satellites high altitude platforms LEO satellite's visibility time whale optimization algorithm

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Robust and lossless data privacy preservation: optimal key based data sanitization

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EVOLUTIONARY INTELLIGENCE 2022年第2期15卷 1123-1134页

作者： Shailaja, G. K. Rao, C. V. Guru Kakatiya Inst Technol & Sci Dept IT Warangal 506015 Telangana India SR Engn Coll Dept CSE Warangal 506371 Telangana India

Privacy-preserving data mining (PPDM) is the most significant approach on data security, in which more research work is under progress. This paper intends to propose a new PPDM model that includes two phases: data sanitization and restoration. Initially, association rules get extracted for proceeding the mentioned phases. The first and foremost tactic on the proposed privacy preservation model is the generation of the optimal key that is used to produce the sanitized data from the original data. The same key takes complete responsibility for processing the data restoration process by the receiver. As the key extraction plays a major role, this paper intends to propose a new hybrid algorithm;Trial based update on whale and particle swarm algorithm (TU-WPA) for selecting the optimal key. The proposed method is the combination of particle swarm optimization and whale optimization algorithm. More importantly, the research issues such as hiding failure rate, information preservation rate, false rule generation and degree of modification are minimized through the proposed sanitization and restoration processes. Finally, the performance of the proposed TU-WPA model is verified over other conventional models.

关键词： Privacy preservation Ant colony algorithm whale optimization algorithm Data sanitization Data restoration

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Comparison of various electricity market pricing strategies to reduce generation cost of a microgrid system using hybrid WOA-SCA

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EVOLUTIONARY INTELLIGENCE 2022年第3期15卷 1587-1604页

作者： Dey, Bishwajit Bhattacharyya, Biplab IIT ISM Dept Elect Engn Dhanbad Bihar India

This paper aims to minimize the generation cost of a low voltage (LV) grid-connected microgrid system using a novel hybrid whale optimization algorithm (WOA)- Sine cosine algorithm (SCA) optimization technique. Three different methods of electricity market prices are formulated in turns and the generation cost is evaluated for each method to sort out the best among them which yields the least generation cost of the same microgrid system. Both active and passive participation of grid are studied to acknowledge its importance. Numerical results show that the time of usage (TOU) method of electricity market pricing proved to be the most convenient and economic method for cost analysis of the system. The generation cost was at its maximum when the electricity price was a constant value. Also during the TOU method of electricity pricing, a 15% surge in the price was realized when the grid was passively participating in supplying power to the system. Figures and statistical data aids the fact that proposed hybrid WOASCA outperformed WOA in consistently providing a robust and superior quality result.

关键词： Microgrid Optimal scheduling whale optimization algorithm Sine cosine algorithm Electricity market

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