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Hamiltonian deep neural network optimized with pelican optimization algorithm-fostered substrate-integrated waveguide antenna design for 5G

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JOURNAL OF COMPUTATIONAL ELECTRONICS 2024年第3期23卷 620-633页

作者： Gurulakshmi, A. B. Rajesh, G. Saroja, B. Jackulin, T. New Horizon Coll Engn Dept Elect & Commun Engn Bengaluru India Siddartha Inst Sci & Technol Dept Elect & Commun Engn Puttur Andhra Pradesh India Panimalar Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India

Due to the growing need for higher speed data, the 5G terrestrial heterogeneous wireless network deployments are expected to happen quickly throughout the world in the next decade. In such type of networks, mm-wave small-cells overlapped the sub-6 GHz macro-cells being used to serve to population-rich areas. Subsequently, many problems appear with the antenna design technologies. The presented antenna is functioning at a frequency range from 24.8 to 31.6 GHz, with a 24% bandwidth and 8.5 dB peak gain at 27 GHz. It encompasses the complete 28 GHz frequency band utilized through 5G applications. Consequently, fifth-generation communication systems are best suited for it. The proposed Hamiltonian deep neural network optimized with pelican optimization algorithm-fostered Substrate-Integrated Waveguide Antenna Design for 5G (SIW-HDNN-POA-5G) is implemented, and performance of proposed technique is estimated based on several metrics, including resonant frequency (GHz), reflection coefficient (S11 in dB), mean absolute error (MAE), and root mean square error (RMSE). The proposed SIW-HDNN-POA-5G method provides 24.36%, 33.55% and 44.22% higher gain and 43.21%, 38.87% and 25.65% lesser mean absolute error comparing to the existing designs, like Design of Zero Clearance SIW End fire Antenna Array Based on Machine Learning-Assisted optimization (SIW-MLAO-5G), SIW-Fed Wideband Filtering Antenna for Millimeter-Wave Applications (SIW-5G-MLOM), and Compact SIW Fed Dual-Port Single Element Annular Slot MIMO Antenna for 5G mm Wave Applications (SIW-FWFA-MMWA), respectively.

关键词： Band width Hamiltonian deep neural network pelican optimization algorithm Substrate-integrated waveguide antenna 5G Vias and pads

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Reservoir Porosity Prediction Based on BiLSTM-AM Optimized by Improved pelican optimization algorithm

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ENERGIES 2024年第6期17卷 1479页

作者： Qiao, Lei He, Nansi Cui, You Zhu, Jichang Xiao, Kun Hebei Petr Univ Technol Hebei Instrument & Meter Engn Technol Res Ctr Chengde 067000 Peoples R China Hebei Petr Univ Technol Dept Comp & Informat Engn Chengde 067000 Peoples R China PetroChina Res Inst Petr Explorat & Dev Beijing 100083 Peoples R China East China Univ Technol State Key Lab Nucl Resources & Environm Nanchang 330013 Peoples R China

To accurately predict reservoir porosity, a method based on bi-directional long short-term memory with attention mechanism (BiLSTM-AM) optimized by the improved pelican optimization algorithm (IPOA) is proposed. Firstly, the nonlinear inertia weight factor, Cauchy mutation, and sparrow warning mechanism are introduced to improve the pelican optimization algorithm (POA). Secondly, the superiority of IPOA is verified by using the CEC-2022 benchmark test functions. In addition, the Wilcoxon test is applied to evaluate the experimental results, which proves the superiority of IPOA against other popular algorithms. Finally, BiLSTM-AM is optimized by IPOA, and IPOA-BiLSTM-AM is used for porosity prediction in the Midlands basin. The results show that IPOA-BiLSTM-AM has the smallest prediction error for the verification set samples (RMSE and MAE were 0.5736 and 0.4313, respectively), which verifies its excellent performance.

