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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Al-Attabi, Kassem Aluvala, Srinivas Manjula, B.M. Mohammed, Ibraheem Hatem Bodduppalli, Santhosh The Islamic University Najaf Iraq SR University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Al Rafidain University College Department of Computer Communications Engineering Bagdad Iraq Malla Reddy Engineering College for Women Department of Cse Hyderabad India

ISBN: (纸本)9798350315455

The Vehicular Ad hoc Network (VANET) is a mobile network which enables a numerous intelligent transportation system. The secure routing is significant to prevent mobile devices from other threats so, the effective characteristics in VANET are utilized to attain an efficient secure routing path. The high vehicle distribution and node mobility compromises the network scalability and topology. So, creating the reliable and scalable vehicle communication, network physical layout formation, unstable link to enable robust are difficult task in traffic network. In this research, novel optimization algorithm is considered with transmission range, speed direction, grid size and node density during the clustering. The Improved whale optimization algorithm for clustering in VANET (IWOA) is proposed for selecting an optimal Cluster Head (CH). Primarily, the optimal CH are selected by using IWOA and then the route path is selected. The simulations are performed and then the experiments are conducted on the IWOA. The performance of IWOA is evaluated using throughput, Packet Delivery Ratio (PDR) and latency by various rounds of 30, 40, 50, 60 and 70. The IWOA attained high throughput of 6. 87mbps and PDR of 0.93 with less latency of 0.10s which is superior than other existing algorithms. © 2023 IEEE.

关键词： Cluster Head Secure Routing Path Transmission Range Vehicular Ad hoc Network whale optimization algorithm

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Energy-Efficient Task Scheduling based on whale optimization algorithm Cyber-Physical Systems 2

Energy-Efficient Task Scheduling based on Whale Optimization...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Al-Jawahry, Hassan M. Shankar, Smitha Sharath Chythanya, Kanegonda Ravi Maharajan, K. Swathi, Akuthota The Islamic University Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Sr University School of Computer Science and Artificial Intelligence Warangal India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Krishnankoil India Cvr College of Engineering Department of CSE Telangana India

ISBN: (纸本)9798350317558

The extensive application of Cyber-Physical Systems (CPS) requires efficient optimization of computational units and physical plants. Task scheduling (TS) is critical to improving resource utilization and system efficacy within CPS. Standard task schedulers in embedded real-time techniques fall short of achieving CPS performance criteria due to task diversity and system heterogeneity. This research proposes a novel task scheduling technique called whale optimization algorithm based Energy-Efficient TS (WOA-EETS) that is specifically designed for the CPS context. WOA-EETSS involves assigning number of autonomous tasks to various resources during the TS process. The objective function focuses on reducing task completion time and optimizing resource use. A series of simulations were run to demonstrate WOA-EETSS' improved performance. The comparison analysis demonstrated that the WOA-EETSS system performed better across a variety of parameters. The proposed approach attains the better performance in Task Execution (ms) of 347ms, Convergence Rate of 511ms and Average Load Balancing Ratio (%) of 69.91% from 100 iterations. © 2024 IEEE.

关键词： Cyber-Physical Systems Energy Efficient Task Diversity Task Scheduling whale optimization algorithm

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Buoy Autonomous Depth Determination Control algorithm Based on Improved whale optimization algorithm 5

Buoy Autonomous Depth Determination Control Algorithm Based ...

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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Yuan, Chenyang Li, Zhihong Yi, Jiafu Lv, Yaxuan Deng, Lihao Wang, Xi Gao, Yue Xu, Long Wang, Jingjing School of Information and Communication Engineering Hainan University Hainan Haikou China

ISBN: (纸本)9798350357950

In order to realize the reliable control of autonomous depth setting in the course of buoy movement, an autonomous depth setting control algorithm based on Improved whale optimization algorithm (IWOA) is proposed in this paper, utilizing the improved whale optimization algorithm to realize the self-tuning of PID control parameters. To improve the convergence speed and accuracy of control parameter self-tuning, in the initialization stage of the whale optimization algorithm (WOA), a Logistic mapping is introduced to generate the initial population, enhancing diversity among the initial solutions. During the iterative optimization process, an adaptive inertia weight and a non-linear convergence factor are employed to balance the global and local search phases. Additionally, a Lévy flight strategy is incorporated to prevent the algorithm from getting stuck in local optima, thereby improving its accuracy. PID parameters of buoy depth determination system of IWOA and WOA were optimized in MATLAB/Simulink respectively. The results show that IWOA optimized PID control has smaller overshoot and stability time, faster iteration speed and better optimization results than WOA optimized PID control. © 2023 IEEE.

