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Reliability in Cloud Computing Applications with chaotic particle swarm optimization algorithm

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第7期15卷 1180-1190页

作者： Wang, Wenli Bai, Yanlin Zhengzhou Railway Vocat & Tech Coll Sch Artificial Intelligence Zhengzhou 450000 Peoples R China

In recent years, IT managers of large enterprises and stakeholders have turned to cloud computing due to the benefits of reduced maintenance costs and security concerns, as well as access to high-performance hardware and software resources. The two main challenges that need to be considered in terms of importance are ensuring that everyone has access to services and finding efficient allocation options. First, especially with software services, it is very difficult to predict every service that may be needed. The second challenge is to select the best independent service among different providers with features related to application reliability. This paper presents a framework that uses the particle swarm optimization technique to optimize reliability parameters in distributed systems applications. The proposed strategy seeks a program with the best service and a high degree of competence. Although this method does not provide an exact solution, the particle swarm optimization algorithm reaches a result close to the best solution and reduces the time required to adjust the parameters of distributed systems applications. The results of the work have been compared with the genetic algorithm and it has been shown that the PSO algorithm has a shorter response time than both the genetic algorithm and the PSO. Also, the PSO algorithm shows strong stability and ensures that the solution obtained from the proposed approach will be close to the optimal solution.

关键词： Reliability cloud computing chaotic particle swarm optimization algorithm distributed systems

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Intelligent Analysis and Processing System of Financial Big Data Based on chaotic particle swarm optimization algorithm 22

Intelligent Analysis and Processing System of Financial Big ...

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Proceedings of the 2022 International Conference on Big Data, IoT, and Cloud Computing

作者： Ying Zhang Jiangxi Vocational Technical College of industry & trade China

ISBN: (纸本)9781450399548

Abstract. With the rapid development of information management means in domestic large and medium-sized enterprises, information technology has become an important support and means to improve the level of enterprise management. At the same time, standardizing the management of information infrastructure has become a more and more prominent problem. This paper summarizes the chaotic particle swarm optimization algorithm and financial sharing services, discusses the construction of particle swarm optimization algorithm and the development of intelligent finance, analyzes the implementation of the blueprint of accounting information decision-making system, and studies the regional fiscal expenditure of China, the United States and the United States, Japan and Russia. The results show that China's financial expenditure ranks first in the whole company.

关键词： Basic particle swarm optimization algorithm Big Data Intelligent Analysis and Processing System chaotic particle swarm optimization algorithm Finance

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Validation study of Yunnan ethnic culture industry stock model under epidemic situation based on chaotic particle swarm optimization neural networks

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第1期24卷 105-120页

作者： Jiang, Jiaying Wang, Zhixiu Tao, Sha Tan, Xinyi He, Ying Pan, Wenchao Hunan Univ Sci & Technol Sch Econ & Management Yongzhou 425000 Hunan Peoples R China

The new crown pneumonia epidemic is raging, in the context of global integration, the scope of the impact of this sudden event spread around the world, the stock market has not been spared, the financial risk has increased dramatically compared with the past, the emergence of the epidemic has led to the spread of investor panic, March 2020, the U.S. S&P 500 index appeared in the four plunge, and led to the market trading meltdown, the world's financial markets have had an extremely serious impact. The study of the impact of Xin Guan Pneumonia on the company's stock returns is not only conducive to enriching the theoretical study of public health emergencies, but also conducive to improving the coping strategy, stabilizing the general economic market, and enhancing the public's awareness of risk response. This paper compares the effect of the four intelligent algorithms of chaotic particle swarm algorithm, chaotic bee colony algorithm, chaotic fruit fly algorithm and chaotic ant colony algorithm combined with neural network on the prediction of the stock price trend of Yunnan national culture, and the study shows that the speed of convergence of the chaotic particle swarm optimization neural network and the speed of descent is better than that of the two models of chaotic fruit fly and chaotic bee colony, and the coefficients of decision of the chaotic particle swarm optimization neural network are higher than that of the other three models, and the errors are lower than the other three models. Indexes are lower than the other three models and have high accuracy in stock prediction of Yunnan ethnic culture, this finding emphasizes the potential of PSO-BP model to provide robust stock market prediction, which is important for both investors and policy makers in dealing with volatile market conditions.

关键词： chaotic particle swarm optimization algorithm BP neural network ethnic culture of yunnan stock prediction

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Study on chaotic particle swarm optimization algorithm in solution of logistics scheduling problem

Study on Chaotic Particle Swarm Optimization Algorithm in so...