关键词： porosity prediction bi-directional long short-term memory pelican optimization algorithm nonlinear inertia weight factor Cauchy mutation sparrow warning mechanism

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Chaotic Binary pelican optimization algorithm for Feature Selection

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2023年第3期31卷 497-530页

作者： Eluri, Rama Krishna Devarakonda, Nagaraju VIT AP Univ Sch Comp Sci & Engn Amaravati 522237 Andhra Pradesh India

This research proposes a new wrapper model based on chaos theory and nature-inspired pelican optimization algorithm (POA) for feature selection. The base algorithm is converted into a binary one and a chaotic search to augment POA's exploration and exploitation process, denoted as chaotic binary pelican optimization algorithm (CBPOA). The main focus of chaos theory is to resolve the slow convergence rate as well as entrapment in local optimal issues of classical POA. Therefore, ten dissimilar chaotic maps are entrenched in POA to tackle these issues and attain a more robust and effective search mechanism. CBPOA executes on continuous search;thus, the continuous search is reformed to a discrete one by adapting transfer functions. In CBPOA, eight transfer functions are used to find the best one and inspect CBPOA. Consequently, the performance of the CBPOA has been investigated by targeting several metrics under 18 UCI datasets. The best variant is nominated and explored the performance with classical wrapper-based and filter-based schemes. Furthermore, the proposed CBPOA is evaluated using 23 functions from CEC-2017, 2018 and 2020 benchmarks. As an outcome, CBPOA has accomplished better outcomes than existing schemes and is superior in handling feature selection problems.

关键词： pelican optimization algorithm chaotic maps feature selection transfer functions classification

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Enhancing distribution generator impact mitigation using an adaptive protection scheme based on modified pelican optimization algorithm and active database management system

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ENERGY SCIENCE & ENGINEERING 2023年第11期11卷 4108-4127页

作者： Abdelhamid, Mohamed Kamel, Salah Zeinoddini-Meymand, Hamed Aswan Univ Fac Engn Elect Engn Dept Aswan Egypt Grad Univ Adv Technol Dept Elect & Comp Engn End haft Bagh HwyPOB 76315-117 Kerman Iran

This paper addresses the challenge of protecting electrical networks in the presence of distribution generators (DGs). The use of DGs affects fault currents, leading to miscoordination between protection relays and causing constraints on network reliability. To tackle this issue, the authors propose an adaptive protection scheme (APS) based on a modified pelican optimization algorithm (MPOA) and active database management system (ADBMS). The APS coordinates directional overcurrent relays and distance relays, while the MPOA simulates a pelican mating strategy and includes a modified internal equation. The proposed APS is further upgraded with ADBMS to save system resources by storing relay settings in the database and calling them when the state of DGs changes without running optimization algorithms. The effectiveness of the proposed APS is validated on the Institute of Electrical and Electronics Engineers (IEEE) eight-bus test system and the IEEE 14-bus distribution network. Results indicate that the APS can effectively protect electrical networks in the presence of DGs, while the ADBMS upgrade saves system resources. The use of distribution generators affects fault currents, leading to miscoordination between protection relays and causing constraints on network reliability. To tackle this issue, an adaptive protection scheme (APS) based on a modified pelican optimization algorithm (MPOA) and active database management system is proposed. The APS coordinates directional overcurrent relays and distance relays, while the MPOA simulates a pelican mating strategy and includes a modified internal ***

关键词： adaptive protection scheme distribution generators electrical grid pelican optimization algorithm protection relays

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Multi-objective pelican optimization algorithm for Engineering Design Problems 19th

Multi-objective Pelican Optimization Algorithm for Engineeri...

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19th International Conference on Distributed Computing and Intelligent Technology (ICDCIT)

作者： Naidu, Y. Ramu Natl Inst Technol Andhra Pradesh Sch Sci Tadepalligudem Andhra Pradesh India

ISBN: (纸本)9783031248474;9783031248481

This work presents an efficient multi-objective version of the pelican optimization algorithm (POA) which is recently proposed in the family of meta-heuristic algorithms. It is called a multi-objective pelican optimization algorithm (MOPOA). From the literature, it is observed that the POA performed well on a set of unconstrained classical optimization problems as well as some engineering design problems. To extend its applicability to multi-objective engineering design models, the MOPOA has been proposed and applied for two engineering design models, four bar truss and speed reducer problems. The obtained results are compared with the literature and they proved that the MOPOA is an efficient and robust optimizer.

