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37th International Conference on Information Networking (ICOIN)

作者： Ei, Nway Nway Aung, Pyae Sone Park, Seong-Bae Huh, Eui-Nam Hong, Choong Seon Kyung Hee Univ Dept Comp Sci & Engn Yongin 17104 Gyeonggi Do South Korea

ISBN: (纸本)9781665462686

Low earth orbit (LEO) satellites and high altitude platforms (HAPs) have recently gained popularity due to its seamless connectivity and global coverage. The aerial network comprising high altitude platforms (HAPs) collects the generated data of the ground internet of things (IoT) devices and then transmits the aggregated data to the terrestrial data processing center via the LEO satellites. However, LEO satellites are generally orbiting with high speed and their visibility times are limited according to the elevation angle and orbital height. Moreover, it is challenging for the HAPs to determine the feasible LEO satellite to associate with and decide the optimal transmit power due to their power constraint. Therefore, in this paper, we formulate a joint association and power allocation problem to maximize the data transmitted by the HAPs and minimize the energy consumption by considering the HAP's power budget and QoS requirements as well as the data storage and visibility time of the LEO satellites. To address the formulated mixed-integer non-convex problem, the two sub-problems are introduced, namely, HAP-LEO satellite association and power allocation problems, which are then alternately solved by using the GUROBI optimizer and whale optimization method, respectively. The evaluation results have demonstrated that the proposed approach achieves the superior performance when compared to the random scheme.

关键词： Low earth orbit satellites high altitude platforms LEO satellite's visibility time whale optimization algorithm

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LFC Scheme for A Deregulated Multi-Area Power System using Cascaded Fractional Order Controller 1

LFC Scheme for A Deregulated Multi-Area Power System using C...

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1st International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET)

作者： Murali, Sariki Shankar, Ravi Shrivastav, Shivam Dhawal, Utkarsh Kannauijia, Vishal Natl Inst Technol Patna Dept Elect Engn Patna 800005 Bihar India

ISBN: (纸本)9781728175492

Active power imbalance between electrical power generation and demand causes deviation in frequency and tieline power of an interconnected power system. Load Frequency Control (LFC) is an epoch-making method to maintain nominal frequency and tie-line power of a power system. Also, continuous research is going on to improve the effectiveness of the LFC mechanism. In this scenario, this paper contributes to design an effective controller configuration for LFC mechanism of a nonlinear multi area interconnected system. A cascaded combination of Proportional-Integral controller with Fractional Order Proportional-Derivative controller (PI-FOPDAN) has been proposed in this work. One of the popular whale optimization algorithm (WOA) has been deployed to optimize the proposed controller parameters. The proposed scheme has been compared with several conventional controllers and its robustness has been checked by subjecting it to variation in power system model parameters. Deregulation environment of power system is also incorporated for more realistic approach.

关键词： Cascaded PI-(PDN)-N-2 Deregulation LFC Nonlinear whale optimization algorithm

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Improved Spotted Hyena Optimizer Fused with Multiple Strategies 40th

Improved Spotted Hyena Optimizer Fused with Multiple Strateg...

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40th National Conference of Theoretical Computer Science (NCTCS)

作者： Mo, Chunhui Wang, Xiaofeng Zhang, Lin North Minzu Univ Coll Comp Sci & Engn Yinchuan 750021 Ningxia Peoples R China North Minzu Univ Ningxia Key Lab Informat & Big Data Proc Yinchuan 750021 Ningxia Peoples R China

ISBN: (纸本)9789811981517;9789811981524

Aiming at the shortcomings of spotted hyena optimizer, such as slow convergence speed, low searching accuracy and easy to fall into local optimal, this paper proposes an improved spotted hyena optimization algorithm (OWSHO) integrating multiple strategies. The reverse population is constructed by using the Opposition-Based Learning strategy to increase the diversity of the population and further improve the convergence speed of the algorithm. At the same time, the spiral exploration mechanism of whale optimization algorithm is combined to enhance the ability of exploring unknown regions and improve the global search performance of the algorithm. Then adaptive weight strategy is introduced to balance and improve the global exploration and local development ability of the algorithm. In this paper, 8 benchmark functions of CEC test set are used for simulation experiments, and compared with 3 heuristic algorithms, the test results show that: The improved spotted hyena optimization algorithm based on the combination of reverse learning, spiral exploration mechanism and adaptive weight strategy has a great improvement in the search accuracy and convergence speed, and gets rid of the local optimal to a certain extent, which proves the effectiveness and advancement of the algorithm.

关键词： Spotted Hyena Optimizer whale optimization algorithm Opposition-based learning Spiral exploration Adaptive weight

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Intrusion detection System based on Hybrid whale-Genetic algorithm 2

Intrusion detection System based on Hybrid Whale-Genetic Alg...