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International Forum on Materials Science and Industrial Technology (IFMSIT 2013)

作者： Chen, Yuqiang Peng, Naxin DongGuan Polytech Dept Comp Engn Dongguan Guangdong Peoples R China Guangdong Univ Sci & Technol Dept Comp Guangdong Peoples R China

ISBN: (纸本)9783037858417

Study the basic theory and process of chaos particle swarm optimization (PSO) algorithm, improve the basic PSO algorithm by introducing the self-adaptive inertia weighting factor method. Construct the mathematical model of basic logistics scheduling to complete the simulation analysis experiments. Experiment results show that self-adaptive chaos particle swarm optimization algorithm is effective and feasible to solve the logistics scheduling model problem.

关键词： Vehicle Routing Problem chaotic particle swarm optimization algorithm Related Vehicle Routing Problem self-adaption

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Research on Image Segmentation optimization algorithm based on chaotic particle swarm optimization and Fuzzy Clustering 2018

Research on Image Segmentation Optimization Algorithm based ...

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7th International Conference on Software and Computer Applications (ICSCA)

作者： Tan Linglong Chen Yehui Li Changkai Anhui Xinhua Univ Elect Commun Engn Coll Hefei Anhui Peoples R China

ISBN: (纸本)9781450354141

Fuzzy C-Means clustering, FCM, is an unsupervised learning algorithm. The algorithm is easily affected by noise points and depends on the initial values. When the sample value is large, the algorithm is easy to fall into local extremum. In this study, the traditional fuzzy clustering algorithm is improved, and the particle swarm optimization algorithm with global optimization ability is applied to the FCM algorithm, and chaotic technology is added. chaotic variables produce a chaotic sequence based on the current global optimal position, using chaotic sequence has the best fitness value of particles randomly instead of a particle of the particle swarm, the improved algorithm can effectively avoid the stagnation of particles in the iteration, fast search to the global optimal solution, avoid convergence to local extremum. Experimental results indicate that this algorithm overcomes the dependence on the initial clustering centre of FCM, which brings high robustness and segmentation accuracy, and has more faster convergence speed.

关键词： FCM particle swarm optimization algorithm chaotic particle swarm optimization algorithm image segmentation

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Multi-energy coordinated and flexible operation optimization and revenue reallocation models for integrated micro energy system considering seasonal and daily load characteristics of different buildings

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ENERGY REPORTS 2022年 8卷 12583-12597页

作者： Lin, Hongyu Yan, Qingyou Li, Xueting Dang, Jialu Yang, Shenbo Gejirifu, De Yao, Lujin Wang, Yao North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab New Energy Low Carbon Dev Beijing 102206 Peoples R China Beijing Univ Technol Coll Econ & Management Beijing 100124 Peoples R China State Grid Econ & Technol Res Inst Co LTD Beijing 102209 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

For pursuing economic and environmental-friendly goals, this paper constructs a regional integrated micro energy system where electricity, heat, cooling, and gas energies are interacted and integrated among buildings. First, independent and collaborative operations of micro energy systems are proposed. Second, seasonal and daily power, heat, and cooling loads of a residential building, a business building, and a mall are depicted. Third, in order to minimize the operation cost, energy consumption and CO2 emission, a multi-energy coordinated flexible operation optimization model of integrated micro energy system is established, and the chaotic particle swarm optimization algorithm is applied to solve the optimization model. A benefit evaluation model is built based on perspectives of energy, economy, and environment to evaluate the optimization results. Then, Shapley method is improved by integrating it with cloud focus theory to more fairly allocate optimized benefits, and influencing indexes for adjusting the weights of participants are proposed. Finally, a case study is conducted. The results indicate that: (1) the integrated micro energy system enabled surplus energy inter-supply among the subsystems, thus realizing interconnection and energy complementarity between micro energy systems;(2) seasonal load characteristics showed the differences among the building subsystems, where the winter load showed more flexibility in energy conversion and inter-supply among the subsystems than the summer load;(3) the improved Shapley method are more effective and fairer for benefit allocation, based on different level of importance of participants;(4) chaotic particle swarm optimization algorithm are more superior in terms of calculation efficiency and accuracy for optimization solutions. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Micro energy system cluster Multi-energy coordinated and flexible operation optimization Benefit allocation 3Es theory-based evaluation Cloud focus theory-based Shapley chaotic particle swarm optimization algorithm

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Digital Power Network Fault Diagnosis Method Based on Improved RBF-BP Neural Network 5

Digital Power Network Fault Diagnosis Method Based on Improv...

