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检索条件"主题词=whale optimization algorithms"

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Nonlinear Aircraft Trim Using A Newton-Raphson Based whale optimization Algorithm

Nonlinear Aircraft Trim Using A Newton-Raphson Based Whale O...

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Human-Computer Interaction, optimization and Robotic Applications (HORA), International Congress on

作者： Seyda Aydin Hakan Yazici Dept. of Mechanical Engineering Yildiz Technical University Istanbul Turkiye

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Determining the trim point of an aircraft is essential for designing control law and plays a key role in analyzing flight mechanics and overall performance. This point is obtained by solving a set of nonlinear algebraic equations that define the aircraft’s steady-state conditions. Due to various nonlinearities, the trim problem, formulated as a constrained optimization problem, may not be solved or yield multiple solutions. The Newton-Raphson (NR) method, a widely used approach for solving such nonlinear equations, is highly sensitive to initial conditions and may not converge if the starting point is distant from the actual solution. To address this limitation, a hybrid method combining the NR technique with the whale optimization Algorithm (WOA), a modern swarm-based meta-heuristic, is proposed and tailored for aircraft trim analysis in this study. A nonlinear aircraft model of an F-16 was developed in the MATLAB/Simulink environment. The model was first trimmed using the NR method and, then, with the proposed hybrid method under different trim conditions. The results show that the hybrid algorithm successfully finds the trim point in cases where the NR method fails to converge to an accurate solution.

关键词： Accuracy Atmospheric modeling Robot kinematics Metaheuristics Mathematical models whale optimization algorithms Steady-state Aircraft Newton method MATLAB

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Enhancing Privacy in Health IoT Using Fog Computing with Proxy Re-Encryption and Modified whale optimization Algorithm

Enhancing Privacy in Health IoT Using Fog Computing with Pro...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Rahul Jaywantrao Shimpi Vibha Tiwari Department of Electronics Engineering Medicaps University Indore

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Internet of Things (IoT)-based cloud system has various obstacles, including confidentiality, vulnerabilities in centralized operations, difficulties in managing IoT devices, and vulnerability to malicious assaults. Secure and reliable transmission of Electronic Health Records (EHRs) between healthcare and cloud IoT is critical. IoT-enabled healthcare equipment, which has been increasingly adopted by hospitals, presents significant security risks. However, medical organizations fail to address this risk leading to possible threats to encryption and authentication of healthcare IoT devices. This paper introduces a novel framework, the Proxy Re-Encryption-Modified whale optimization Algorithm (PRE-MWOA), designed to improve security of Health IoT networks through Fog Computing. By combining Proxy Re-Encryption (PR) with MWOA, the framework ensures that sensitive data remains protected while allowing for intelligent task distribution and robust anomaly detection. The decryption of results occurs only at the final stage, ensuring that data remains secure throughout the computational process. Experimental results indicate that the PRE-MSSA framework significantly enhance both data security and energy consumption compared to traditional state-of-art solutions.

关键词： Cloud computing Privacy Hospitals Data security Medical services Organizations whale optimization algorithms Internet of Things Reliability Edge computing

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DSSC Device Simulation Modeling and optimization Configuration Method Based on WOA

DSSC Device Simulation Modeling and Optimization Configurati...

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Electrical Automation and Artificial Intelligence (ICEAAI), International Conference on

作者： Jie Zhang Sizhuo Lv Chao Zheng State Grid Chongqing Electric Power Co. Ltd. Chongqing China China Electric Power Research Institute Beijing China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

As the proportion of renewable energy integration increases, power flow control issues in power grids become increasingly complex. This paper proposes a simulation modeling and optimization configuration method for power flow based on distributed static series compensator (DSSC). By constructing a DSSC electromechanical transient model, analyzing its working principle and regulation characteristics, and combining a power flow optimization model, this method achieves regional grid load balancing and improved power transmission efficiency. The whale optimization algorithm (WOA) is adopted to solve the optimization configuration problem. The effectiveness of the proposed method is verified through simulations based on the IEEE 39-bus system, providing new ideas for distributed grid regulation.

关键词： Simulation Power transmission Regulation whale optimization algorithms Stability analysis Transient analysis optimization Load flow control Load modeling Load flow

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Intelligent Charge Controllers for EVs Using whale optimization Algorithm for Demand Management

Intelligent Charge Controllers for EVs Using Whale Optimizat...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： A. Sujin Jose S Sakthiya Ram C.R. Bharathi Devarajan M A.P. Jaya Krishna A. Madhan Department of Automobile Engineering New Horizon College of Engineering Bangalore India Department of Electronics and Instrumentation Engineering Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Mathematics Sethu Institute of Technology Virdhunagr Tamil Nadu India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamilnadu India Department of Electrical and Electronics Engineering Velammal College of Engineering and Technology Madurai Tamilnadu India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

This paper proposes the development of a smart charge controller for electric vehicles that may take advantage of the whale optimization Algorithm (WOA) to improve on energy efficiency and demand management. The technology optimized charging parameters through dynamic adjustment of the charging process in relation to the energy demand from the grid, SOC, and power pricing. This ability of WOA to balance between exploration and exploitation allows the controller to minimize thermal losses in systems and prevent battery degradation. Moreover, energy wastage is avoided so that the controller can ensure efficient energy utilization. The experiment results show that compared to the current charge controllers, a WOA-based controller would improve grid load management by 10%, reduce battery degradation by 15%, and increase efficiency in the utilization of energy by 20%. This system has the scalability as well as the real-time adaptability to changing requirements of batteries and grid conditions that would make it suitable for areas with high penetration of electric vehicles. This system outperforms the others by optimizing the load distribution necessary for optimal charging.