关键词： improved whale optimization algorithm self-tuning of PID control parameters whale optimization algorithm

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Hybrid Grasshopper whale optimization algorithm Based on Faster Recurrent Convolutional Neural Network for Intrusion Detection System in Internet of Things

Hybrid Grasshopper Whale Optimization Algorithm Based on Fas...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, Suvarna Sunil Goutham, V. Prathima, Y. Unnisa, Raheem Aruna, T.M. Malla Reddy Engineering College for Women It Department Hyderabad India Nalla Narasimha Reddy Group of Institutions Cse Department Hyderabad India Cmr Engineering College Department of Computer Science and Engineering Hyderabad India Cmr Technical Campus Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Yelahanka Bengaluru India

ISBN: (纸本)9798350313413

Intrusion Detection System (IDS) is one of the general Deep Learning (DL) techniques which are utilized to find and identify the outliers to prevent adversarial attacks, fraud, intrusions in Internet of Things (IoT). The proposed hybrid optimization technique known as GWOA by integrating the Grasshopper optimization algorithm (GOA) and the whale optimization algorithm (WOA). For better IDS performance, this GWOA can be utilized into the Faster Recurrent Convolutional Neural Network (FRCNN) model. This proposed method utilized the BoT-IoT dataset to assess the efficacy of this GWOA-based strategy. The main objective was to improve the IDS's balance between exploration and exploitation. This system makes use of the FRCNN to categorize incursions precisely and effectively, improving the overall detection capabilities. When compared to measures of evaluation like precision, recall, accuracy, and f1-score, the suggested GWOA-based FRCNN model's results show a considerable improvement. Comparing the model to other intrusion detection methods, such as Deep Neural Network (DNN), Long Short-Term Memory (LSTM) and IDS-SIoEL. It attains values of 93.77% for accuracy, 86.66% for precision, 95.87% for recall, and 91.03% for f1-score. © 2023 IEEE.

关键词： Deep Learning Faster Recurrent-Convolutional Neural Network Grasshopper optimization algorithm Internet of Things Intrusion Detection System whale optimization algorithm

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Research on Integrated Energy System Planning Based on the whale optimization algorithm 6

Research on Integrated Energy System Planning Based on the W...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Qian, Xu Xu, Xiaochen Zhu, Jingdong Dong, Pengxu Li, Yibo Wang, Yongli Electromechanical Engineering Institute PowerChina Huadong Engineering Corporation Limited Zhejiang310014 China North China Electric Power University School of Economics and Management Beijing102206 China

ISBN: (纸本)9798350377798

In this paper, a comprehensive energy system planning optimization method based on heuristic algorithm is proposed, which aims to improve the economic efficiency of the system. Firstly, the background and development status of the integrated energy system are analyzed, and the challenges and optimization opportunities of equipment configuration and modeling are discussed. Subsequently, an optimization method based on economic performance was introduced, and the whale optimization algorithm was used for system optimization. Through case analysis, the results show that the proposed method can significantly reduce the operating cost of the system. Under the initial population size of 120 iterations, the whale optimization algorithm converged around the 44th generation, and the final solution obtained was $6,218,367.29. The results obtained by the genetic algorithm are 1.90% higher than those obtained using the whale optimization algorithm. Therefore, the algorithm used in this paper shows strong global search ability and improves the optimization accuracy. © 2024 IEEE.

关键词： integrated energy system planning optimization whale optimization algorithm

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Energy Management Strategy for Hybrid Electric Vehicles Based on whale optimization algorithm 3

Energy Management Strategy for Hybrid Electric Vehicles Base...

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3rd International Conference on Energy, Power and Electrical Engineering, EPEE 2023

作者： Tian, Shaopeng Cai, Wen School of Automotive Engineering Wuhan University of Technology Hubei Wuhan China

ISBN: (纸本)9798350318180

To further explore the emission-reduction and energy-saving capabilities of hybrid electric vehicles (HEV), an energy management strategy (EMS) based on the whale optimization algorithm (WOA) in charge-sustaining mode was proposed for the HEV. The strategy takes the minimum equivalent fuel consumption and maintaining SOC balance as the optimization objectives, and uses the WOA algorithm to optimize the parameters of the equivalent consumption minimization strategy (ECMS). The vehicle model was established by Matlab, and simulation analysis was carried out in the global light test cycle (WLTC) and the new European driving cycle (NEDC). The results show that the WOA-based ECMS (WOA-ECMS) reduces the comprehensive fuel consumption by 8.9% under the NEDC cycle and by 6.5% under the WLTC cycle compared with ECMS while maintaining SOC stability. © 2023 IEEE.

