检索结果-内蒙古大学图书馆

Optimized Distance Vector Hop Localization Based on pelican optimization algorithm in Underwater Wireless Sensor Networks

引用

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第1期38卷

作者： Nanthakumar, Sathish Jothilakshmi, P. Sri Venkateswara Coll Engn Dept Elect & Commun Engn Sriperumbudur India Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Elect & Commun Engn Chennai India

In recent days, underwater exploration has emerged as one of the most predominant technologies for enhancing surveillance and early warning systems. Finding the location of the nodes placed in underwater is a difficult task owing to its harsh underwater environment. In large underwater wireless sensor networks (UWSNs), pinpointing the exact coordinates of sensor nodes may not be feasible or be incredibly expensive. In most of the applications, the coarse coordinate of the node is adequate. The primary technique used in UWSNs to determine the location of sensor nodes, based on the average distance between hops, is referred to as distance vector-hop (DV-Hop) localization. Nevertheless, the positioning accuracy in the classic DV-Hop technique is influenced by the average hop distance. To reduce the localization error, it is possible to create a distinct and optimized DV-Hop approach. To improve the effectiveness of the localization process, the average distance between hops is primarily used as an objective function. The optimization of this objective function is achieved by employing the pelican optimization algorithm (POA). There is a noticeable decrease in the localization discrepancy if the optimized average hop distance is used to precisely determine the unidentified node to the anchor node distance among them. The factors used to evaluate the ability of the proposed methodology are the ratio of anchor, transmission range, and the density of the node. Compared to other localization procedures, the obtained outcomes demonstrate that the optimized approach that has been suggested achieves a low localization error of 0.3.

关键词： DV-Hop algorithm hop size pelican optimization algorithm range-free localization underwater wireless sensor network

来源：评论

学校读者我要写书评

暂无评论

Optimal sizing of islanded microgrid using pelican optimization algorithm for Kutubdia Island of Bangladesh

引用

ELECTRIC POWER SYSTEMS RESEARCH 2025年 238卷

作者： Sagor, Abidur Rahman Hazari, Md. Rifat Ahmad, Shameem Ogliari, Emanuele Hossain, Chowdhury Akram Jahan, Effat Mannan, Mohammad Abdul Amer Int Univ Bangladesh AIUB Dept Elect & Elect Engn Dhaka 1229 Bangladesh Amer Int Univ Bangladesh Dept Comp Engn Dhaka 1229 Bangladesh Politecn Milan Dept Energy Via Giuseppe La Masa34 I-20156 Milan Italy

This study proposes an optimal design approach, based on the pelican optimization algorithm (POA), to configure the optimal sizing of design variables on an islanded microgrid: photovoltaic (PV) modules, wind turbines (WT), diesel generators (DG), and batteries in Kutubdia, Bangladesh based on optimal life cycle cost (LCC) and cost of energy (COE). Additionally, the economic analysis of three independent battery technologies, notably lead acid, lithium-ion, and nickel-iron are carried out to find the economically feasible technology, to ensure uninterrupted power supply. Moreover, reliability and sensitivity analyses of the optimized microgrid using POA were conducted for various reliability indices and variable interest rates. Results show that proposed POA method provides the optimal island microgrid configuration with lead acid (LA) batteries (PV/WT/LA/DG) based on a minimum LCC of $8334901, COE of 0.1080$/KWh and greenhouse gas emission amount of 19664 kgs/year. Furthermore, the outcomes generated by the POA are compared with genetic algorithm, particle swarm optimization, moth flame optimization algorithm, whale optimization algorithm and grey wolf optimization. It is found that POA method achieves more competitive results compared to other optimization techniques due to its ability to adjust parameters, fast convergence speed, and straightforward computations.

关键词： Cost of energy Island microgrid Life cycle cost pelican optimization algorithm Renewable energy source

来源：评论

学校读者我要写书评

暂无评论

Multi-strategy fusion pelican optimization algorithm and logic operation ensemble of transfer functions for high-dimensional feature selection problems

引用

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年 1-38页

作者： Song, Hao-Ming Wang, Jie-Sheng Hou, Jia-Ning Wang, Yu-Cai Song, Yu-Wei Qi, Yu-Liang Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Liaoning Peoples R China

