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检索条件"主题词=Whale optimization algorithms"

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Parallel whale optimization Algorithm-Kmeans for Image Segmentation using Multiprocessing

Parallel Whale Optimization Algorithm-Kmeans for Image Segme...

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iNnovative Informatics of Biskra (ISNIB), International Symposium on

作者： Ali Mezaghrani Mohammed Debakla Khalifa Djemal Departement of computer science LISYS Laboratory Faculty of exact science University of Mustapha Stambouli Mascara Algeria IBISC Laboratory Evry Val d'Essone University Evry Every France

ISBN: (数字)9798331509576

ISBN: (纸本)9798331509583

Image segmentation is an important step in inter-preting and analyzing images, allowing computers and algorithms to process the content of an image accuratly and more effectively. Image segmentation is a crucial step in image preprocessing, while Kmeans is a widely-used clustering algorithm for im-age segmentation. However, the whale optimization Algorithm (WOA) is a nature-inspired metaheuristic optimization tech-nique known for its simplicity and global search capability. In this study, a hybrid approach was proposed, which combined WOA with Kmeans algorithm (WOA-Kmeans) to optimize image segmentation process, in the other hand, a parallel strategy was implemented for enhancing both segmentation quality and execution time. Traditional Kmeans suffers from sensitivity to initial cluster centroids, often leading to suboptimal results. By integrating WOA, the centroids initialization is improved, guiding Kmeans towards better convergence. Additionally, the parallelization of WOA-Kmeans using multiprocessing enables faster computation by distributing the work among multiple processes, making it suitable for high-resolution image processing tasks. This combination of WOA-Kmeans in a multiprocess environment ensures efficient and accurate image segmentation. The results demonstrate that the Parallel WOA-Kmeans outperform the sequentiel WOA-Kmeans in term of several performance measures, such as, accuracy, Root Mean Squar Error (RMSE), Peak signal to noise ratio (PSNR), and structural similarity index (SSIM).

关键词： Image segmentation PSNR Accuracy Sensitivity Metaheuristics Measurement uncertainty Clustering algorithms whale optimization algorithms Noise measurement Informatics

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Reducing Task Scheduling Time in Cloud Computing using Novel Improved whale optimization Algorithm over Ant Colony Algorithm

Reducing Task Scheduling Time in Cloud Computing using Novel...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： P. Sushma Priya S. John Justin Thangaraj Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

To decrease Cloud Computing Task Scheduling Time utilising Novel Improved whale Optimisation Algorithm over Ant Colony Algorithm. A Novel Improved whale Optimisation Algorithm schedules tasks in cloud computing. Cloud computing involves internet-based distribution of data storage, servers, databases, networking, and applications. Task scheduling data is obtained using cloudsim. We analyse, create, and implement cloud computing application framework and cloud computing infrastructures. After data import, Improved whale Optimisation and Ant Colony Algorithm are tried. Two algorithms have two groups. Each group has 120 samples with 80% G power, 0.05% threshold, and 95% CI. Task Scheduling Time (ms) is returned for inputs. The findings come from IBM SPSS. Results indicate a significant independent sample t test (p= 0.000, p<0.05). The algorithms vary statistically. Ant Colony takes longer to schedule tasks than Improved whale Optimisation Algorithm. According to study, the Improved whale Optimisation Algorithm (53.6000 MS) has a shorter Task Scheduling Time than the Ant Colony Algorithm (93.1000 MS).

关键词： Cloud computing Schedules Quantum computing Processor scheduling whales Scheduling whale optimization algorithms

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A Hybrid Multiagent Adaptive Clustering Algorithm Using whale optimization in VANETs Network

A Hybrid Multiagent Adaptive Clustering Algorithm Using Whal...

