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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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Reliability in Cloud Computing Applications with chaotic particle swarm optimization algorithm

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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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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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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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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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Predictive Control algorithm based on Type-2 T-S fuzzy Model and chaotic particle swarm optimization Algorithem

Predictive Control Algorithm based on Type-2 T-S fuzzy Model...

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31st Chinese Control Conference

作者： Zhang Enkui Su Baili Zhang Xianxia Qufu Normal Univ Sch Elect Informat & Automat Rizhao 276826 Peoples R China Shanghai Univ Sch Mechatron & Automat Shanghai 200072 Peoples R China

ISBN: (纸本)9789881563811

In this paper, a predictive control algorithm is presented based on Type-2 fuzzy model and chaotic particle swarm optimization (CPSO) algorithm: using the Type-2 T-S fuzzy model as predictive model, and using CPSO algorithm to solve the optimization index, so the predictive control can avoid complex gradient calculation and matrix inversion, and can quickly to search the optimal solution. Furthermore, in order to improve the effectiveness of Type-2 model, the modified G-K clustering method is applied to determine the clustering number c and clustering centers. For a numerical example, the simulation results show the effectiveness of the algorithm proposed in this paper.

关键词： Predictive control chaotic particle swarm optimization algorithm Nonlinear system Type-2 T-S fuzzy model G-K clustering method

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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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Study on Improved chaotic particle swarm optimization Based Logistics and Transport Scheduling Problems with Soft Time Windows

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2015年第3期8卷 219-226页

作者： Chen, Yuqiang Guo, Jianlan Dong Guan Polytech Dept Comp Engn Dongguan Guangdong Peoples R China

Research the Incident Vehicle Routing Problem with Soft Time-windows (IVRP-STW). Associated logistics scheduling problem with soft time windows using the logistic function optimization method to improve the chaotic particle algorithm, compared with the GA and the standard PSO algorithm. Simulation results show that such optimization method can effectively improve the global search of the particles in the particle swarm and the ability of local search is effective in resolving such problems.

关键词： chaotic particle swarm optimization algorithm Soft time windows Scheduling problem

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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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Application of chaotic particle swarm optimization in the Short-term Electricity Price Forecasting

Application of Chaotic Particle Swarm Optimization in the Sh...

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23rd Chinese Control and Decision Conference

作者： Zhang, Jianhua Yu, Changhai Hou, Guolian North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

ISBN: (纸本)9781424487363

The market-oriented reform of the electric power industry is a trend around the world, electricity price issues are the key problems in the power markets and how to price the special commodity-electricity is essential for the smooth market operation. Accurate price forecasting provides crucial information for electricity market participants to make reasonable competing strategies, which is related to the position and benefit of the market participators. So using the relative historic data in predicting the future electricity price is a very meaningful work. With comprehensive considerations of the fluctuation rules and the various influencing factors on the forming of price in the power market, a short-term electricity price forecasting method based on the time series ARMAX model was chosen in this paper. Aimed to solve the problem with traditional method of parameter identification which is easy to fall into the local least values and has low identification precision, chaotic particle swarm optimization (CPSO) algorithm was proposed in this paper. Calculation example shows that this method can reflect the law of the development of the electricity price well and improve forecasting accuracy greatly.

关键词： Short-term electricity price forecasting ARMAX model chaotic particle swarm optimization algorithm

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