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检索条件"主题词=Whale optimization algorithms"

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ABC-DE-WOA: A New Hybrid Algorithm for optimization Problems

ABC-DE-WOA: A New Hybrid Algorithm for Optimization Problems

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International Conference on Circuits, Communication, Control and Computing

作者： Pradeep Kumar D Sowmya BJ Anita Kanavalli Annvin Vincent Dept. of Computer Science and Engineering M S Ramaiah Institute of Technology (Affliaited to VTU) Bangalore Dept. of Artificial Intelligence and Data Science M S Ramaiah Institute of Technology (Affliaited to VTU) Bangalore

One of the most effective swarm intelligence-based algorithms for many global optimization issues is the Artificial Bee Colony (ABC) strategy. Despite the fact that there are many Artificial Bee Colony (ABC) variants, the algorithm normally has a low convergence rate. Therefore, it is still essential to moderate an algorithm's intensity and diversity. In this instance, the standard Artificial Bee Colony (ABC) algorithm has been combined with the whale optimization Algorithm (WOA) and Differential Evolution Algorithm (DE) to generate a novel Hybrid Artificial Bee Colony algorithm (ABC), Artificial Bee Colony-Differential Evolution-whale optimization Algorithm (ABC-DE-WOA). For simple benchmark problems with up to 100 dimensions, 50 dimensions, 30 dimensions, and 10 dimensions, the proposed hybrid technique is compared with Artificial Bee Colony (ABC) variants like Artificial Bee Colony-whale optimization Algorithm (ABC-WOA), Artificial Bee Colony-Differential Evolution Algorithm (ABC-DE), and original Artificial Bee Colony Algorithm (ABC). The results show that the proposed technique performs better than its competitors in terms of convergence speed.

关键词： Benchmark testing Artificial bee colony algorithm whale optimization algorithms Hybrid power systems optimization Standards Convergence

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Improved whale optimization Algorithm for Optimal Network Coverage in Industrial Wireless Sensor Networks

Improved Whale Optimization Algorithm for Optimal Network Co...

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Future Trends in Smart Communities (ICFTSC), International Conference on

作者： P. Arun Mozhi Devan Rosdiazli Ibrahim Madiah Binti Omar Kishore Bingi Hakim Abdulrab Fawnizu Azmadi Hussin Department of Electrical and Electronic Engineering Universiti Teknologi PETRONAS Malaysia Department of Chemical Engineering Universiti Teknologi PETRONAS Malaysia

ISBN: (纸本)9798350334555

This paper aims to develop an improved whale optimization algorithm (IWOA) using naturally occurring spiral characteristics for optimal router placement in the industrial wireless sensor networks (IWSN) with adequate network connectivity and coverage for all the available clients in its network. The proposed algorithm uses the widely known Archimedean spiral characteristics for the humpback whale’s bubble-net hunting behaviour. The proposed algorithm is compared with the existing whale optimization algorithm (WOA) over multiple optimization benchmark test functions using various spiral patterns. Furthermore, the algorithm is additionally validated for the IWSN problem to obtain the optimal locations for placing the routers in the network to provide maximum connectivity and client coverage for all the available clients with possible minimization of the area overlapping. The numerical and convergence results show that using sin and cos based spiral behaviours improved the convergence rate by 42.866% and 100% in benchmark test functions and IWSN problem, respectively.

关键词： Wireless communication Wireless sensor networks Spirals Smart cities Benchmark testing Minimization whale optimization algorithms

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Predictive Hybrid Routing with Multi Objective optimization Model for Opportunistic Routing for VANET

Predictive Hybrid Routing with Multi Objective Optimization ...

