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2nd IEEE International Conference on Cloud Computing and Intelligent Systems (CCIS)

作者： Yang, Zhengqiu Liu, Meiling Xiu, Jiapeng Liu, Chen Beijing Univ Posts & Telecommun Sch Software Engn Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

ISBN: (纸本)9781467318556;9781467318570

Cloud computing environment for the efficient resources allocation is an important issue in the field of cloud computing. The resources in Cloud computing application platform are distributed widely and with great diversity. User demands of real-time dynamic change are very difficult to predict accurately. The heuristic ant colony algorithm could be used to solve this kind of problems, but the algorithm has slow convergence speed and parameter selection problems. Aiming at this problem, this paper proposes an optimized ant colony algorithm based on particle swarm to solve cloud computing environment resources allocation problem.

关键词： particle swarm optimization algorithm Ant colony optimization algorithm Cloud resource allocation

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Nonlinear Inertia Weigh particle swarm optimization combines Simulated Annealing algorithm and Application in Function and SVM optimization

Nonlinear Inertia Weigh Particle Swarm Optimization combines...

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3rd International Conference on Mechanical and Electronics Engineering (ICMEE 2011)

作者： Jiao Bin Xu Zhixiang Shanghai DianJi Univ Sch Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9783037852866

This paper proposes an improved particle swarm optimization algorithm (PSO) for the global and local equilibrium problem of searching ability. It improves the iterative way of inertia weight in PSO, using non-linear decreasing algorithm to balance, then PSO combines with simulated annealing(SA). Finally, the optimization test experiments are carried out for the typical functions with the algorithm (ULWPSO-SA), and compare with the basic PSO algorithm. Simulation experiments show that local search ability of algorithm, convergence speed, stability and accuracy have been significantly improved. In addition, the novel algorithm is used in the parameter optimization of support vector machines (ULWPSOSA-SVM), and the experimental results indicate that it gets a better classification performance compared with SVM and PSO-SVM.

关键词： particle swarm optimization algorithm inertia weight simulated annealing algorithm function optimization parameter optimization

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Calculation and optimization of Electric Helicopter Flight Performance Based on particle swarm algorithm 36

Calculation and Optimization of Electric Helicopter Flight P...

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36th Chinese Control and Decision Conference (CCDC)

作者： Ji, Ming Qi, Yiwen Zhang, Qingxin Wang, Feng Zhou, Guoqing Lv, Yanbo Shenyang Aerosp Univ Sch Automat Shenyang 110136 Peoples R China Liaoning Gen Aviat Acad Shenyang 110136 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

As energy issues become increasingly prominent, the electrification of aviation aircraft has gradually become a research hotspot, and electric helicopters are also in a stage of rapid development. Based on the flight performance analysis method of fuel-powered helicopters and combined with the characteristics of electric power system, this paper proposes a calculation method for the flight performance of electric helicopters and establishes an analysis model. This model can demonstrate the performance of electric helicopters in three aspects: vertical flight, climbing flight and horizontal flight. Then, the particle swarm optimization (PSO) algorithm is used to optimize the overall flight performance of the helicopter. It can allocate weights reasonably according to design requirements and selectively optimize key performance indicators. The results show that the adopted optimization algorithm has good effect and applicability to the overall parameters optimization problem of electric helicopters.

关键词： Electric helicopters Flight performance analysis particle swarm optimization algorithm Overall parameters optimization

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2nd International Conference on Intelligent Technologies and Engineering Systems (ICITES)

作者： Shih, Chien-Jong Teng, Tso-Liang Chen, Shiau-Kai Tamkang Univ Dept Mech & Electromech Engn Danshui 25137 New Taipei Taiwan Hsiuping Univ Sci & Technol Dept Mech Engn Taichung 41280 Taiwan

ISBN: (纸本)9783319045733;9783319045726

A niche-related particle swarm meta-heuristic algorithm for dealing with multimodal optimization problem is proposed in this paper. The inspiration and numerical algorithm are presented and the Rastrigin function with numerous local optima is adopted as the illustrative example. Proposed multimodal particle swarm optimization (MPSO) is sensitive to predetermined multimodal numbers, particle numbers, niche radius, and convergent iterations. The results show that the proposed MPSO is accurate and stable. The presented MPSO is ready for applied engineering optimization and further application.