In view of the simple structure of transfer function, an integrated strategy of transfer function logic operation is proposed, which combines different transfer functions reasonably and improves the classification accuracy. It is applied to the binary pelican optimization algorithm (BPOA) to solve the high-dimensional feature selection problem. At the same time, the adaptive retractable strategy and the electric eel hunting strategy are introduced to improve the pelican optimization algorithm (POA) in order to improve the unbalanced exploration and exploitation ability of the pelican optimization algorithm itself, which is easy to fall into local optimal problems. The simulation experiment is divided into three parts. Firstly, two different logical operation integration routes are proposed, and four different transfer functions of S-shaped, V-shaped, U-shaped and RZ-shaped are respectively integrated, and the POA variant with the best effect is selected. In the following experiment, the transfer function of the same integration idea is combined in pairs to further improve the integrated transfer function of logical operations. Finally, adaptive scaling strategy and electric eel hunting strategy are introduced to improve the existing problems of POA, and compared with BAOA, BCOA, BEO, BHHO, BPDO and BZOA. Twelve standard high-dimensional UCI datasets were used to perform performance tests, and the results were statistically analyzed by Friedman test and Wilcoxon rank sum test. Simulation results show that the improved method can effectively simplify feature subsets, improve classification accuracy and obtain lower fitness values in solving these data sets.

关键词： Adaptive retractable strategy Electric eel hunting strategy Feature selection Logic operation ensemble pelican optimization algorithm Transfer function

来源：评论

学校读者我要写书评

暂无评论

An innovative coverage optimization method for smart information monitoring in agricultural IoT using the multi-strategy pelican optimization algorithm

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-20页

作者： Chen, Wei Cao, Qike Cao, Bingyu Jin, Bo Xinjiang Coll Sci & Technol Sch Informat Sci & Engn Korla 841000 Xinjiang Peoples R China Henan Inst Sci & Technol Xinxiang 453003 Henan Peoples R China Univ Coimbra Inst Syst & Robot Dept Elect & Comp Engn P-3030290 Coimbra Portugal Portuguese Natl Acad Sci P-1600477 Lisbon Portugal

With the rapid advancement of agricultural technology, Agricultural Wireless Sensor Network (AWSN) monitoring for crop growth in large-scale fields has become pivotal in smart agriculture. Optimizing AWSN coverage is crucial for enhancing production efficiency and resource utilization. However, traditional optimization algorithms struggle with local convergence and accuracy in large-scale sensor deployment, an NP-hard problem. To address this, we propose a novel Multi-Strategy pelican optimization algorithm (MSPOA), integrating a good point set strategy, a 3D spiral L & eacute;vy flight strategy, and an adaptive T-distribution variation strategy. The good point set strategy expands the search range and enhances local search capability, while the 3D spiral L & eacute;vy flight strategy improves convergence speed and global search accuracy. The adaptive T-distribution variation strategy further boosts global search ability, and pelican-inspired movement and collaboration strategies enhance adaptability and robustness in diverse agricultural scenarios. Comparative experiments with Improved Artificial Bee Colony algorithm (IABC), Chaotic Adaptive Firefly optimization algorithm (CAFA), Adaptive Particle Swarm optimization (APSO), and L & eacute;vy Flight Strategy Chaotic Snake optimization algorithm (LCSO) demonstrate that MSPOA improves network coverage by 5.85%, 11.33%, 21.05%, and 20.66%, respectively. Additionally, MSPOA exhibits strong adaptability and stability in dynamic agricultural environments.

关键词： algorithm modeling and simulation pelican optimization algorithm Wireless sensor network Levy flight strategy Point set strategy Adaptive distribution t-variation

来源：评论

学校读者我要写书评

暂无评论

Reliability based multi-objective optimized routing protocol for VANETs using pelican optimization algorithm

引用

DISCOVER APPLIED SCIENCES 2025年第4期7卷 1-23页

作者： Muthaiyan, R. Senthilkumar, S. Joe, C. Vijesh Kavitha, M. Univ Coll Engn Thirukkuvalai Dept Elect & Commun Engn Thirukkuvalai 610204 Tamil Nadu India EGS Pillay Engn Coll Dept Elect & Commun Engn Nagapattinam 611002 Tamil Nadu India Sankar Polytech Coll Dept Comp Engineerin Tirunelveli 627357 Tamil Nadu India

A developing methodology that may be used in intelligent transportation frameworks is Vehicular Ad hoc Networks (VANETs). For VANET systems to be dependable, routing protocols are necessary. The innovative paradigm Reliability Based Multi-Objective Optimized Routing Protocol for VANETs (RBMOORPV) is proposed in this research. The structure consists of three levels: the modelling of the VANET network at level 1, the routing condition at level 2, which is dependent on dependability and a meta heuristic algorithm at level 2, and the routing algorithm at level 3. The following step describes the real network through an optimization block. The overall structure also has an optimization block, which regulates the reliability, meta heuristic algorithm, and routing blocks' individual characteristics. The pelican optimization algorithm (POA) provides the basis for the optimization, which is given from a multi-objective standpoint. This covers the stages of exploration and exploitation. Two levels were used to accomplish the evaluation. The first was the RBMOORPV evaluation on the basis of the network simulator, and the second was the POA evaluation utilizing benchmarking mathematical functions. The acquired measures, such as delta metric, set coverage, Packet Delivery Ratio (PDR), hyper-volume, and End to End (E2E) delay, show that POA and RBMOORPV with POA are superior to their base techniques.