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International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： Bilaiya, Riya Sharma, Rajeev Mohan Natl Inst Technol Kurukshetra Dept Comp Engn Kurukshetra Haryana India

ISBN: (纸本)9781538619742

As the community is now a days moving rapidly towards the cloud, its security is something we really need to think and research about. The problem of security is as old as the internet is, and hence a number of solutions for the problem has also been thought of. Many of the various solutions consists using firewalls for the network or in the host, anti-malware programs installed on the system, packet-sniffing techniques for detection of malicious entities as well as fake traffic, pattern and signature matching techniques for various files that the system has to deal with every now and then. One of these is solutions is deploying Intrusion Detection System (IDS) in the network. Here, we have devised a model that will be able to find any unwanted intrusions in the network. For this, we have employed Genetic algorithm (GA) with the whale optimization algorithm which we will see is an effective algorithm to use. In the first step, an Intrusion Detection System (IDS) is built using GA with natural selection method. The proposed hybrid algorithm in this scheme is then tested for the detection of malicious network traffic date set.

关键词： IDS Genetic algorithm whale optimization algorithm Hybrid

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Improvement optimal power flow solution under loading margin stability using new partitioning whale algorithm

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2019年第1期14卷 64-77页

作者： Mahdad, Belkacem Univ Biskra Dept Elect Engn Biskra Algeria

This paper focuses on the solution of the optimal power flow (OPF) problem considering load growth using a novel metaheuristic optimization algorithm called whale optimization algorithm (WOA). An interactive partitioning procedure is proposed to achieve a better solution and to improve search space diversity. In this study the new variant named partitioning WOA is applied to improve the security optimal power flow (SOPF) by minimization the total production cost with different complexities and constraints such as valve point effect and prohibited zones, total power loss and total voltage deviation considering critical load growth. The efficiency of the proposed algorithm has been validated on the IEEE 30-Bus, the Algerian 59-bus power system and to the large test system IEEE 118-Bus for various standard objective functions. For the test system IEEE-30 Bus, a competitive optimized quadratic cost (798.9225 $) is achieved compared to many recent techniques;also significant amounts of power loss and voltage deviation have been reduced for all test systems without affecting security constraints in particular for the IEEE 118-Bus, the total loss being reduced from 132.86 MW at base case (without optimization) to a competitive value 8.6099 MW. The proposed strategy proves its remarkable ability in solving the practical security constrained OPF for modern large power systems.

关键词： Power system planning OPF whale optimization algorithm partitioning strategy boundary handling loading margin stability SVC

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IoT Resource Allocation and optimization Based on Heuristic algorithm

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SENSORS 2020年第2期20卷 539-539页

作者： Sangaiah, Arun Kumar Hosseinabadi, Ali Asghar Rahmani Shareh, Morteza Babazadeh Bozorgi Rad, Seyed Yaser Zolfagharian, Atekeh Chilamkurti, Naveen VIT Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India Islamic Azad Univ Behshahr Branch Dept Comp Engn Behshahr 51148515 Iran Islamic Azad Univ Babol Branch Dept Comp Engn Babol Sar *** Iran La Trobe Univ Dept Comp Sci & Comp Engn Melbourne Vic 3086 Australia

The Internet of Things (IoT) is a distributed system that connects everything via internet. IoT infrastructure contains multiple resources and gateways. In such a system, the problem of optimizing IoT resource allocation and scheduling (IRAS) is vital, because resource allocation (RA) and scheduling deals with the mapping between recourses and gateways and is also responsible for optimally allocating resources to available gateways. In the IoT environment, a gateway may face hundreds of resources to connect. Therefore, manual resource allocation and scheduling is not possible. In this paper, the whale optimization algorithm (WOA) is used to solve the RA problem in IoT with the aim of optimal RA and reducing the total communication cost between resources and gateways. The proposed algorithm has been compared to the other existing algorithms. Results indicate the proper performance of the proposed algorithm. Based on various benchmarks, the proposed method, in terms of "total communication cost", is better than other ones.

关键词： internet of things resource allocation problem whale optimization algorithm communication cost

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Capacity-based optimization using whale optimization technique of a power distribution network

ENGINEERING REPORTS

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ENGINEERING REPORTS 2022年第1期4卷

作者： Simachew, Bawoke Khan, Baseem Guerrero, Josep M. Padmanaban, Sanjeevikumar Mahela, Om Prakash Alhelou, Hassan Haes Natl Taipei Univ Technol Dept Elect Engn Taipei Taiwan Hawassa Univ Dept Elect & Comp Engn Awasa Ethiopia Aalborg Univ Ctr Res Microgrids CROM Aalborg Denmark Aarhus Univ CTiF Global Capsule Dept Business Dev & Technol Herning Denmark Rajasthan Rajya Vidyut Prasaran Nigam Ltd Power Syst Planning Div Jaipur Rajasthan India Tishreen Univ Dept Elect Power Engn Latakia Syria

In the power distribution network, real power loss and voltage profile management are critical issues. By providing active and reactive power support, both of these issues can be managed. Distributed generation (DG) and capacitor bank (QG) can be utilized to solve these issues. Therefore, this paper utilized optimally placed and sized DG and capacitor (QG) to minimize losses and improve the voltage profile. The overall problem is optimized using an upgraded method of the fitness assignment and solution chasing based on the aggregate approach called multi-objective whale optimization algorithm (MWOA). Wind and solar photovoltaic sources with biomass are utilized as the DG sources with their probabilistic outputs. The developed method is tested using two practical feeders of Bahir Dar city distribution network, Ethiopia. The results of loss minimization and voltage profile enhancement with MWOA are compared with multi-objective particle swam optimization (MPSO) with an equal number of iterations to show the superiority of the developed method.