关键词： particle swarm optimization algorithm Multimodal function Niche Bio-logical based optimization

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Ascent Trajectory optimization of Hypersonic Vehicle Based on Improved particle swarm algorithm

Ascent Trajectory Optimization of Hypersonic Vehicle Based o...

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Chinese Automation Congress (CAC)

作者： Wu, Ge Liu, Lei Wang, Yongji Liu, Xing Huazhong Univ Sci & Technol Sch Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China

ISBN: (纸本)9781467371896

By introducing the adaptive inertia weight, the time factor and the structure rebuilding of particle swarm optimization (PSO), the improvements of PSO are completed. In order to improve the accuracy and convergence speed, a PSO strategy is proposed, which consists of the dynamic population structure, opposition-based learning, crossover operator and variable step integral. Combining the improvement of PSO and the optimization strategy, the modified particle swarm optimization ( MPSO) algorithm is formed. The MPSO is applied to optimize the ascent trajectory of hypersonic vehicle. The precision and efficiency of this trajectory optimization method are demonstrated by comparing the results of PSO and MPSO. The simulation results show that the performance of MPSO is significantly superior to PSO either convergence speed or convergent accuracy.

关键词： trajectory optimization particle swarm optimization algorithm variable population opposition-based learning crossover operator

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particle swarm optimization and Cuckoo Search Paralleled algorithm 3

Particle Swarm Optimization and Cuckoo Search Paralleled Alg...

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3rd IEEE International Conference on Computer and Communications (ICCC)

作者： Yang Xiaodong Cai Zefan Shunde Polytech Elect & Informat Engn Deptartment Shunde Peoples R China

ISBN: (纸本)9781509063529

particle swarm optimization algorithm and cuckoo search algorithm both are bionic swarm optimization algorithms, which are simple and convenient. They have been applied to many fields. However, the algorithms have obvious disadvantages. When they are applied to complex optimization problems, they cannot obtain the optimal solutions, so some measures must be adopted in order to improve their global search ability. In this paper, particle swarm optimization algorithm and cuckoo search algorithm evolve in parallel. At the end of each generation, the better solution of the two algorithms is selected as the global optimal solution. The simulation results show that the paralleled algorithm absorbs the advantages of the two algorithms, improves the global search ability and the average convergence speed, and enhances the robustness of the algorithm. The new algorithm is able to solve complex optimization problems more efficiently.

关键词： cuckoo search algorithm particle swarm optimization algorithm paralleled algorithm

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Forecasting S&P 500 stock prices using ARIMA Model Estimated by particle swarm optimization Diagnosis 29

Forecasting S&P 500 stock prices using ARIMA Model Estimated...

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29th International Computer Conference, Computer Society of Iran, CSICC 2025

作者： Nikoupour, Mohammad Khalili, Mohammad Amin Ali Mousavi Hessari, Seyed Department of Financial Management Faculty of Financial Management and Accounting Shahid Beheshti University Tehran Iran Department of Finance and Banking Faculty of Management and Accounting Allameh Tabataba’i University Tehran Iran Department of Accounting Faculty of Economics Management and Administrative Sciences Semnan University Semnan Iran