关键词： pelican optimization algorithm Multi-objective problems Engineering design problems

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Automated Insect Detection and Classification Using pelican optimization algorithm with Deep Learning on Internet of Enabled Agricultural Sector

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SN Computer Science 2024年第5期5卷 576页

作者： Assiri, Mohammed Elhameed, Elmouez Samir Abd Kumar, Arun Singla, Chinu Department of Computer Science College of Sciences and Humanities- Aflaj Prince Sattam bin Abdulaziz University Aflaj 16273 Saudi Arabia Department of Computer Science College of Post-Graduated Studies Sudan University of Science and Technology Khartoum Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Recently, the combination of Deep Learning (DL) methods within the Internet of Things (IoTs) has developed in the agricultural field, especially in the domain of pest management. This study considers the implementation and development of an innovative method for Insect Detection and Classification using DL within the environment of the IoTs in agriculture. The developed system leverages advanced DL approaches for analyzing images captured by IoT-enabled devices, enabling real-time identification and categorization of insect pests. By continuously incorporating these technologies, this research goals to increase the efficiency and precision of pest monitoring, finally providing to sustainable agricultural technologies and increased crop yield. This study presents an Automated Insect Detection and Classification using pelican optimization algorithm with Deep Learning (AIDC-POADL) technique on Internet of Enabled Agricultural Sector. The main aim of the AIDC-POADL system is to detect and categorize various kinds of insects exist in the agricultural field. In the primary stage, the AIDC-POADL technique involves DenseNet-121 model to learn complex features in the input images. Besides, the hyperparameter selection of the DenseNet-121 model developed by the POA. At last, multilayer perceptron (MLP) model can be applied to discriminate the insects into various classes. To validate the improved performance of the AIDC-POADL algorithm, a series of simulations were involved. The experimental outcomes stated that the AIDC-POADL technique offers enhanced recognition results over other approaches. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Agricultural region Deep learning Insect detection Internet of things pelican optimization algorithm

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Efficient approach for optimal parameter estimation of PV using pelican optimization algorithm

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COGENT ENGINEERING 2024年第1期11卷

作者： Ajay Rathod, Asmita Subramanian, Balaji Vellore Inst Technol Sch Elect Engn Vellore India

In order to optimize the performance of a Solar Photovoltaic (PV) system, it is necessary to develop an appropriate PV cell model and accurately determine the unknown parameters associated with the model. The process of extracting parameters for PV models is a complex optimization issue that involves nonlinearity and multiple models. Accurate estimation of the characteristics of PV units is crucial since these factors significantly affect the performance of PV systems in terms of power and current generation. Consequently, this research presents an advanced methodology, known as the pelican optimization algorithm (POA), aimed to find the optimal values for the unspecified parameters of PV units. In this study, the Single Diode Model (SDM) is employed to analyze four datasets like RTC France, Photowatt-PWP201, STP-120/36, as well as STM6-40/36 PV panels. The POA algorithm is utilized to determine the unknown parameters of solar PV modules. Furthermore, to enhance the precision of the obtained solutions, this study incorporates the Newton-Raphson (NR) method into the POA algorithm. The POA achieves the optimum Root Mean Square Error (RMSE) values for the four PV models (RTC France, Photowatt-PWP201, STM6-40/36 and STP6-120/36) and the values are found to be 7.7298E-04, 2.0528E-03, 1.7220E-03 and 1.4458E-02 respectively. From the results, it is observed that, POA exhibit superior performance compared to the other MH optimization algorithms. Furthermore, the statistical findings show that the POA algorithm has a higher average robustness and accuracy than the other algorithms.

关键词： Solar Photovoltaic Parameter Extraction pelican optimization algorithm Single Diode Model

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optimization of Economic Dispatch Problem using Nature-Inspired pelican optimization algorithm 1

Optimization of Economic Dispatch Problem using Nature-Inspi...