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International Conference on Engineering Technology and their Applications (IICETA)

作者： Ali Hashim Abbas Ahmed Jamal Ahmed Sami Abduljabbar Rashid Nejood Faisal Abdulsattar Mustafa Hamid Hassan Mohammed Ahmed Jubair Mohammed I. Habelalmateen Department of Computer Technical engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna Iraq Department of Computer Engineering Techniques Al-Maarif University College Al-Anbar Iraq Department of Computer Technical Engineering College of Technical Engineering Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

Opportunistic network model is one among the prediction-based routing approach for effective transmission of data. In opportunistic network identification of routing path are critical task due to mobility and dynamic nature of network. In microscopic opportunistic routing approach, route prediction is an essential factor for effective communication. In addition to that decision making is also plays an important role in computation of routes. In order to overcome this entire problem we introduced a novel mechanism namely Fuzzy Set Theory with whale optimization Algorithm (FST-WOA). The first objective is to develop appropriate machine learning based that incorporates fuzzy set theory for prediction of routes in the network based on the distance, number of hop and energy level of nodes. ALEXNET architectural model is used for route prediction in fuzzy set theory. This fuzzy based ALEXNET model integrated with multi-objective optimization technique called whale optimization Algorithm (WOA). For the process of simulation Network Simulator (NS3) is used. The proposed method is tested and compared with the earlier models such as BOR and RLFGRP. The parameters used for the process of performance analysis are, latency calculation, Packet Delivery Ratio and routing overhead.

关键词： Simulation Vehicular ad hoc networks Predictive models Routing Prediction algorithms Fuzzy set theory whale optimization algorithms

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A Many-objective Chaotic whale optimization based Automatic Clustering Algorithm for Distributed Data Analysis in WSN

A Many-objective Chaotic Whale Optimization based Automatic ...

引用

Electrical Power Engineering, Communication and Computing Technology(ODICON), Odisha International Conference on

作者： Dinesh Kumar Kotary Satyasai Jagannath Nanda Dept. of Electronics and Communication Engg. Institute of Engineering Technology Lucknow Uttar Pradesh India Dept. of Electronics and Communication Engg. Malaviya National Institute of Technology Jaipur Rajasthan India

ISBN: (纸本)9781665478403

The potential applications of Wireless senor networks (WSN) has increased rapidly in weather reporting, target tracking, oxygen content monitoring and many other applications due to their low cost and less power requirements. The sensor nodes in WSN collect the dataset which is transferred to base station for further processing. Clustering is a key part of processing WSN dataset. To reduce the burden of base station researchers are using distributed approaches for clustering where individual sensor node performs cluster analysis with collaboration of neighboring nodes. Nature inspired algorithms provides better clustering results for WSN dataset in single and multi-objective framework. As the complexity of WSN data set increases the single and multi objective algorithms fails to give correct partitions when applied for clustering. To alleviate this problem this paper introduces an automatic distributed many-objective chaotic whale optimization algorithm (ADCWOA) to perform distributed clustering in WSN datasets. The proposed ADCWOA outperforms automatic distributed many objective particle swarm optimization (ADPSO) and automatic distributed K-Means (ADK-Means).

关键词： Wireless communication Wireless sensor networks Base stations Target tracking Chaotic communication Clustering algorithms whale optimization algorithms

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An Improved Resource Allocation in 5G C-RAN using Hybrid Swarm Intelligence Algorithm

An Improved Resource Allocation in 5G C-RAN using Hybrid Swa...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Voore Subba Rao G. Ravindra Babu Department of CSE APJ Abdul Kalam Technical University in Lucknow India Department of CSE Swami Vivekanand Subharti University Meerut India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

C-RAN is known as Cloud Radio Access Network is a centralized Radio Access Network is an architecture specialized design for radio access network for co-ordinate 2G, 3G,4G and 5G mobile communications for future wireless communications standards. In C-RAN architecture all BBUs are include in base stations will be connected to a Baseband Unit of a centralized pool.. The frequence known as RD is in-expensive RRH are in C-RAN architecture. The Basseband Units are efficient computational tasks are consuming more energy. It is essential to minimize energy consumption and maximize energy efficiency. The resource allocation is a strategy to satisfy Quality of Services, and balance energy efficiency this paper proposed a hybrid method of dynamic resource allocation management algorithm ITS-WOA(Improved Tunicate Swarm whale optimization) to minimize the energy consumption of Baseband Units and also minimize energy consumption by Remote Radio Access. The optimization method is to minimize usage of RRH mapping with BBU and deployed need for RRH and BBU deployment to minimize the operational cost. There should be a provision of active and sleep mode of selection for the both RRH and BBU to minimize energy. The active state shows BBU and RRH are in running state. And sleep mode state shows BBU and RRH are in sleep mode to minimize the energy consumption and maximize the energy utilization for the maintenance of Quality of Service in C-RAN architecture.