关键词： Vehicular ad hoc networks Intelligent transportation frameworks pelican optimization algorithm Packet delivery ratio End-to-end delay

来源：评论

学校读者我要写书评

暂无评论

An improved pelican optimization algorithm for solving stochastic optimal power flow problem of power systems considering uncertainty of renewable energy resources

RESULTS IN ENGINEERING

引用

RESULTS IN ENGINEERING 2025年 26卷

作者： Jamal, Raheela Khan, Noor Habib Ebeed, Mohamed Zeinoddini-Meymand, Hamed Shahnia, Farhad Qingdao Binhai Univ Coll Mech & Elect Engn Qingdao 266540 Shandong Sheng Peoples R China Sohag Univ Fac Elect Engn Elect Dept Sohag 82524 Egypt Grad Univ Adv Technol Dept Elect & Comp Engn Kerman Iran Murdoch Univ Sch Engn & Energy Perth Australia

This paper proposes solving the non-convex stochastic optimal power flow problem of a power system incorporating uncertain and intermittent renewable energy resources by an improved pelican optimization algorithm (IPOA). The POA, inspired from the foraging the behavior of pelicans, has stagnation problems and may trap into local optima. To avoid these, this paper has developed and implemented three novel improvements of mutation- based strategy, fitness distance balanced and exploitation-based gorilla troops strategies to enhance the exploitation and exploration strength of the traditional POA. The performance and effectiveness of the proposal are validated through statistical and non-parametric tests conducted via CEC 2019 test suite. In addition, IPOA is further used to solve a stochastic optimal power flow problem by integration of solar and wind energy to the modified IEEE 30-bus system to attain the lessen generation cost without and with inclusion of emissions. Statistical and non-parametric tests such as Wilcoxon ranking and Friedman tests validate the effectiveness of the proposed IPOA and the obtained least power generation costs and emissions for the considered numerical case studies.

关键词： pelican optimization algorithm Optimal power flow Renewable energy resources Uncertainty

来源：评论

学校读者我要写书评

暂无评论

pelican optimization algorithm with Federated Learning Driven Attack Detection model in Internet of Things environment

引用

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2023年第1期148卷 118-127页

作者： Al-Wesabi, Fahd N. Mengash, Hanan Abdullah Marzouk, Radwa Alruwais, Nuha Allafi, Randa Alabdan, Rana Alharbi, Meshal Gupta, Deepak King Khalid Univ Coll Sci & Art Mahayil Dept Comp Sci Abha Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Saud Univ Coll Appl Studies & Community Serv Dept Comp Sci & Engn Box 22459 Riyadh 11495 Saudi Arabia Northern Border Univ Coll Sci & Arts Dept Comp & Informat Technol Ar Ar Saudi Arabia Majmaah Univ Coll Comp & Informat Sci Dept Informat Syst Al Majmaah 11952 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci POB 151 Alkharj 11942 Saudi Arabia Maharaja Agrasen Inst Technol Dept Comp Sci & Engn Delhi India Chandigarh Univ UCRD Mohali Punjab India

The Internet of Things (IoT) is comprised of millions of physical devices interconnected with the Internet through network that performs a task independently with less human interference. Despite the benefits of IoT, it is vulnerable to malicious attacks. Therefore, machine learning (ML) inspired decision-making is hardly preferred due to drawbacks (the requirement that every training dataset is stored on the central database), computation cost related to the training of massive amounts of information on the unified server, and security issues related to transferring attained statistics from IoT sensor to the server. The Federated Learning (FL) algorithm is the adaptable and most prominent way to resolve the shortcoming of ML-based techniques. The study proposes a pelican optimization algorithm with Federated Learning Driven Attack Detection and classification (POAFL-DDC) technique in the IoT. The POAFL-DDC technique operates on decentralized on-device data for attack detection in the IoT. By substituting update weight with the central FL server, the data is placed on the local IoT device whereas federating training cycles on the DL model. For attack detection process, deep belief network (DBN) model is used in this work. In this study, the POA is utilized to optimize the DBN hyperparameters. The experimental validation of the POAFL-DDC algorithm is tested on TON_IOT dataset and the results are examined in terms of different measures. The experimental outcomes demonstrated that the POAFL-DDC technique reaches superior results over other models.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Internet of Things Deep learning pelican optimization algorithm Federated Learning Attack detection

来源：评论

学校读者我要写书评

暂无评论

pelican optimization algorithm with convolutional-recurrent hop field neural network for unmanned aerial image classification model