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1st International Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Singh, Sugandh Pratap Khan, Rizwan Chakrabarti, Saikat Sharma, Ankush Singh, Vinay Pratap Indian Inst Technol Kanpur Dept Elect Engn Kanpur Uttar Pradesh India MPMKVVCLBhopal Bhopal MP India Malaviya Natl Inst Technol Jaipur Dept Elect Engn Jaipur Rajasthan India

ISBN: (纸本)9798350349696;9798350349689

Economic Dispatch (ED) is vital to running a power system. It is capable of lowering operational costs and conserving energy resources. The ED problem that is addressed in this paper is non-convex and quadratic in nature, having system constraints such as power balancing and generation limit constraints. The effect of valve-point loading is also considered in the generation cost function. A penalty function strategy is employed to solve the constraint violation problems inherent in this ED problem. The penalized cost of generation is calculated using static penalty functions for infeasible solutions. In this study, we present a pelican optimization algorithm (POA) based strategy for addressing ED problems, which takes its inspiration from the natural world. Inspiring itself from the pelican's foraging habits, POA is a population-based meta-heuristic algorithm with two basic stages: exploration and exploitation. It has a good rate of convergence. The functionality of POA is evaluated using four different test systems of varying degrees of complexity. The results of the experiments and comparisons with other techniques that have been described for ED solutions demonstrate that POA is capable of producing a solution of comparable quality.

关键词： Economic load dispatch meta-heuristic pelican optimization algorithm

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An Efficient QoS Aware Hybrid Localization Model Using pelican optimization algorithm for Cognitive Wireless Sensor Networks 1

An Efficient QoS Aware Hybrid Localization Model Using Pelic...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Kumar, Gotte Ranjith John, Y. M. Mahaboob Patra, Raj Kumar Hussein, A.H.A. Habelalmateen, Mohammed I. School of Computer Science & Artificial Intelligence Sr University Warangal India Mahendra College of Engineering Ece Department Salem India Department of Computer Science and Engineering Hyderabad India College of Pharmacy Ahl Al Bayt University Karbala Iraq The Islamic University Najaf Iraq

ISBN: (纸本)9798350316469

The transmission of detected information to a central base station is critical in Wireless Sensor Networks (WSNs). These sensor devices work within the limits of restricted resources due to their compact size and reliance on battery power. To address the constraints of such resource-constrained surroundings, a ground-breaking framework for large-scale applications with an enormous number of sensors distributed across a broad expanse is provided. This novel protocol not only addresses communication challenges, but also ensures the development of a continually flawless communication system. Using cognitive sensors, the integration of learning and thought processes ensures dependable data delivery within the network. Localization is utilized in both sparsely and densely populated sensor networks by applying an intelligent cluster head election and route selection mechanism based on cognitive principles and the pelican optimization algorithm (POA) algorithms. The proposed scheme is examined and compared with the existing methods such as CBSO, EW-DHO and HPSO-CSC. The proposed scheme outperforms Energy consumption by 0.5, Network Lifetime by 6200 and Throughput by 0.99% respectively. © 2023 IEEE.

关键词： Cognitive Wireless Sensor Networks Localization Network Lifetime pelican optimization algorithm Power Management

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A hyperchaotic map with distance-increasing pairs of coexisting attractors and its application in the pelican optimization algorithm

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CHAOS SOLITONS & FRACTALS 2023年第1期173卷

作者： Ge, Xizhai Li, Chunbiao Li, Yongxin Yi, Chenlong Fu, Haiyan Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China Qilu Inst Technol Collaborat Innovat Ctr Memrist Comp Applicat CICMC Jinan 250200 Peoples R China

When both an absolute value function and a cosine function are introduced for chaotic sequence generation, coexisting attractors with different polarities and locations could be extracted by the offset boosting of the initial condition, and thus a hyperchaotic map with distance-increasing coexisting attractors is constructed. It is found that this map has two controllers for rescaling the oscillation totally and partially. The digital circuit implementation shows good consistency with numerical simulation. Moreover, the parameter adaptation in the basic pelican optimization algorithm (POA) is improved by the above-mentioned hyperchaotic map. Correspondingly, the Hyperchaotic pelican optimization algorithm (HCPOA) is constructed, where the hyperchaotic sequences are employed as the sampling pool when random numbers are required in the basic POA. Furthermore, the impact of chaos regulation on the performance of HCPOA is investigated, and the indicators of HCPOA are analyzed by employing five benchmark functions, where the single attractor shows better performance than the double-cavity attractor. The methods of HCPOA proposed in this paper bring the chance to improve the quality of solutions and the ability to escape local optima.

关键词： Hyperchaotic map Coexisting attractors Amplitude control pelican optimization algorithm Benchmark function

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