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An Energy Efficient particle swarm optimization based VM Allocation for Cloud Data Centre: EEVMPSO

EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2023年第5期10卷 1-15页

作者： Pandey, Abhishek Kumar Singh, Sarvpal Madan Mohan Malaviya Univ Technol Dept Informat Technol & Comp Applicat Gorakhpur Uttar Pradesh India

Cloud computing has witnessed exponential growth in recent years, resulting in a significant surge in energy consumption and operational costs of cloud data centres. Efficiently allocating Virtual Machines (VMs) within these data centres is crucial to achieve energy efficiency and optimize resource utilization. System instability may result from repeated requests for computing resources. One of the most critical difficulties facing virtualization technology is finding the best way to stack virtual machines on top of physical devices in cloud data centers. The host must move virtual machines from overloaded to underloaded hosts as part of load balancing, which has an impact on energy consumption. We propose energy-efficient particle swarm optimization algorithm (EEVMPSO) for Virtual Machine allocation is designed to maximize the load balancing. System resources, including CPU, storage, and memory, are optimized using EEVMPSO. The energy-aware virtual machine migration using the particle swarm optimization algorithm for dynamic VMs placement and energy -efficient cloud data centers. We conducted extensive experiments and simulations to evaluate the performance of the proposed algorithm in comparison to existing VM allocation methods. The results demonstrate the superiority of our approach in achieving energy efficiency and resource optimization. The experimental result shown in the proposed method, consumption energy in comparison to the PAPSO, KHA, EALBPSO, and RACC-MDT algorithm by 10.86%, 18.22%, 25.8%, and 31.34%, respectively, demonstrated the improvements in the service level agreements violation 5.77%, 15.3%, 26.19%, and 30.4%, as well as the average CPU utilization 2.2%, 24%, 22.6%, and 14.6%.

关键词： particle swarm optimization algorithm Cloud Computing Virtual Machine Placement Cloud data center Service level agreements

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A High-Dimensional Feature Selection Method via Selection and Non-selection Operators and Local Search Mechanism in particle swarm optimization 20th

A High-Dimensional Feature Selection Method via Selection an...

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20th International Conference on Intelligent Computing (ICIC)

作者： Zhu, Zhouming Li, Lingjie Xiao, Zhijiao Liu, Songbai Ma, Lijia Lin, Qiuzhen Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Guangdong Lab Artificial Intelligence & Digital E Shenzhen Peoples R China Shenzhen Technol Univ Coll Big Data & Internet Shenzhen Peoples R China

ISBN: (纸本)9789819755806;9789819755813

Recently, the problem of high-dimensional feature selection (FS) has become a current research focus in the field of evolutionary algorithms (EAs). However, most EA-based FS methods still face challenges in effectively combining relevance to remove redundant features, leading to low search efficiency and difficulty in finding high-quality feature subsets. To address this issue, this paper proposes a particle swarm optimization algorithm based on selection and non-selection operators and a local search mechanism, denoted as SNSLS-PSO. First, we design selection and non-selection operators based on Relief-F and roulette wheel selection to improve the quality of the selected feature subsets. Second, we introduce a local search mechanism based on an adaptive mutation operator, thereby avoiding local optima and enhance population diversity. In addition, we enhance the quality of particle selection by designing mutation probabilities for gene positions. The experimental results indicate that, compared to state-of-the-art FS methods, our proposed SNSLS-PSO can more efficiently select feature subsets and demonstrate superior classification performance on 14 high-dimensional datasets.

关键词： Feature selection high-dimensional datasets particle swarm optimization algorithm evolutionary algorithm

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Research on particle swarm Compensation Method for Subdivision Error optimization of Photoelectric Encoder Based on Parallel Iteration

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SENSORS 2022年第12期22卷 4456页

作者： Hou, Han Cao, Guohua Ding, Hongchang Li, Kun Changchun Univ Sci & Technol Mech Engn Fac Changchun 130022 Peoples R China Changchun Univ Sci & Technol Chongqing Res Inst 618 Liangjiang Ave Chongqing 401135 Peoples R China