ISBN: (纸本)9798331523114

In recent years, neural networks have garnered significant attention in portfolio management, particularly for forecasting stock prices, which is a challenging yet crucial task for the financial market. This study focuses on the S&P 500, a benchmark index that comprises 500 major stocks and serves as a key indicator of the U.S. economy's health. For passive investors, the S&P 500 represents an essential exposure to the U.S. market through index funds. The research addresses the need for accurate stock price predictions by investigating the effectiveness of deep learning models in this domain. In this study, we introduce an innovative approach that combines particle swarm optimization (PSO) with the Autoregressive Integrated Moving Average (ARIMA) model, creating a hybrid PSO-ARIMA framework designed to improve stock price forecasting accuracy. Through rigorous comparative analysis, the predictive capabilities of the PSO-ARIMA model are evaluated alongside popular models like GRU, LSTM, and traditional ARIMA. Results indicate that the PSO-ARIMA model outperforms these models across all evaluation metrics, including Mean Squared Error (MSE), achieving an MSE of 170.766, highlighting its superior precision and efficiency. The PSO-ARIMA model’s effectiveness stems from its ability to leverage the strengths of both ARIMA and PSO-ARIMA models are well-suited for capturing linear trends in time series data, but they often struggle with complex, nonlinear patterns common in stock price movements. By integrating PSO, a metaheuristic optimization technique, the model can optimize ARIMA’s parameters to better fit the non-linear characteristics of financial data. © 2025 IEEE.

关键词： ARIMA model Forecasting machine learning particle swarm optimization algorithm stock prices

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Cultural algorithm Based on particle swarm optimization for Partner Selection of Virtual Enterprise

Cultural Algorithm Based on Particle Swarm Optimization for ...

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31st Chinese Control Conference

作者： Zhou Wei Bu Yan-ping East China Univ Sci & Technol Sch Business Shanghai 200237 Peoples R China Shanghai Jiao Tong Univ Sch Technol Shanghai 201101 Peoples R China

ISBN: (纸本)9789881563811

A novel cultural algorithm based on particle swarm optimization (PSO) algorithm was proposed in this paper. After analyzing the partner selection problems of virtual enterprise, the CPSO algorithm was presented to solve enterprise alliance problem within reasonable time and cost. There are certain number partners of each sub-task in virtual enterprise environment. The objective is, by selecting the optimal combination of partners, to minimize project's completion time and project's total cost. We tested the CPSO algorithm against the PSO method. Simulation results demonstrate that it can be superior to the regular PSO. We also tested the CPSO algorithm with the exhaustion method to show the algorithm's efficiency.

关键词： cultural algorithm particle swarm optimization algorithm virtual enterprise partner selection

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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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optimization of 3D printing process parameters to minimize surface roughness with hybrid artificial neural network model and particle swarm algorithm

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PROGRESS IN ADDITIVE MANUFACTURING 2021年第2期6卷 199-215页

作者： Shirmohammadi, Mohammad Goushchi, Saeid Jafarzadeh Keshtiban, Peyman Mashhadi Urmia Univ Technol Fac Ind Engn Orumiyeh Iran Urmia Univ Technol Fac Mech Engn POB 57155-419 Orumiyeh Iran

Due to the significant impact on the product quality and performance, the surface roughness of produced parts by 3D printers is one of the important factors in the 3D printing process. Then, the main objective of this research is to determine the optimal composition of input parameters to minimize surface roughness, using hybrid artificial neural network and particle swarm algorithm. For this purpose, after using Central Composite Design (CCD) of experiments with five independent parameters (nozzle temperature, layers thickness, printing speed, nozzle diameter and material density) with three levels, 43 flat parts were produced with a three-dimensional printer, and roughness tests were performed on produced parts. After training experimental matrix by multilayer perceptron neural network (7-4-1) with a coefficient of 0.95, the subjected matrix was combined with the particle swarm algorithm to determine the optimal composition of input parameters. To verify results accuracy, the optimized process parameters obtained from the combined algorithm, have been tested with experimental results. In addition, to specify the effect of input parameters on the surface roughness, a quadratic model has been developed using Response Surface Method (RSM). Based on results of the hybrid algorithm, the optimal combination of input parameters was extracted. It was inferred that, the nozzle temperature of 192.20 degrees C, the layers' thickness of 100 mu m, the printing speed of 97.06 mm/s, the nozzle diameter of 0.3 mm and the internal density of 24.88% lead to the surface roughness of 11.319. Therefore, the use of this hybrid algorithm improves the surface quality of the printed parts during the 3D printing process.

关键词： 3D printing Surface roughness Artificial neural network particle swarm optimization algorithm Response surface method

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