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Communication and Networks (ASIANComNet), Asian Conference on

作者： Zahraa Saad Abdulali Hussein Al-Aboudy S. Sri Nandhini Kowsalya Fatima H. Alsalamy Maysam Reyad Hadi Mohammed Ihsan National University of Science and Technology Dhi Qar Iraq Department of Computer Techniques Engineering Mazaya University College DhiQar Iraq Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Medical Laboratories Techniques Department Al-Mustaqbal University Hillah Iraq Department of Computer Science Altoosi University College Najaf Iraq Department of Computer Technical Engineering College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350367003

ISBN: (纸本)9798350367225

Mainly to improve the efficiency to reduce the power utilization in earlier researches the clustering model is concentrated. It is the formation of clusters in the network which increases the connection degree fundamentally and with the leaders the network stability is also increased. With the presence of a huge number of devices even the earlier clustering models consists of certain drawbacks like data loss and delay. Overcome such drawbacks in this article a hybrid multiagent adaptive algorithm with whale optimization (HMACWO) is developed so that an optimal clustering is introduced which is able to increase the efficiency of the vehicular network. The core modules which are present in this article are an efficient clustering process and whale optimization with an improved clustering model. Experimental demonstration of this model is done in NS3 software and using certain parameters such as the cluster efficiency, CH lifetime, packet delivery ratio, network throughput, and average delay the performance of the network is analyzed. From the results, it is shown that the HMACWO attains a maximum cluster efficiency and $\mathbf{C H}$ lifetime when compared with the earlier methods.

关键词： Adaptation models Adaptive systems Clustering algorithms Throughput whale optimization algorithms Stability analysis Software Hybrid power systems Delays optimization

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An Effectual Strategy to Augment the Hello Interval of Each Node Using Enhanced Humpback whales optimization

An Effectual Strategy to Augment the Hello Interval of Each ...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Richa Purohit T. Saravanan Rashmi Dharwadkar Y Vahidhabanu S.P. Sasirekha N. Krishnaveni School of Computer Studies Sri Balaji University Pune India Dept. of CSE GITAM School of Technology GITAM (Deemed to be University) Bengaluru India Dept. of Computer Science Sri Balaji University Pune India Department of Computer Science and Engineering Kongunadu college of Engineering and Technology Trichy India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Wireless networks are highly utilized, especially Mobile Ad-hoc Networks (MANETs) for real-time data transmission. However, current routing protocols for MANETs face challenges that limit their effectiveness. Design issues include minimizing energy usage and ensuring node security. A commonly used routing protocol, AODV, involves periodic broadcasting of hello messages to monitor link connectivity and maintain routing tables. When link failure occurs due to node mobility, hello messages are broadcasted more frequently, which consumes more node energy and increases network overhead. To address this issue, this research proposes an Enhanced Humpback whales Optimized Routing Approach (EHWORA) that calculates an optimal hello interval based on a node's energy and mobility using an adaptive fuzzy inference system. This approach involves optimizing the hello interval of AODV routing through the use of an adaptive fuzzy inference system. Each node can dynamically adjust the transmission of hello messages based on the fuzzy-based hello interval. Simulation results demonstrate that ORA reduces network overhead, end-to-end delay, hello message fraction, average energy consumption, packet delivery ratio, and improves throughput compared to a recent method. The proposed protocol is evaluated using MATLAB software. To assess the validity of the research, we utilized MATLAB software to compare the proposed method with a recent approach. During the simulation, the proposed protocol depleted the energy of 3 nodes after 10 seconds, 5 nodes after 20 seconds, 9 nodes after 30 seconds, 11 nodes after 40 seconds, and 13 nodes after 50 seconds, respectively.

关键词： Fuzzy logic Adaptive systems Routing whale optimization algorithms Ad hoc networks Software Routing protocols MATLAB Artificial intelligence Mobile computing

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Feasibility and optimization of Solar Systems for Academic Institutions: A Multi-Algorithm Approach

Feasibility and Optimization of Solar Systems for Academic I...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md. Ashaduzzaman Niloy M M Naushad Ali Md Shahadat Hossen Bibekananda Nath Md Ashraful Islam Shahela Akter Ahmed Al Mansur Department of Electrical and Electronic Engineering Green University of Bangladesh Bangladesh Department of ECE Gannon University Pennsylvania