关键词： Degradation Heuristic algorithms Load management whale optimization algorithms Real-time systems Energy efficiency Batteries State of charge Vehicle dynamics optimization

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An Improved Adaptive whale optimization Algorithm with Multi-Strategy for optimization Problems

An Improved Adaptive Whale Optimization Algorithm with Multi...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Jiarui Zhang Qunfeng Liu Yunchao Zhi Kanghua Zhu Department of Computer Science and Technology Dongguan University of Technology

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

The whale optimization Algorithm (WOA) is indeed recognized for its effectiveness in solving complex optimization problems, including NP-hard problems. However, like many metaheuristic algorithms, it has its limitations. Insufficient population diversity and ineffective search strategies are common challenges that can reduce its performance. We urgently need to introduce effective strategies to alleviate these core shortcomings of WOA. To address the slow convergence, low accuracy, and local optima issues of WOA, this paper proposes an Improved Adaptive whale optimization Algorithm with multi-strategy(IWOA). Firstly, enhanced Sine chaotic map was introduced to improve the quality of the initial population. Secondly, the adaptive inertia weights are used to balance the global and local searches, so as to accelerate the convergence of the algorithm. Lastly, the Metropolis criterion is incorporated to strengthen the algorithm's ability to escape local optima. Datasets are used to verify the performance of the improved adaptive whale optimization algorithm in solving problems.

关键词： Flowcharts Accuracy NP-hard problem Heuristic algorithms Metaheuristics Simulated annealing Search problems whale optimization algorithms Consumer electronics Convergence

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Multi-Cloud Security Policy optimization

Multi-Cloud Security Policy Optimization

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Louay Karadsheh Faten Hamad Hussam Fakhouri Faculty of Information Technology University of Petra Amman Jordan Sultan Qaboos University Sultanate of Oman The University of Jordan Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Cloud computing has revolutionized the deployment and management of large-scale applications. However, with the advantages of rapid scalability and flexibility come significant security challenges. Optimizing security policies in a multi-cloud environment is a multi-dimensional, complex problem, often involving trade-offs among encryption overhead, open port configurations, privilege levels, and subscription costs for advanced threat detection services. In this paper, we present a detailed comparative study of these metaheuristics for security policy optimization in the cloud. We implement an enhanced objective function that incorporates baseline risk, encryption overhead, open port penalties, and advanced analytics subscription costs. We benchmark the algorithms over 30 independent runs, each with a maximum of 200 iterations, and measure their performance according to best, worst, mean, median, standard deviation (Std), interquartile range (IQR), runtime, and an average rank metric. Our results reveal distinct advantages and trade-offs among the metaheuristics, demonstrating that no single algorithm dominates every scenario. Grey Wolf Optimizer (GWO) and Marine Predators Algorithm (MPA) emerge as strong contenders, achieving low objective values and relatively consistent performance. We discuss these findings in depth, and we provide recommendations on the suitability of each method for large-scale cloud security challenges.

关键词： Costs Runtime Cloud computing security Scalability Metaheuristics whale optimization algorithms Threat assessment Encryption Security Standards

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Hybrid WOA-GA Algorithm for Coverage optimization and Energy Efficiency in Wireless Sensor Networks

Hybrid WOA-GA Algorithm for Coverage Optimization and Energy...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Momar Ousmane BA Cheikhou Thiam Mamadou Mbaye Department of Science and Technology Université Iba Der Thiam Thiès Senegal Department of Economic and Social Sciences Université Iba Der Thia Thiès Senegal

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

This article presents a comparative study of the hybrid WOA-GA algorithm (whale optimization Algorithm - Genetic Algorithm) applied to the optimization of wireless sensor networks, in comparison with three other algorithms: GA (Genetic Algorithm), PSO (Particle Swarm optimization), and ACO (Ant Colony optimization). The performance of the algorithms is evaluated based on three key criteria: network lifetime, residual energy, and the amount of data transmitted to the base station. The results show that WOA-GA outperforms the other algorithms in each category, significantly extending node lifetime through better energy management while maintaining more efficient data transmission. WOA-GA demonstrates particularly marked superiority in the network's later phases by retaining higher residual energy and delaying node death. These results highlight WOA-GA's capabilities in optimizing sensor network resources in energy-constrained environments. Finally, the article proposes avenues for future research, including the evaluation of WOA-GA in more complex and dynamic networks and the integration of new optimization approaches, such as artificial intelligence, to enhance the robustness and efficiency of sensor networks in real-world contexts.

