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检索条件"主题词=Whale optimization algorithms"

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WSN Secure Routing Protocol Based on an Improved WOA Algorithm

WSN Secure Routing Protocol Based on an Improved WOA Algorit...

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Electronic Information Engineering and Computer Technology (EIECT), International Conference on

作者： Qundan Cao Xiuwu Yu University of South China Hengyang China

ISBN: (数字)9798331528850

ISBN: (纸本)9798331528867

With the development and prospects of intelligent networks, the Internet of Things is becoming increasingly popular. It aims to utilize network edges to achieve intelligent services and evaluation of the Internet of Things. In addition, this utilization enhances the user experience, and ensures service resilience in the face of any disaster scenario. IoT devices deploy distributed structures that are more responsive, with better quality of service and proximity to end ***, security is primarily considered to be the sensitivity to resist attacks. This work aims to introduce a new secure routing model, it chooses and encrypts by the best path. Firstly, the optimal path or node is selected for secure transmission while performing the optimal link-state multiplexing *** optimize path selection for destination and source, encryption is performed to ensure secure transmission. In this study, an improved WOA algorithm (IWOA) for security authentication was *** simulation results show that the IWOA protocol not only chooses a safer path but also has fewer hops on average, so that the nodes have more energy left on average and energy consumption is more uniform. In addition, the IWOA protocol has a higher average packet loss rate, which means that it is faster than the WOA protocol in excluding malicious nodes from the network. In the face of wormhole attacks, the WOA protocol is powerless, but the IWOA protocol can quickly reduce the trust value of the wormhole and expel it from the network.

关键词： Wireless communication Wireless sensor networks Packet loss Routing Routing protocols Energy efficiency whale optimization algorithms Internet of Things Data communication Faces

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Adaptive Swarm Intelligence-Based optimization Approach for Smart Grids Power Dispatch

Adaptive Swarm Intelligence-Based Optimization Approach for ...

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Emerging Technologies in Electronics, Computing and Communication (ICETECC), International Conference on

作者： Um-E-Habiba Alvi Ijaz Ahmed Syed Rizwan Hasan Babar Ashfaq Muhammad Raza Sana Mukhtar Department of Electrical Engineering PIEAS Islamabad Pakistan Department of Mechanical Engineering GIKI Topi KPK Pakistan Department of Economics PIDE Islamabad Pakistan Department of Electrical Engineering Sir Syed CASE Institute of Technology Islamabad Pakistan

Approximately 70–90 percent of today’s energy usage is derived from fossil fuels. Due to global warming and countless air pollutants, the atmosphere is polluted, compelling us to use new computer paradigms for efficient dispatching of smart energy grids. In order to solve the difficult nonlinear, non-convex, and constrained confined EEDs, a new whale optimization algorithm (WOA) is devised for the minimization of total cost and emission. The proposed method takes into account the social behaviour of humpback whales, with an emphasis on the nature of suggested meta-heuristic optimization algorithms. The proposed optimization paradigm generated data demonstrating AWSOA superiority to other advanced approaches. To ensure the efficacy of the proposed method, we compare it to other approaches and consider a number of technological constraints; the findings demonstrate that our approach is both cost-effective and accurate. In addition, the research assists electric energy firms in controlling emissions as required by emission regulatory agencies and saving the annual operational cost of thermal plants.

关键词： Costs Metaheuristics whales Atmosphere Minimization whale optimization algorithms Global warming

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whale optimization for Energy Efficient Healthcare System in WBAN Environment

Whale Optimization for Energy Efficient Healthcare System in...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： Ranjana Sharma Rahul Agarwal Department of Computing Science & IT Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9781665493970

Researchers have lately been interested in intelligent healthcare systems because of its significance in an IoT setting. In these scenarios, biomedical sensors are implanted into patients in order to collect data on their health status throughout a wireless body area network (WBAN). The suggested approach splits the body into an upper, lower, and centre section. A more refined version of the LEACH clustering technique is used in each area. As a further step, we apply an evolutionary technique called the whale optimization algorithm (WOA) to choose out our cluster leaders. For the purpose of assessing the efficacy of the suggested technique, the algorithm is implemented in a Matlab platform and evaluated to the conventional algorithms, which has been suggested before for the same kinds of problems. The suggested system provides longer battery life for devices in a wireless body area network, and it surpassed the alternatives.

关键词： Wireless communication Wireless sensor networks Clustering algorithms Medical services Pressing Body area networks whale optimization algorithms

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Automated Groundnut Leaf Disease Recognition Using whale optimization Algorithm with Deep Learning Model

Automated Groundnut Leaf Disease Recognition Using Whale Opt...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： G. Suresh K. Seetharaman Department of Computer and Information Science Annamalai University Chidambaram Tamil Nadu India

ISBN: (纸本)9781665489638

Groundnut is a significant oilseed crop in the world, and India is the second-largest producer of groundnuts. This crop can be attacked by several diseases that are predominant factors contributed towards the loss of degradation and productivity in the quality; leads to a low agricultural economy. Hence, there is a need to find more reliable and better automation solution to identify groundnut leaf disease. In this study, we propose an Automated Groundnut Leaf Disease Recognition using whale optimization Algorithm with Deep Learning (AGLDR-WOADL) technique. The presented AGLDR-WOADL technique comprises a series of operations. Primarily, threshold based segmentation is applied to identify the diseased portions of the image. Next, NASNet large feature extraction technique is exploited. Finally, the WOA with long short term memory (LSTM) method is utilized to recognize various kinds of plant diseases. To demonstrate the enhanced performance of the AGLDR-WOADL approach, an extensive range of simulations were performed. The comprehensive comparison study indicated the improved outcomes of the AGLDR-WOADL approach over other existing methods with higher accuracy of 99.63%.

关键词： Deep learning Productivity Image segmentation Crops Feature extraction whale optimization algorithms Reliability

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An enhanced whale optimization Algorithm based Control Strategy for LVRT Capability Enhancement of Grid-Connected DFIG-WECS

An enhanced Whale Optimization Algorithm based Control Strat...

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IEEE India Conference (INDICON)

作者： Arindam Chakraborty Tanmoy Maity Deparment of Electrical Engineering Indian Institute of Technology(ISM) Dhanbad Dhanbad Jharkhand India

ISBN: (纸本)9781665452724

A novel application of enhanced whale optimization algorithm (EWOA) is proposed in this research work to improve the low voltage ride through (LVRT) capability of the grid-connected doubly fed induction generator (DFIG) based wind energy conversion system (WECS). The EWOA is proposed in this article due to rapid convergence velocity, simple construction, and low computational burden. The proposed EWOA modifies the gains of all proportional-integral (PI) controllers at a high convergence speed. The optimal PI controllers are designed by the proposed EWOA. There are four PI controllers are employed in the rotor side converter (RSC), and another four PI controllers are applied in the grid side converter (GSC) under the cascaded structure. The DFIG is integrated into the grid through RSC and GSC with a common dc-link capacitor. The RSC is regulated to reduce the active power loss by using cascaded structure-based PI controllers, whereas GSC is regulated to keep constant the dc-link voltage and grid side reactive power. The validity of the proposed EWOA-PI controllers is verified by comparing the results with whale optimization algorithm (WOA) based PI controllers under severe grid disturbances. The LVRT capability is performed using the MATLAB program.

关键词： PI control Doubly fed induction generators Rotors whale optimization algorithms Steady-state Voltage control optimization

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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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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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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 ...

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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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