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accelerated particle swarm optimization Algorithm for Efficient Cluster Head Selection in WSN

Computers, Materials & Continua 2024年第6期79卷 3585-3629页

作者： Imtiaz Ahmad Tariq Hussain Babar Shah Altaf Hussain Iqtidar Ali Farman Ali Institute of Computer Sciences and Information Technology The University of AgriculturePeshawarPakistan School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhouChina College of Technological Innovation Zayed UniversityDubaiUnited Arab Emirates School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Department of Applied AI School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063South Korea

Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and *** of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering *** have several challenges,including throughput,energy usage,and network lifetime *** strategies have been applied to get over these *** may,therefore,be thought of as the best way to solve such ***,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy *** paper proposed an accelerated particle swarm optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)*** the help of APSO in the implementation of the WSN,the main methodology of this article has taken *** simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 *** sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to *** number of dead nodes likewise dropped for the given combination,falling between 71 and *** network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based *** to underwater,WSN can make use of the proposed *** overall results have been evaluated and

关键词： Wireless sensor network cluster head selection low energy adaptive clustering hierarchy accelerated particle swarm optimization

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Chaos-enhanced accelerated particle swarm optimization

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2013年第2期18卷 327-340页

作者： Gandomi, Amir Hossein Yun, Gun Jin Yang, Xin-She Talatahari, Siamak Univ Akron Dept Civil Engn Akron OH 44325 USA Univ Cambridge Dept Engn Cambridge CB2 1PZ England Univ Tabriz Marand Fac Engn Tabriz Iran

There are more than two dozen variants of particle swarm optimization (PSO) algorithms in the literature. Recently, a new variant, called accelerated PSO (APSO), shows some extra advantages in convergence for global search. In the present study, we will introduce chaos into the APSO in order to further enhance its global search ability. Firstly, detailed studies are carried out on benchmark problems with twelve different chaotic maps to find out the most efficient one. Then the chaotic APSO (CAPSO) will be compared with some other chaotic PSO algorithms presented in the literature. The performance of the CAPSO algorithm is also validated using three engineering problems. The results show that the CAPSO with an appropriate chaotic map can clearly outperform standard APSO, with very good performance in comparison with other algorithms and in application to a complex problem. (c) 2012 Elsevier B.V. All rights reserved.

关键词： accelerated particle swarm optimization Chaotic maps Global optimization

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Intelligent sizing of a series hybrid electric power-train system based on Chaos-enhanced accelerated particle swarm optimization

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APPLIED ENERGY 2017年 189卷 588-601页

作者： Zhou, Quan Zhang, Wei Cash, Scott Olatunbosun, Oluremi Xu, Hongming Lu, Guoxiang Univ Birmingham Dept Mech Engn Birmingham B15 2TT W Midlands England

This paper firstly proposed a novel HEV sizing method using the Chaos-enhanced accelerated particle swarm optimization (CAPSO) algorithm and secondly provided a demonstration on sizing a series hybrid electric powertrain with investigations of chaotic mapping strategies to achieve the global optimization. In this paper, the intelligent sizing of a series hybrid electric powertrain is formulated as an integer multi objective optimization issue by modelling the powertrain system. The intelligent sizing mechanism based on APSO is then introduced, and 4 types of the most effective chaotic mapping strategy are investigated to upgrade the standard APSO into CAPSO algorithms for intelligent sizing. The evaluation of the intelligent sizing systems based on standard APSO and CAPSOs are then performed. The Monte Carlo analysis and reputation evaluation indicate that the CAPSO outperforms the standard APSO for finding the real optimal sizing result with much higher reputation, and CAPSO with logistic mapping strategy is the most effective algorithm for HEV powertrain components intelligent sizing. In addition, this paper also performs the sensitivity analysis and Pareto analysis to help engineers customize the intelligent sizing system. (C) 2016 Published by Elsevier Ltd.