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5th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Zhang, Xingfu Xu, Yuewei Zhang, Xing Yang, Xiuyu Mao, Xiaojun Beijing Zhongdian Puhua Informat Technol Co Ltd Cloud Technol Business Dept Beijing Peoples R China Xijiao Liverpool Univ Int Business Sch Suzhou Peoples R China State Grid Chongqing Elect Power Co Informat Commun Branch Gen Management Dept Chongqing Peoples R China State Grid Chongqing Elect Power Co Chengxi Substat Perat & Maintenance Ctr EHV Branch Chongqing Peoples R China State Grid Chongqing Elect Power Co Operat & Maintenance Dept EHV Branch Chongqing Peoples R China

ISBN: (纸本)9781665490542

Aiming at the problems of complex topology and less fault samples in digital power grid, a fault diagnosis method based on improved radial basis function and back propagation (RBF-BP) neural network is proposed. Firstly, build a digital power grid architecture, and realize the functions of each application of the system through the collection and analysis of the data of the physical equipment of the power grid. Then, the RBF-BP neural network is improved by using the chaotic particle swarm optimization algorithm, and the grid data is input into the optimal network model to complete the accurate diagnosis of fault types. Finally, based on the small current grounding fault simulation platform, the proposed method is demonstrated by experiments. The results show that the diagnostic accuracy and diagnostic time are 95.69% and 197 ms respectively, which can efficiently complete the fault diagnosis of digital power grid.

关键词： digital power grid fault diagnosis chaotic particle swarm optimization algorithm RBF-BP neural network grid physical equipment simulation platform for small current grounding fault

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Research on Image Segmentation optimization algorithm based on chaotic particle swarm optimization and Fuzzy Clustering

Research on Image Segmentation Optimization Algorithm based ...

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2018年第七届软件与计算机应用国际会议

作者： Tan Linglong Chen Yehui Li Changkai Electronic communication engineering college Anhui Xinhua University

关键词： FCM particle swarm optimization algorithm chaotic particle swarm optimization algorithm image segmentation

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Data-based variable universe adaptive fuzzy controller with self-tuning parameters

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APPLIED SOFT COMPUTING 2022年 123卷

作者： Jin, Yali Cao, Weihua Wu, Min Yuan, Yan China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China

Data-based variable universe adaptive fuzzy controllers (VUAFCs) with self-tuning parameters are developed for complex systems with unknown universes in this paper. The main feature of the proposed VUAFC is the new defined contraction-expansion (C-E) factor on an infinite universe, which is more flexible and practical in real applications. Moreover, the data-based methods to tune the parameters including the peak points of output fuzzy subsets and the C-E factor parameters are proposed. The peak points of the output fuzzy subsets are mined by an improved Wang-Mendel method based on conflicting rules. The parameters of the VUAFC are optimized by solving an offline optimization problem using the chaotic particle swarm optimization (CPSO) algorithm. The simulation results on the strip temperature control of the radiant-tube indirect-fired furnace of annealing furnace show that our proposed method has strong practicability and good control performance. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Variable universe adaptive fuzzy control Contraction-expansion factor Wang-Mendel method Annealing furnace chaotic particle swarm optimization algorithm

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Optimal parameters estimation and input subset for grey model based on chaotic particle swarm optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第7期38卷 8151-8158页

作者： Wang, Jianzhou Zhu, Suling Zhao, Weigang Zhu, Wenjin Lanzhou Univ Sch Math & Stat Lanzhou 730000 Peoples R China

Optimum prediction is a difficult problem, because there are no optimal models for all forecasting problems. In this paper, the authors attempt to find the high precision prediction for grey forecasting model (GM). Considering that chaotic particle swarm optimization algorithm (CPSO) will not get into local optimum and is easy to implement, the paper develops an approach for grey forecasting model, which is particularly suitable for small sample forecasting, based on chaotic particle swarm optimization and optimal input subset which is a new concept. The input subset of traditional time series consists of the whole original data, but the whole original does not always reflect the internal regularity of time series, so the new optimal subset method is proposed to better reflect the internal characters of time series and improve the prediction precision. The numerical simulation result of financial revenue demonstrates that developed algorithm provides very remarkable results compared to traditional grey forecasting model for small dataset forecasting. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Optimal input subset chaotic particle swarm optimization algorithm Grey forecasting model Optimal parameters estimation

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