关键词： capacitor placement distributed generation loss minimization particle swarm optimization whale optimization algorithm

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Less: Large-scale Workload Forecasting Model Based on Multiple Sequence Compression

Less: Large-scale Workload Forecasting Model Based on Multip...

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IEEE International Conference on Web Services (IEEE ICWS)

作者： Ding, Zeyuan Feng, Binbin Yu, Wangyang Zhang, Bolan Shaanxi Normal Univ Sch Comp Sci Xian Peoples R China Tongji Univ Dept Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798350368567;9798350368550

As application migration to the cloud becomes the mainstream way of application deployment, application run-time management presents a significant need for large-scale workload prediction technology. Existing large-scale workload approaches commonly construct training sets based on all the training samples generated from the original data to generate prediction models. However, due to the similar behavior between different container instances of the microservice application, training and modeling in this way results in a huge number of redundant samples, which produces a significant redundant training overhead. Therefore, this paper proposes Less, a large-scale workload forecasting model based on multiple sequence compression. First, based on the grouping results of similar containers, a container workload feature recognition algorithm is proposed to determine the common and individual features of container workloads in each prediction period, so as to guide the compression of workload sequences within each group;second, a fitness function that takes into account the common features, individual features, and the number of sequences are designed, and the optimal compressed sequences are solved by the whale optimization algorithm to efficiently reduce the number of redundant training workload sequences, and then the Bidirectional Gated Recurrent Unit model is built and trained based on the compressed sequences, which effectively reduces the model complexity and overhead while ensuring the accuracy. Finally, we validate the comprehensive advantages of Less in terms of accuracy and overhead based on public datasets and verify the effectiveness of each subpart of our model through ablation experiments.

关键词： large-scale multiple sequence compression feature recognition common features individual features whale optimization algorithm fitness function Bi-directional Gated Recurrent Unit

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IWOA-RBF Neural Network Ultra-Wideband Antenna Modeling Method

IWOA-RBF Neural Network Ultra-Wideband Antenna Modeling Meth...

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Nan, Jingchang Sun, Wenwen Liaoning Tech Univ Huludao 125105 Peoples R China

ISBN: (纸本)9781665478342

To solve the time-consuming problem of nonlinear data processing and modeling in the antenna structure, a modeling method based on the Improved whale optimization algorithm (IWOA) to optimize the Radial Basis Function (RBF) neural network is proposed. The improved algorithm introduces nonlinear factors in the stage of hunting prey and introduces inertia weights when searching for positions to avoid being limited to local extreme values. In the stage of attacking the prey, the Cauchy strategy is used to improve the global search ability of individual whales and expands the solution space. Finally, the IWOA-RBF network proposed in this paper is simulated and predicted for the ultra-wideband stepped microstrip monopole antenna, and the accuracy of the model is observed by comparing the root mean square error (RMSE) of the return loss. The simulation results verify that compared with the WOA-RBF network and the RBF network, the accuracy of the IWOA-RBF neural network modeling method proposed in this paper is improved by 135.71% and 85.63%, respectively, and the time consumption is shorter, which proves the feasibility of this model.

关键词： radial basis function neural network ultra-wideband antenna whale optimization algorithm Cauchy strategy nonlinear convergence factors

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Optimizing constrained engineering problem nH-WDEOA: using hybrid nature-inspired algorithm

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1899-1907页

作者： Mishra, Pawan Pooja Tripathi, Shashi Prakash Department of Electronics and Communication University of Allahabad UP Prayagraj India Department of Applied Mathematics and Computer Science Liverpool John Moores University Liverpool United Kingdom

Handling optimization problems can be a challenging task. In order to achieve optimality, several variants of nature-inspired algorithms viz. swarming based, population-based and a few based on physical laws have been proposed with different working mechanisms. Although, nature-inspired algorithms perform mathematical operations mimicking certain unique feature of a natural specie to obtain the best optimal solutions, nevertheless, they can be inefficient in term of easily falling in local optimal region, providing the best convergence quality, premature convergence, slow convergence rate etc. The present work provides a different hybrid approach for optimization, named as novel hybridized whale-differential evolution optimization algorithm (nH-WDEOA). © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Constraint optimisation problem Differential evolution algorithm Nature inspired algorithms whale optimization algorithm

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