Photoelectric encoders are widely used in high-precision measurement fields such as industry and aerospace because of their high precision and reliability. In order to improve the subdivision accuracy of moire grating signals, a particle swarm optimization compensation model for grating the subdivision error of a photoelectric encoder based on parallel iteration is proposed. In the paper, an adaptive subdivision method of a particle swarm search domain based on the honeycomb structure is proposed, and a raster signal subdivision error compensation model based on the multi-swarm particle swarm optimization algorithm based on parallel iteration is established. The optimization algorithm can effectively improve the convergence speed and system accuracy of traditional particle swarm optimization. Finally, according to the subdivision error compensation algorithm, the subdivision error of the grating system caused by the sinusoidal error in the system is quickly corrected by taking advantage of the high-speed parallel processing of the FPGA pipeline architecture. The design experiment uses a 25-bit photoelectric encoder to verify the subdivision error algorithm. The experimental results show that the actual dynamic subdivision error can be reduced to 1/2 before compensation, and the static subdivision error can be reduced from 1.264 '' to 0.487 '' before detection.

关键词： interpolation error compensation grating moire fringe FPGA particle swarm optimization algorithm

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Parameter identification of Hammerstein-Wiener nonlinear systems with unknown time delay based on the linear variable weight particle swarm optimization

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ISA TRANSACTIONS 2022年 120卷 89-98页

作者： Li, Junhong Zong, Tiancheng Lu, Guoping Nantong Univ Sch Elect Engn Nantong 226019 Peoples R China

This paper deals with the parameter estimation of Hammerstein-Wiener (H-W) nonlinear systems which have unknown time delay. The linear variable weight particle swarm method is formulated for such time delay systems. This algorithm transforms the nonlinear system identification issue into a function optimization issue in the parameter space, then utilizes the parallel searching ability of the particle swarm optimization and the iterative identification technique to realize the simultaneous estimation of all parameters and the unknown time delay. Finally, parameters in the linear submodule, nonlinear submodule and the time delay are separated from the optimum parameter. Moreover, two illustrative examples are exhibited to evaluate the effectiveness of the proposed method. The simulation results demonstrate that the derived method has fast convergence speed and high estimation accuracy for estimating H-W systems with unknown time delay, and it is applied to the identification of the bed temperature systems. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Hammerstein-Wiener system Parameter estimation Time delay particle swarm optimization algorithm

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particle-swarm-optimization-Based 2D Output Feedback Robust Constraint Model Predictive Control for Batch Processes

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IEEE ACCESS 2022年 10卷 8409-8423页

作者： Zhang, Wangxi Ma, Jian Wang, Liming Jiang, Feng Hainan Normal Univ Sch Math & Stat Haikou 571158 Hainan Peoples R China Hainan Normal Univ Minist Educ Key Lab Data Sci & Intelligence Educ Haikou 571158 Hainan Peoples R China Key Lab Computat Sci & Applicat Hainan Prov Haikou 571158 Hainan Peoples R China Nanjing Univ Inst Philosophy Intelligence & Future Humanity Nanjing 210093 Peoples R China Nanjing Sport Inst Sch Phys Educ & Humanities Nanjing 210014 Peoples R China

For the input and output constraints and uncertainties in batch processes, a 2D output feedback robust constrained model predictive control (MPC) method is designed by combining iterative learning control (ILC), MPC and output feedback. Firstly, an equivalent 2D-FM closed-loop prediction model is established by combining with the proposed output feedback controller. Then an optimization performance index function with terminal constraint is constructed to study its control optimization. According to the designed optimization performance index and Lyapunov stability theory, the feasible MPC problem is obtained by solving the linear matrix inequalities (LMIs). At the same time, the gain of the new output feedback control law is given to ensure that the performance index reaches the minimum upper bound under the constraints of input and output. In order to solve the manual adjustment problem of some parameters in the performance index function, the particle swarm optimization (PSO) algorithm is introduced, and a better solution is found near the controller by using the search optimization method. Finally, taking the injection molding process as an example and comparing with the existing method without using PSO algorithms, it is proved that the above method is more feasible.

关键词： Output feedback Batch production systems Predictive control Performance analysis Uncertainty Prediction algorithms particle swarm optimization Batch processes input-output constraint iterative learning output feedback predictive control particle swarm optimization algorithm

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A particle swarm Optimizer with adaptive dynamic neighborhood for multimodal multi-objective optimization 35

A Particle Swarm Optimizer with adaptive dynamic neighborhoo...