关键词： Hybrid electric powertrain Intelligent components sizing Multi-objective optimization accelerated particle swarm optimization Chaotic mapping strategy

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Band-Area Resource Management Platform and accelerated particle swarm optimization Algorithm for Container Deployment in Internet-of-Things Cloud

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IEEE ACCESS 2022年 10卷 86844-86863页

作者： Ouyang, Mingxue Xi, Jianqing Bai, Weihua Li, Keqin South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China Zhaoqing Univ Sch Comp Sci Zhaoqing 526061 Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

The method of building and deploying applications through the combination of container virtualization technology and a microservices framework has been widely used in Internet-of-Things clouds. However, there are gaps and a lack of coordination mechanisms between the Internet-of-Things and cloud computing. This study constructs a resource management platform, which is based on application container virtualization technology and combined with the microservices framework. The platform provide a support environment for the construction and deployment of Internet-of-Things cloud applications. However, there is no unified specification for the microservices templates. Therefore, a new service model called tool service was designed. The invocation relationship between services is studied, and developers can combine services through the invocation relationship between services to form a service function chain. However, container-based service deployment remains an unresolved issue. The deployment method of a container involves the quality of service of end users and the profit of cloud providers. To balance the profits of both parties, it is necessary to minimize the service response time and improve the resource utilization of the cloud data center. To address this problem, an accelerated particle swarm optimization strategy is proposed to realize service deployment. Through the invocation relationship between services, the execution containers are aggregated, so as to reduce the service transmission overhead and improve resource utilization. Compared with the experimental results of existing deployment strategies, the proposed optimization strategy has significantly improved performance parameters such as service transmission overhead, container aggregation, and resource utilization.

关键词： Containers Cloud computing Internet of Things Microservice architectures Resource management optimization Virtualization particle swarm optimization accelerated particle swarm optimization cloud computing container Internet-of-things microservices multi-objective optimization

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On the performance of accelerated particle swarm optimization for charging plug-in hybrid electric vehicles

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ALEXANDRIA ENGINEERING JOURNAL 2016年第1期55卷 419-426页

作者： Rahman, Imran Vasant, Pandian M. Singh, Balbir Singh Mahinder Abdullah-Al-Wadud, M. Univ Teknol PETRONAS Dept Fundamental & Appl Sci Tronoh Malaysia King Saud Univ Coll Comp & Informat Sci Dept Software Engn Riyadh Saudi Arabia

Transportation electrification has undergone major changes since the last decade. Success of smart grid with renewable energy integration solely depends upon the large-scale penetration of plug-in hybrid electric vehicles (PHEVs) for a sustainable and carbon-free transportation sector. One of the key performance indicators in hybrid electric vehicle is the State-of-Charge (SoC) which needs to be optimized for the betterment of charging infrastructure using stochastic computational methods. In this paper, a newly emerged accelerated particle swarm optimization (APSO) technique was applied and compared with standard particle swarm optimization (PSO) considering charging time and battery capacity. Simulation results obtained for maximizing the highly nonlinear objective function indicate that APSO achieves some improvements in terms of best fitness and computation time. (C) 2015 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V.

关键词： PHEV optimization swarm intelligence Smart grid particle swarm optimization accelerated particle swarm optimization

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A Leader-Follower Based Parallel accelerated particle swarm optimization Algorithm for Smart Grid Resource Allocation

A Leader-Follower Based Parallel Accelerated Particle Swarm ...

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North American Power Symposium (NAPS)

作者： Liaquat, Sheroze Fourney, Robert Hansen, Timothy M. Hussain, Tanveer Celik, Berk South Dakota State Univ Dept Elect Engn & Comp Sci Brookings SD 57007 USA Idaho Natl Lab Power & Energy Syst Idaho Falls ID USA Univ Technol Compiegne AVENUES Compiegne France

ISBN: (纸本)9798350315097

Smart grid resource allocation (SGRA) is a largescale demand response optimization problem to lower the electricity cost of customers by intelligently shifting their schedulable devices via an aggregator. The SGRA problem aims to maximize the aggregator profit while still considering the inconvenience and the availability constraints of each customer's assets. A heuristic approach provides a reliable method to find near-optimal solutions to non-convex, complex, and multi-dimensional problems by reducing the computational time and not relying on gradients. To further improve the computational time of the heuristics, high-performance parallel computing concepts can be used to divide the formulated problem into smaller sub-problems. This research proposes a leader-follower based parallel accelerated particle swarm optimization (APSO) to find near-optimal solutions of SGRA using the OpenMP framework in MATLAB. The leader-follower topology allows the parallel execution of each particle, resulting in lower execution time. The proposed parallel APSO was both significantly faster (2 - 7x) than existing SGRA literature methods, and achieves similar aggregator profit, customer savings, and peak demand reduction.

关键词： accelerated particle swarm optimization demand response HPC leader-follower configuration smart grid resource allocation

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Multi-Objective accelerated particle swarm optimization Technique for Scientific workflows in IaaS cloud 7

Multi-Objective Accelerated Particle Swarm Optimization Tech...