关键词： Energy consumption Costs Baseband Heuristic algorithms Computer architecture Quality of service whale optimization algorithms Energy efficiency Resource management Particle swarm optimization

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Research on Bridge Pier Crack Feature Image Recognition and Classification Method Based on IGOA-SVM

Research on Bridge Pier Crack Feature Image Recognition and ...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Yueyue Zong Jiahui Ye Zhiqiang Liu Feng Zhou School of Mechanical Engineering Jiangsu University of Science and Technology Zhenjiang China Marine Engineering and Electromechanical Research Laboratory Zhejiang Marine Development Research institute Zhoushan China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

In response to the issues of low automation, strong subjectivity in detection results, and serious impacts on the safety of bridges and public outings caused by existing methods for detecting cracks in bridge piers, this paper proposes an improved grasshopper optimization algorithm (IGOA) to optimize a support vector machine (SVM) classification model for crack detection. First, feature extraction of crack images is performed using a threshold segmentation method. Then, interference information such as surface impurities is removed using a binarization inverse operation and noise removal algorithm. The IGOA-SVM model is then employed to identify and classify the crack images of bridge piers. Finally, the accuracy and precision of the classification results are analyzed and compared among the SVM, grasshopper optimization algorithm (GOA)-SVM, and IGOA-SVM models. The results indicate that the proposed IGOA-SVM model improves both accuracy and precision, providing a new approach for the accurate identification of cracks in bridge piers.

关键词： Support vector machines Bridges Accuracy Data models whale optimization algorithms Classification algorithms Safety Surface cracks Particle swarm optimization optimization

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A Cooperative Method for Solving the Set-Union Knapsack Problem

A Cooperative Method for Solving the Set-Union Knapsack Prob...

引用

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Juntao Zhao Mhand Hifi College of Information Science and Engineering Northeastern University Shenyang China EPROAD UR 4669 UPJV Amiens France Laboratory EPROAD UR 4669 UPJV Amiens France

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

In this paper, a population-based method, along with various local operators, is proposed to tackle the set-union knapsack problem. The devised approach integrates diverse features to form a customized cooperative method. It includes a greedy procedure for generating an initial set of solutions, an optimized greedy repair and optimization operator to handle infeasibility, a tailored local operator for exploring the search space, and the integration of a multi-neighborhood tabu search operator to enhance the global best solution. These operators are integrated into an iterative search based on the whale optimization procedure, maintaining a focus on solution quality throughout the process. Finally, an experimental study is presented to evaluate the proposed method’s performance on benchmark instances extracted from the literature. The provided results are compared with those achieved by more recent methods available in the literature, highlighting the efficiency of the method and revealing several new solutions.

关键词： Process control Machine learning Maintenance engineering Benchmark testing whale optimization algorithms Space exploration Iterative methods Information technology optimization Mathematical programming

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Denial of Service (DoS) Attack Detection Using Feed Forward Neural Network in Cloud Environment

Denial of Service (DoS) Attack Detection Using Feed Forward ...

引用

Data Science and Network Security (ICDSNS), International Conference on

作者： Ponugoti Kalpana P. Srilatha Gudepu Sai Krishna Ahmad Alkhayyat Debarshi Mazumder Department of Computer Science and Engineering AVN Institute of Engineering and Technology Hyderabad India Department of AI & DS Chaitanya Bharathi Institute of Technology Hyderabad India Department of Information Technology Anurag University Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