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2024年第33期83卷 79029页

作者： Geetha, Nakkala Sunitha, Gurram Mohan Babu Univ Sch Comp Tirupati AP India

Nowadays, unmanned aerial vehicles (UAVs) becomes more prominent because of their benefits namely versatile, inexpensive, scalable, and autonomous. The aerial image classification procedure has involved further attention to the UAV-allowed surveillance model facilitating objective detection, and tracking procedure and significantly enhancing the visual surveillance outcomes. Earlier methods to resolve the scene classification method dependent upon feature coding systems with unsupervised feature learning or low-level hand-engineered features can make mid-level image features with limited representational capabilities that usually limit them from the achievement of effectual outcomes. The development of convolutional neural networks (CNNs) helps to attain an efficient classification process in the area of image classification. This paper introduces a pelican optimization algorithm with Convolutional-Recurrent Hop-Field Neural Network (POA-CRHFNN) model for the UAV image classification model. The presented POA-CRHFNN method initially involves preprocessing in two stages namely Gaussian Filter (GF) based noise elimination and contrast enhancement. In addition, the POA-CRHFNN method exploits the ShuffleNetv2 CNN model for feature vector generation. For aerial image classification, the CRHFNN model is used, which allocates different types of classes to the aerial images. The combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in the CRHFNN can capture both spatial and temporal dependencies in aerial images. The integration of the Hopfield Neural Network (HNN) can recognize and classify aerial images based on learned patterns and associations. Finally, the POA is applied for fine-tuning the hyperparameters of the CRHFNN model. By leveraging the behavioural patterns of pelicans, the POA may explore the search space effectively and potentially find better hyperparameter configurations for improved classification performance. The effectiv

关键词： Aerial image classification Deep learning Unmanned aerial vehicles pelican optimization algorithm Image processing

来源：评论

学校读者我要写书评

暂无评论

Novel pelican optimization algorithm (POA) With Stacked Sparse Autoencoder (SSAE) Based IDS for Network Security

引用

Transactions on Emerging Telecommunications Technologies 2025年第5期36卷

作者： R. Kanimozhi A. Neela Madheswari Department of Computer Science and Engineering Muthayammal Engineering College Rasipuram Tamil Nadu India Faculty of Computer Science and Engineering Mahendra Engineering College Namakkal Tamil Nadu India

Security is a crucial factor for information systems and other vital infrastructures. Ensuring robust security measures is imperative due to the substantial volume of network traffic. On the other hand, many network components are susceptible to cyber threats and attacks due to their inherent properties. The increasing use of networks paves the way for widespread security vulnerabilities. In this context, the implementation of intrusion detection systems (IDS) plays a key role in safeguarding information systems and their network architectures. This research introduces an optimized deep learning model aimed at improving network security by accurately detecting intrusions. The proposed IDS, also termed as the POA-SSAE IDS model (pelican optimization model-stacked sparse autoencoder), integrates a POA for optimal feature selection and an SSAE for feature classification. The effectiveness of this IDS was tested using benchmark datasets, namely CICIDS2018 and KDDCUP'99. The results exhibited the proposed model's superior performance, achieving an accuracy of 97.45% on the CICIDS2018 dataset and 98.7% accuracy on the KDDCUP'99 dataset.

关键词： data privacy deep learning network security optimization pelican optimization algorithm stacked sparse autoencoder

来源：评论

学校读者我要写书评

暂无评论

Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function

引用

SOFT COMPUTING 2023年第15期27卷 10607-10646页

作者： Song, Hao-Ming Xing, Cheng Wang, Jie-Sheng Wang, Yu-Cai Liu, Yu Zhu, Jun-Hua Hou, Jia-Ning Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

pelican optimization algorithm (POA) is a new heuristic algorithm that simulates the pelican's natural behavior in the hunting process. In order to improve the convergence speed and accuracy of the original algorithm and to solve the problem that the original algorithm is easy to fall into local optimization, an improved POA based on chaotic interference factor and elementary mathematical function is proposed. In this paper, ten different chaotic interference factors are introduced in the exploration stage of POA. After selecting an improved POA with the best performance, six different elementary mathematical functions are introduced in the exploitation stage of POA to improve its optimization performance. Then 30 benchmark functions in CEC-BC-2017 were used to test the performance of different improved algorithms. The experimental results showed that the performance of the improved algorithms have been improved effectively compared with the original POA, and the accuracy and optimization ability to balance exploration and exploitation were significantly improved. Compared with seven different algorithms, the feasibility of the improved POA proposed in this paper is proved. Finally, four engineering design problems are optimized, and the simulation results show that among four different engineering design problems, the improved POA proposed in this paper is obviously superior to the original POA, which proves that the improved POA based on chaotic interference factor and elementary function is competitive in optimization performance on function optimization and practical engineering applications.

关键词： pelican optimization algorithm Chaotic mapping Elementary mathematical function Function optimization Engineering optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：