关键词： Wireless sensor networks Machine learning algorithms Heuristic algorithms Conferences Prediction algorithms whale optimization algorithms Robustness Particle swarm optimization optimization Genetic algorithms

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Financial Distress Prediction by using Long Short-Term Memory based Adaptive whale optimization Algorithm

Financial Distress Prediction by using Long Short-Term Memor...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Qiuyu Yang Yantai Vocational College Yantai China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In recent days, financial distress had been attributed to both internal and external factors, the internal causes included poor financial management while external influences economic downturns, heightened competition, and regulatory shifts. Traditional approaches for predicting financial distress had faced several challenges which include high sensitivity to outliers and inability to process sequential data. Therefore, this research proposes Long Short-Term Memory based Adaptive whale optimization Algorithm (LSTM-AWOA) for predicting financial distress, as it is effective at handling sequential data. Initially, data is collected from polish company dataset which consists of 10,503 samples with 64 financial attributes. Then, this dataset is preprocessed for handling missing values, and removing redundant rows which improves data quality, leading to better model performance and also resolves overfitting issues. After that, prediction is done by using LSTM and to improve its performance AWOA optimization is done. The proposed LSTM-AWOA achieved better results in terms of accuracy (0.9765), precision (1.0000), recall (0.9678) and F1-score (0.9769) when compared to existing AWOA-Deep Learning (AWOADL).

关键词： Accuracy Data integrity Companies Prediction algorithms whale optimization algorithms Data models Long short term memory optimization Streams Overfitting

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Adaptive Energy and Latency optimization in Multi-Tier Networks via DDPG-Based Multi-Objective Control

Adaptive Energy and Latency Optimization in Multi-Tier Netwo...

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International Conference on Information Networking

作者： Vitou That Vanda Yorn Jung-Ryun Lee Department of Intelligent Energy Industry Convergence Chung-Ang University Seoul South Korea Department of Intelligent Energy and Industry School of Electrical and Electronics Engineering Chung-Ang University Seoul South Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

With the increasing computational demands of Internet of Things (IoT) applications, air-ground integrated networks (AGIN), leveraging the capabilities of Unmanned Aerial Vehicles (UAVs) and High-Altitude Platform (HAP), provides an essential solution to these challenges. In this paper, we propose a framework that facilitates local computing at IoT devices and offers the flexibility to offload tasks to aerial platforms when necessary. Specifically, we formulate a multi-objective optimization model aiming at simultaneously minimizing energy consumption and reducing task latency by adjusting control variables such as transmit power, offloading decisions, and UAV placement in a distributed network of IoT devices. Our proposed framework employs Deep Deterministic Policy Gradient (DDPG) techniques to dynamically optimize network operations, allowing for efficient real-time adjustments to network conditions and task demands. The performance of the proposed algorithm is compared to traditional algorithms, including the whale optimization Algorithm (WOA), Gradient Search with Barrier, and Bayesian optimization (BO). Simulation results show that this approach significantly minimizes energy consumption and latency, outperforming conventional optimization methods. Additionally, scalability tests confirm that our framework can efficiently integrate an increasing number of IoT devices and UAVs.

关键词： Performance evaluation Energy consumption Heuristic algorithms Simulation Autonomous aerial vehicles Deep reinforcement learning whale optimization algorithms Air to ground communication Internet of Things Vehicle dynamics

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Structural optimization Design of the Rotary Blowout Preventer Shell Based on an Adaptive Sand Cat and whale optimization Algorithm

Structural Optimization Design of the Rotary Blowout Prevent...

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IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

作者： Tian Haijiang Houpin Yoong Bih Lii Chua Ismail Saad Institute of Intelligent Manufacturing Fujian Polytechnic of Information Technology Fuzhou China Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

A critical component of the Rotary Blowout Preventer (RBOP) is the rotating assembly, essential for its “drill-in circulation” functionality. The bottom of the outer cylinder of the rotating assembly endures significant hydrostatic pressure, leading to substantial deformation. This study establishes a finite element analysis model of the outer cylinder of the rotating assembly in ANSYS Workbench and validates the strength of the assembly shell by analyzing the stress distribution. The radii of curvature at the interface between the rotating assembly and the clamp, along with five shell thicknesses at different locations along the outer cylinder, are designated as independent variables. To minimize the mass of the shell, a mathematical model is formulated using Von Mises stress as the state variable. optimization computations are conducted using the Adaptive Sand Cat and whale optimization Algorithm (SCWOA). By adjusting the radii of curvature, the concentration of maximum stress is reduced, optimizing the stress distribution, thinning the wall thickness, and reducing the outer cylinder's weight. Compared to optimization results using the hybrid penalty function in ANSYS Workbench, SCWOA demonstrates faster convergence, achieving near-optimal solutions in fewer iterations, making it suitable for addressing challenging engineering optimization problems. Furthermore, the optimized results provide a theoretical foundation for subsequent repair and remanufacturing studies.

关键词： Deformation Maintenance engineering whale optimization algorithms Mathematical models Finite element analysis Clamps optimization Assembly Stress Convergence

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