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Renewable energy systems (RES) present a sustainable and resilient alternative to traditional single-source energy systems, offering improved safety and cost-effectiveness in power generation. This study investigates the efficiency and economic viability of a photovoltaic (PV)-based energy system with integrated energy storage, tailored for a university campus setting. To optimize the utilization of available space, advanced optimization algorithms- Grey Wolf Optimizer (GWO), whale optimization Algorithm (WOA), and Artificial Hummingbird Al-gorithm (AHA) were employed to identify the optimal system size and associated costs. The findings highlight the strong potential of RES in urban environments. Specifically, the case study on the Green University of Bangladesh campus demonstrates the feasibility of the proposed system, achieving a cost of energy (COE) of $0.018SlkWh and a payback period of 8 years and 10 months. This research illustrates the capacity of PV-based systems to provide reliable and sustainable energy to urban campuses, delivering significant economic and environmental advantages that advocate for their wider adoption in similar settings.

关键词： Photovoltaic systems Renewable energy sources Costs Urban areas Green products whale optimization algorithms Solar system Safety Reliability optimization

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Optimal Trajectory Planning for Robotic Arm Based on Improved whale optimization Algorithm

Optimal Trajectory Planning for Robotic Arm Based on Improve...

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Service Robotics (ICoSR), International Conference on

作者： Zhiqiang Ni Guirong Wang Hengchu Ye College of Mechanical and Electrical Engineering China Jiliang University Hangzhou China

ISBN: (数字)9798350367119

ISBN: (纸本)9798350367126

An integrated optimal trajectory planning approach is suggested to increase the shock resilience and operational efficiency of industrial robots. First, the refractive reverse learning mechanism, Lévy flights, and nonlinear convergence factor are incorporated to the basic whale optimization algorithm to create the improved whale optimization algorithm (IWOA). Then the superiority and efficacy of the improved algorithm are confirmed using the CEC2017 test function set. Subsequently, trajectory planning in the robot joint space utilizing 3-5-3 polynomial functions determines the whole optimization objective function in terms of time, impact, and energy consumption. When the joint motion curves derived from the improved algorithm are continuous and devoid of sudden shifts, the efficacy of the trajectory planning approach has finally been verified.

关键词： Energy consumption Trajectory planning Service robots Electric shock Manipulators whale optimization algorithms Polynomials Robots optimization Resilience

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Optimized PV Panel Control in Hybrid DC Microgrid Systems Using whale optimization & Reinforcement Learning

Optimized PV Panel Control in Hybrid DC Microgrid Systems Us...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Kathir D Umesh Prabhu H Maathavan R Department of EEE St. Joseph’s College of Engineering OMR Chennai

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

As the demand for reliable and sustainable energy grows, DC microgrids powered by renewable sources are becoming an efficient alternative for modern power systems. This research introduces an optimized control strategy for a DC microgrid that combines solar, wind, and batteries, ensuring efficient power management and uninterrupted energy supply to connected loads. The system leverages the whale optimization Algorithm combined with Reinforcement Learning to enhance Maximum Power Point Tracking for solar panels, enabling real-time adaptation to changing environmental conditions. A dedicated power converter controls the DC bus voltage, and the battery circuit maximizes charging and discharging to maintain system stability. Additionally, a power flow management algorithm is developed to ensure optimal energy distribution among the three sources. The proposed approach is implemented and tested in SIMULINK under varying solar and wind conditions. Simulation results demonstrate improved energy harvesting, faster convergence, enhanced voltage regulation, and higher system efficiency. This research contributes to the advancement of smart and self-sustaining DC microgrids, providing an intelligent solution for renewable energy management.

关键词： Maximum power point trackers Renewable energy sources Lighting Microgrids Reinforcement learning Power system stability whale optimization algorithms Stability analysis Batteries Voltage control

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Decision Tree with whale optimization Algorithm (DT-WOA) for QoS-Based IoT Service Composition

Decision Tree with Whale Optimization Algorithm (DT-WOA) for...