关键词： energy management equivalent fuel consumption minimum control strategy hybrid electric vehicle whale optimization algorithm

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Improved whale optimization algorithm applied to design PID plus second-order derivative controller for automatic voltage regulator system

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2020年第6期43卷 541-552页

作者： Mokeddem, Diab Mirjalili, Seyedali Univ Ferhat Abbas Setif 1 Dept Elect Engn Fac Technol Setif Algeria Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld Australia

The paper proposes an Improved whale optimization algorithm (IWOA). Its performance is validated by solving 23 benchmark functions. Comparing the results of IWOA with well-known meta-heuristic algorithms shows its efficiency. Three non-parametric statistical tests, namely, Friedman, Friedman aligned and Quade tests are used to confirm the proposed algorithm's superiority. IWOA is employed to design the parameters of a controller, namely, PID plus second-order derivative (PIDD2) for an automatic voltage regulator system (AVR). In fact, the proposed technique benefits from an evolutionary operator crossover to promote the diversity of solutions while maintaining a reasonable local search behavior. The results are compared with the results of similar algorithms including Particle Swarm optimization, Genetic algorithm, Teaching Learning-Based optimization, Differential Evolution, Cuckoo Search algorithm and Artificial Bee Colony, demonstrating the advantages and the efficiency of the IWOA-PIDD(2)controller. Robustness analysis of the optimal design obtained is conducted by varying the time constants of the AVR system component. The results proved that the proposed technique reliably outperforms most of the current techniques.

关键词： whale optimization algorithm metaheuristic technique arithmetic crossover PIDD(2)controller automatic voltage regulator

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A whale optimization algorithm with chaos mechanism based on quasi-opposition for global optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 158卷 113612-113612页

作者： Chen, Hui Li, Weide Yang, Xuan Lanzhou Univ Ctr Data Sci Sch Math & Stat Lab Appl Math & Complex Syst Lanzhou 730000 Peoples R China

whale optimization algorithm (WOA), as a newly developed meta-heuristic algorithm, performs well in solving optimization problems. A WOA with chaos mechanism based on quasi-opposition (OBCWOA) is proposed in this paper to overcome the slow convergence speed of the original WOA and to avoid being trapped in local optimal solutions when dealing with high-dimensional problems. We applied two strategies to the original WOA: using chaos mechanism to generate initial value to improve convergence speed of the algorithm and using the opposition-based learning method to balance exploration and development ability of the algorithm to help the algorithm jump out of local optimal solutions. The proposed algorithm is compared with other algorithms on unimodal functions, multimodal functions and fixed dimensional multimodal functions, and is applied to a famous engineering design problem. Results show that combination of the two strategies can improve convergence speed and enhance global search ability of the original WOA. OBCWOA proposed in this paper performs better than the other existing algorithms. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： whale optimization algorithm Chaos mechanism Quasi-opposition based learning Global optimization problems

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Order whale optimization algorithm in Rendezvous Orbit Design

Order Whale Optimization Algorithm in Rendezvous Orbit Desig...

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10th International Conference on Advanced Computational Intelligence (ICACI)

作者： Jiang Ruiye Chao Tao Wang Songyan Yang Ming Harbin Inst Technol Control & Simulat Ctr Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781538643624

A rising meta-heuristic swarm intelligent computation algorithm, whale optimization algorithm (WOA) has been proved competitive with other intelligent optimization algorithms in solving different optimization problems. This paper has proposed a new improved algorithm, named Order whale optimization algorithm (OWOA). To enhance the initial values properties, the basic concept of Latin hypercube sampling has been introduced. Then the setting method of parameters in WOA is associated with the order of fitness value. Through comparing the optimal results of 8 benchmark functions calculated by these manners, OWOA is proved effective, which is capable of accelerating the convergence rate obviously. Then the improved algorithm is used to optimize the transfer time in the Lambert problem, which is to determine the rendezvous orbit between two scheduled positions. In the end, the rendezvous orbit consumed the minimum fuels is achieved.

关键词： Benchmark functions Latin hypercube sampling rendezvous orbit whale optimization algorithm

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Optimal network reconfiguration and DG allocation using adaptive modified whale optimization algorithm considering probabilistic load flow

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ELECTRIC POWER SYSTEMS RESEARCH 2021年 192卷 106909-106909页

作者： Uniyal, Ankit Sarangi, Saumendra Motilal Nehru Natl Inst Technol Allahabad Allahabad Uttar Pradesh India Natl Inst Technol Srinagar Uttarakhand India

The optimal DG allocation (ODGA) and network reconfiguration (NR) are significant tools to improve voltage stability and minimize power losses in a distribution network. In a real power distribution network loads are random variables, thus ODGA and NR processes are incorporated using a probabilistic load flow (PLF). The reactive power capability of DGs has a significant impact on the network parameters which can be studied using PLF. In the present work, an ODGA and NR processes have been incorporated to improve the voltage stability and loss profile of the distribution system considering probabilistic loads and DGs which are operated at varying pfs. The optimization problem has been solved using an adaptive modified whale optimization algorithm (AMWOA). A novel technique based on depth first search algorithm has been employed to check radiality constraints. The effectiveness of proposed technique has been tested on two standard distribution networks.

关键词： Active power loss Distribution system Distributed generation Network reconfiguration Point estimate methods Probabilistic load flow whale optimization algorithm

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