In the recent years, the Denial of Service (DoS) developed as a big security threats for networks. Preventing as well as identifying DoS attacks is a big task within the industries. There are IoT of existing methods are there to identify DoS attacks and minimize the damage. However, many of these methods do not efficiently distinguish noise from a signal. This research focuses on addressing the DoS problem and introduces a method called Feed forward Neural Network (FNN). First, the KDD cup 99 data set goes through a preprocessing step where data normalization is processed using Min-Max normalization which standardizes the data points values. This normalized data is then passed to the whale optimization method which selects the relevant features making the classification system easier. The selected features are then used by an FNN classifier to differentiate between normal and attacked data. The proposed method is implemented and simulated on MATLAB and tested experimentally resulting to a detection accuracy of 0.99. The proposed FNN classifier is compared with the existing classifiers such as Auto-encoder and LSTM where it outperformed in detecting DoS attacks efficiently.

关键词： Industries Accuracy Neural networks Noise optimization methods Network security whale optimization algorithms Mathematical models Feeds Matlab

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Research on Infrared Thermal Imaging Zero-value Insulator Identification Based on WOA-SVM Algorithm

Research on Infrared Thermal Imaging Zero-value Insulator Id...

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Electrical Engineering and Mechatronics Technology (ICEEMT), IEEE International Conference on

作者： Haolin Li Song Yang Xiaolin Wang Lifeng Jia Wang Yong Xinqiang Wu Li Long Liu Chang Baishan Power Supply Company State Grid Jilin Electric Power Co. Ltd Baishan China

ISBN: (数字)9798350356472

ISBN: (纸本)9798350356489

Aiming at the problem that porcelain insulators are subjected to the influence of electromechanical loads and adverse weather conditions for a long period of time during operation, which leads to their deterioration and the occurrence of zero-value faults. In this study, an infrared thermal imaging zero-value insulator fault identification and diagnosis technique based on the combination of whale optimization Algorithm (WOA) and binary support vector machine (SVM) classifiers is proposed to determine whether a zero-value fault occurs. Firstly, the infrared image of porcelain insulator is preprocessed to obtain the target feature information of zero-value insulator, and the zero-value insulator data set is built according to the feature information. Then the zero-value insulator feature information is used to classify the vector machine, and its parameters are optimized by whale optimization Algorithm. Finally, the actual data is used as test set to test the proposed classification model. By comparing the classification and identification results of PSO-SVM, GS-SVM and WOA-SVM models, the comprehensive evaluation results show that the model obtained by the whale optimization Algorithm optimizing the support vector machine model can identify the zero-valued insulators more accurately and efficiently with a higher accuracy rate.

关键词： Support vector machines Solid modeling Porcelain whales Imaging Insulators whale optimization algorithms Vectors Classification algorithms optimization

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Research on Multi-Transmitter Coil Magnetically Coupled Resonance Wireless Power Transfer System

Research on Multi-Transmitter Coil Magnetically Coupled Reso...

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New Energy and Electrical Technology (ISNEET), International Symposium on

作者： Chuanbo Liu Mengyu Zhang Di Gai Shizhe Ma Yishu Han Guanhua Liu Honghan Qi Jiaolong Lv Xin Ming Yuanlong Bai State Grid Liaoning Electric Power Co. Ltd. Fushun Power Supply Company Fushun Liaoning

ISBN: (数字)9798331505905

ISBN: (纸本)9798331505912

The non-contact power supply approach can avoid many drawbacks of traditional wire-based power supply methods, ensuring the stability of wireless power transfer systems. This paper focuses on the working principle and system structure of magnetically coupled resonance wireless power transfer systems. Based on this, a system with multiple transmitter coils is designed, allowing the system's output power to remain constant under disturbances caused by variations in the coupling coefficient. The mutual inductance model and SS-type compensation structure are selected for design from a circuit perspective. A simulation model for the control design of the wireless power transfer system is built based on simulation software, and the whale optimization algorithm is utilized to tune and optimize the PID control parameters. Analysis of the optimized simulation results indicates that, with PID control, the system can achieve constant output power under variations in mutual inductance, i.e., changes in the coupling coefficient, and exhibits stronger disturbance rejection capabilities.

关键词： Coils Inductance PI control Power supplies Magnetic resonance Wireless power transfer whale optimization algorithms PD control Integrated circuit modeling Power generation

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