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7th International Conference on Computing, Communications and Informatics (ICACCI)

作者： Adhikari, Mainak Amgoth, Tarachand Indian Sch Mines Indian Inst Technol Dept Comp Sci & Engn Dhanbad Bihar India

ISBN: (纸本)9781538653142

Efficient workflow scheduling with multi-objective optimizations for various scientific workflows is a challenging issue in the cloud environment. The cloud providers often try to deploy the tasks to the suitable VM (Virtual Machine) instances while meeting the QoS constraints of the workflow, such as deadline and budget. However, the QoS constraints are conflicted with each other, i.e. the execution speeds of the cheaper VM instances are slower than the expensive VM instances. Furthermore, the existing scheduling strategies minimize one of the objectives of workflow scheduling such as minimizing the total execution time or cost while meeting a QoS constraint. To overcome the above-mentioned problem, in this paper we propose a multi-objective workflow scheduling strategy referred to MAPSO. The algorithm devises an efficient strategy to select the best-fit VM instance for each task based on the accelerated particle swarm optimization technique. This may minimize the total execution time and cost of the workflow while meeting multiple QoS constraints. The algorithm also devises an efficient strategy to find an optimal schedule of the tasks which may maximize the throughput of the servers. We simulate and compare the MAPSO algorithm with the current state-of-arts-algorithms over various scientific workflows.

关键词： Cloud computing scientific workflows accelerated particle swarm optimization workflow scheduling QoS constraints resource utilization

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Image Denoising Using Neural Network Based accelerated particle swarm optimization

Image Denoising Using Neural Network Based Accelerated Parti...

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IEEE Power, Communication and Information Technology Conference (PCITC)

作者： Mishra, Satyasis Bisoi, Ranjeeta Centurion Univ Technol & Management Elect & Commun Engn Dept Bhubaneswar Orissa India Siksha O Anusandhan Univ Multidisciplinary Res Cell Bhubaneswar Orissa India

ISBN: (纸本)9781479974559

This paper proposes a new type of filter called neural network filter for image denoising. The noisy images are fed as input to the network and the weights are updated with LMS algorithm. Further the weights are updated by using a recently developed novel optimization technique called accelerated particle swarm optimization (APSO) for gray level image denoising application. Implementation of APSO is easier than the PSO(particle swarm optimization) and GA(Genetic Algorithm) in terms of computational time. The results of comparison of the optimization has been presented.

关键词： accelerated particle swarm optimization Median filtering PSO SNR

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Synthesis of Radiation Patterns of Arrays with Defined Sidelobe Structure Using accelerated particle swarm optimization

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ENGINEERING LETTERS 2020年第2期28卷 517-521页

作者： Dayal, P. A. Sunny Raju, G. S. N. Mishra, S. Gottumukkala, V. K. Varma Centurion Univ Technol & Management Visakhapatnam 531173 Andhra Pradesh India Centurion Univ Technol & Management Bhubaneswar 752050 Odisha India Qualcomm San Diego CA 92121 USA

Pattern synthesis from array antennas has become a commonly problem to address. Several standard and conventional techniques are available in the literature for the above purpose. Depending on the desired shape of the pattern, the methods are chosen for example, Taylor considered a continuous line sources and generated narrow beams with the desired sidelobe levels of equal height close to the main beam. Elliot has reported a method to generate unsymmetrical patterns. There are some applications where the restricted sidelobes in the mid of the sidelobe region are required. To generate such patterns, no work is available in the literature. In view, of this intensive investigations are carried out to generate such patterns. accelerated particle swarm optimization is applied to optimize the above patterns. The simulated patterns are presented for both small and large arrays.

关键词： Linear arrays accelerated particle swarm optimization Pattern synthesis Sidelobe Level (SLL)

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Image Denoising Using Neural Network Based accelerated particle swarm optimization

Image Denoising Using Neural Network Based Accelerated Parti...

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IEEE Power, Communication and Information Technology Conference

作者： Satyasis Mishra Ranjeeta Bisoi Electronics and Communication Engineering Department Centurion University Of Technology And Management Multidisciplinary Research Cell Siksha O Anusandhan University

ISBN: (纸本)9781479974566

This paper proposes a new type of filter called neural network filter for image denoising. The noisy images are fed as input to the network and the weights are updated with LMS algorithm. Further the weights are updated by using a recently developed novel optimization technique called accelerated particle swarm optimization (APSO) for gray level image denoising application. Implementation of APSO is easier than the PSO (particle swarm optimization) and GA (Genetic Algorithm) in terms of computational time. The results of comparison of the optimization has been presented.

关键词： accelerated particle swarm optimization Median filtering PSO SNR

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