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Electrical, Computer, Telecommunication and Energy Technologies (ECTE-Tech), International Conference on

作者： Souhila Ait Hacene Ouhadda Samia Chibani Sadouki Abdelkamel Tari LIMED laboratory Faculty of Exact Sciences University of Bejaia Bejaia Algeria LIMED laboratory Faculty of Exact Sciences Laboratoire LITAN Higher School of Sciences and Technologies of Computing and Digital (ESTIN) Bejaia Algeria

ISBN: (数字)9798350388480

ISBN: (纸本)9798350388497

From smart homes to smart cities, the Internet of Things (IoT) has revolutionized our lives by connecting billions of devices into an interconnected network. Each smart device offers specific functions, such as remote door unlocking or sending alerts, which we call IoT services. To meet the complex needs of users, combining multiple atomic IoT services is required, as a single service is often insufficient. The increasing number of IoT services offering similar functionality makes service composition based on Quality of Service (QoS) attributes more challenging. To address this issue, we propose a DT-WOA approach combining a Decision Tree (DT) with the whale optimization Algorithm (WOA). We use DT-WOA to find a close-to-optimal solution that meets the QoS user requirements. Simulation results indicate that DT-WOA outperforms the original WOA in terms of fitness value by effectively reducing the search space.

关键词： Training Machine learning algorithms Simulation Quality of service Smart homes whale optimization algorithms Classification algorithms Internet of Things Decision trees Smart devices

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Diagnosing Diabetes Using Binary whale optimization Algorithm-Based Feature Selection

Diagnosing Diabetes Using Binary Whale Optimization Algorith...

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Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Marium Khairalla Ali Ahmed Lawgali Computer Science (Facutly of Information Technology) University of Benghazi Benghazi Libya

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

Early diagnosis of diabetes is critical for prompt treatment that can prevent the disease's progression to potentially lethal complications. Due to factors such as high blood pressure, genetic diabetes, and modern lifestyles that include inactivity, diabetes is one of the most common and severe diseases in the world. Using machine learning to assist in the creation of quick and precise diagnoses is one endeavor being made in the health sector. Feature selection approaches are a crucial step towards achieving high classification accuracy since they improve the efficiency of learning algorithms. In order to detect diabetes disease, the features from the Pima Indians Diabetes (PID) dataset were selected using a binary whale optimization algorithm (BWOA). After that, the classification is applied by the Gaussian process classifier (GPC) and linear discriminant analysis (LDA) independently. The proposed BWOA-GPC system obtained the highest classification accuracy of up to 85.94%.

关键词： Training Accuracy Machine learning algorithms Standardization Feature extraction whale optimization algorithms Diabetes

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Adaptive Random Evolution whale optimization Algorithm (AREWOA) Based Workflow Scheduling in a Virtualized Cloud Environment

Adaptive Random Evolution Whale Optimization Algorithm (AREW...

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International Conference on Computing and Networking Technology (ICCNT)

作者： R. Nathiya S.K. Piramu Preethika Department of Information Technology School of Computing Sciences VEL’S Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cloud workflow task scheduling refers to assigning workflow tasks submitted by users to appropriate computer resources for execution. The workflow scheduler assigns the user request to the available Virtual Machine (VM) hosted on cloud servers during scheduling. However, while limiting energy consumption, the workflow application processing schedule length (makespan) and associated costs must stay within the users budget and deadline. The tasks are chosen using the optimal scheduling technique to reduce Energy Consumption (EC) and maximize Resource Utilization (RU) without compromising system performance. In this paper, Adaptive Random Evolution whale optimization Algorithm (AREWOA) based scheduling is proposed to reduce EC, maximize RU, and workflow execution time. AREWOA is performed based on the dual-operation strategy partnership. Initially, the Gaussian distribution is used in the randomization procedure to raise the variety of the population. Secondly, a new skip step factor is introduced to enhance the optimizer capability to solve local optimum, and specific reinforcement is applied to the hunter process to enhance the whale’s exclusive exploration or exploitation skills. An adaptive weight factor is applied to maintain algorithm stability and a balance among exploration and exploitation. Dynamic provisioning of infinite heterogeneous resources, AREWOA is thought to demonstrate cloud-based Infrastructure-as-a-Service (IaaS). This approach is simulated in Cloud Simulator and the results shows that the efficiency of the methods.

关键词： Cloud computing Energy consumption Schedules Processor scheduling Heuristic algorithms System performance Optimal scheduling whale optimization algorithms Stability analysis Servers

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