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35th Chinese Control and Decision Conference (CCDC)

作者： Wei, Jingyue Zhang, Enze Ge, Rui Yangzhou Univ Informat Engn Coll Yangzhou Jiangsu Peoples R China

ISBN: (纸本)9798350334722

In this paper, a multi-objective particle swarm optimizer based on adaptive dynamic neighborhood (ADN-MOPSO) is proposed to locate multiple Pareto optimal solutions to solve multimodal multi-objective problems. In the proposed algorithm, a spatial distance-based non-overlapping ring topology is used to form multiple subpopulations for parallel search to enhance the local search capability of the algorithm. In addition, an adaptive dynamic neighborhood selection strategy is proposed to balance the exploration and exploitation capabilities of the algorithm, allowing the size of the subpopulation to change automatically when the neighborhood switch time is met. To prevent the algorithm from premature convergence, a stagnation detection strategy is introduced to apply a Gaussian perturbation operation to the particles that fall into the neighborhood optimum. Finally, the proposed algorithm is used to solve multimodal multi-objective test problems and compared with existing multimodal multi-objective optimization algorithms. The results show that the proposed algorithm can obtain more Pareto solutions when solving different types of multimodal multi-objective functions.

关键词： Multi-objective optimization multimodal multi-objective optimization particle swarm optimization algorithm sub-swarm regrouping ring topology

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Study on Minimum Miscibility Pressure of CO2-Oil System Based on Gaussian Process Regression and particle swarm optimization Model

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JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME 2022年第10期144卷 103002页

作者： Hou, Zhiwei Su, Haizhou Wang, Guodong PetroChina Liaohe Oilfield Explorat & Dev Res Ins Panjin 124010 Liaoning Peoples R China PetroChina Chuanqing Drilling Engn Co Ltd Chengdu 610000 Sichuan Peoples R China

Injecting CO2 into the reservoir can not only improve crude oil recovery but also achieve the goal of CO2 geological storage. It can not only reduce the greenhouse effect but also obtain additional economic benefits. From the perspective of minimum miscible pressure, carbon dioxide flooding can be divided into miscible flooding and immiscible flooding, and miscible flooding is widely used in the oil field. The most important condition for miscible flooding is to achieve the minimum miscible pressure (MMP). In the study, combining the particle swarm optimization (PSO) with Gaussian process regression (GPR), a novel intelligent GPR and particle swarm optimization (GPR-PSO) method was proposed to establish the model of predicting the MMP of the CO2 and oil system. The model uses the database with more data than in the previous literature, with 365 data points, and the value range of the data is also wider. Moreover, the accuracy of GPR-PSO model was evaluated by statistical error and graph error and compared with the prediction results of existing models. The results show that compared with other models, the GPR-PSO model has higher accuracy and wider application range, the mean absolute relative error is only 1.66%. Meanwhile, the reliability of the model is verified by the sensitivity analysis of parameters. The results show that the most influential parameter on the prediction results is the reservoir temperature, and the least influential parameter is the critical temperature of injected gas. The GPR-PSO model can be used not only to predict the MMP of CO2 and oil system but also to predict the MMP of other gases and crude oil system.

关键词： MMP CO2 particle swarm optimization algorithm Gaussian process regression GPR-PSO model

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Efficiency Management of Engineering Projects Based on particle swarm Multi Objective optimization algorithm

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Systems and Soft Computing 2025年

作者： Xiu Luo Zhouxin Yi Depantment of Civil engineering Chengdu Technological University Chengdu 611730 China CSCEC Southwest Cousulting Company Limited Chengdu 610041 China

With the development of information technology, computer technology has been increasingly applied to management. This study aims to address problems such as low efficiency, high cost, and unstable quality in engineering project management. A research proposes an optimization method for engineering project efficiency management based on multi-objective particle swarm optimization algorithm. The research can find a balance between cost, schedule and quality through multi-objective optimization, thereby achieving the maximization of comprehensive benefits. The multi-objective particle swarm algorithm combines a segmented pulse module and a cloud adaptation module. The segmented pulse module is used to improve the global search capability, while the cloud adaptation module achieves fast convergence and global optimization by dynamically adjusting inertia weights. The experimental results showed that the distribution index of the multi-objective particle swarm algorithm was 0.084, and the average convergence index was 0.33. After optimizing the application of a new model in a certain construction project, the cost decreased from 651100 yuan to 456500 yuan. The construction time was reduced from 132 days to 106.19 days. The quality coefficient of the main body increased from 0.78 to 0.96, an increase of 23.08%. This results indicated that the particle swarm optimization algorithm could provide efficient, cost-effective, and high-quality optimization solutions for engineering project management. The new model could effectively reduce the engineering cost, significantly shorten the construction time, and improve the construction quality coefficient. In addition, in medical resource allocation, multi-objective particle swarm optimization algorithm can optimize the resource allocation plan, balancing fairness and efficiency. In transportation planning, multi-objective particle swarm optimization algorithm can optimize path planning, improve transportation efficiency and resou

关键词： particle swarm optimization algorithm Multi-objective optimization Segmented pulse Cloud adaptation Engineering management

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FPFTS: A joint fuzzy particle swarm optimization mobility-aware approach to fog task scheduling algorithm for Internet of Things devices

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SOFTWARE-PRACTICE & EXPERIENCE 2021年第12期51卷 2519-2539页

作者： Javanmardi, Saeed Shojafar, Mohammad Persico, Valerio Pescape, Antonio Univ Napoli Federico II Dept Elect Engn & Informat Technol DIETI Naples Italy Univ Surrey 5G Innovat Ctr Inst Commun Syst Guildford Surrey England

In the Internet of Things (IoT) scenario, the integration with cloud-based solutions is of the utmost importance to address the shortcomings resulting from resource-constrained things that may fall short in terms of processing, storing, and networking capabilities. Fog computing represents a more recent paradigm that leverages the wide-spread geographical distribution of the computing resources and extends the cloud computing paradigm to the edge of the network, thus mitigating the issues affecting latency-sensitive applications and enabling a new breed of applications and services. In this context, efficient and effective resource management is critical, also considering the resource limitations of local fog nodes with respect to centralized clouds. In this article, we present FPFTS, fog task scheduler that takes advantage of particle swarm optimization and fuzzy theory, which leverages observations related to application loop delay and network utilization. We evaluate FPFTS using an IoT-based scenario simulated within iFogSim, by varying number of moving users, fog-device link bandwidth, and latency. Experimental results report that FPFTS compared with first-come first-served (respectively, delay-priority) allows to decrease delay-tolerant application loop delay by 85.79% (respectively, 86.36%), delay sensitive application loop delay by 87.11% (respectively, 86.61%), and network utilization by 80.37% (respectively, 82.09%), on average.

关键词： fog computing Internet of Things Mamdani fuzzy inference system particle swarm optimization algorithm resource management task scheduling

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Path planning of surgical needle: A new adaptive intelligent particle swarm optimization method

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2022年第4期44卷 766-774页

作者： Tan, Zhen Liang, Hua-Geng Zhang, Dan Wang, Qing-Guo Zhejiang Univ Technol Res Ctr Automat & Artificial Intelligence 288 Liuhe Rd Hangzhou 310023 Zhejiang Peoples R China Huazhong Univ Sci & Technol Union Hosp Tongji Med Coll Dept Urol Wuhan Peoples R China Beijing Normal Univ Zhuhai Chair Prof Inst Artificial Intelligence & Future Beijing Peoples R China BNU HKBU United Int Coll Beijing Peoples R China

Percutaneous puncture interventional therapy is an important method for pathological examination, local anesthesia, and local drug delivery in modern clinics. Due to the existence of complex obstacles such as nerves, arteries, bones and so on in the puncture path, it is a challenging work to design the optimal path for surgical needle. In this paper, we propose a new path planning method based on the adaptive intelligent particle swarm optimization (PSO) algorithm with parameter adjustment mechanism. First, force and motion analysis are carried out on the bevel-tip flexible needle after piercing into human tissues, the motion model of the needle and the spatial transformation model of puncture route in three-dimensional space are obtained, respectively. Then, a multi-objective function is established, which includes puncture path length function, puncture error function and collision detection function. Finally, the optimal puncture path is obtained based on the adaptive intelligent PSO algorithm. The simulation results show that the newly proposed path planning method has higher efficiency, better adaptability to complex environments and higher accuracy than other path planning methods in literature.

关键词： Path planning surgical needle particle swarm optimization algorithm multi-objective optimization intelligent parameter adjustment

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