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第11届分布式计算及其应用国际学术研讨会

作者： Xiaotang Wen Minghe Huang Jianhua Shi School of Software Jiangxi Normal University

It improves the algorithm because of the shortcoming that the ACO algorithm is easy to fall into local optimal solution in the cloud computing resource scheduling. The improved algorithm makes particle optimization inosculated into ant colony algorithm, which first finds out several groups of solutions using ACO algorithm according to the updated pheromone, and then gets more effective solutions using pso algorithm to do crossover operation and mutation operation so as to avoid the algorithm prematurely into the local optimal solution.

关键词： ACO algorithm pso algorithm Cloud Computing Resources Scheduling

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Research on strip crown by uncertain sampling strategy modified particle swarm optimization with RBF neural network

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APPLIED SOFT COMPUTING 2022年 130卷

作者： Huang, Yue Zhou, Xiaomin Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China

The strip crown directly affects the quality of strip. The prediction of strip crown by general machine learning models usually focuses on the production of a single category of strip steel, and the models lack good prediction ability for complex industrial data environments, which will lead to an increase in production costs and a decrease in product quality. Therefore, this paper proposes a new Radial Basis Function (RBF) neural network model, which is optimized by a new uncertain sampling strategy modified pso algorithm (US-Mpso algorithm). The main feature of the algorithm is to add a new clustering dimension to the particle swarm algorithm (pso). In this paper, clustering optimization is combined with uncertain sampling strategy to help particle swarm optimization iteration. On the one hand, the algorithm collects the population information of the strip crown as much as possible to help the neural network to converge. On the other hand, it adopts the uncertain sampling strategy to realize the optimal iteration of the samples. Finally, it realizes the improvement of the running speed and accuracy of the algorithm. This paper uses a specific model initialization strategy and combination strategy to combine the optimization algorithm with the RBF neural network. In this paper, four different types of data sets are tested to prove the adaptability of the model for complex industrial environments. The results show that the RBF neural network optimized by the US-Mpso algorithm has better prediction accuracy than the previous RBF neural network. The optimization effect of the algorithm is very significant.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Uncertain sampling strategy pso algorithm RBF neural network Crown prediction

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Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data

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AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING 2020年第1期34卷 114-126页

作者： Harandizadeh, Hooman Shahid Bahonar Univ Kerman Dept Civil Engn Fac Engn Pajoohesh SqImam Khomeni HighwayPOB 76169133 Kerman *** Iran

This research intends to investigate a new hybrid artificial intelligence (AI) technique compared to some common CPT methods in estimating axial ultimate pile bearing capacity (UPBC) using cone penetration test (CPT) data in geotechnical engineering applications. A data series of 108 samples was collected in order to develop a new hybrid structure of an adaptive neuro-fuzzy inference system (ANFIS) network, and the group method of the data handling (GMDH) type neural network was optimized by applying the particle swarm optimization (pso) algorithm over the hybrid ANFIS-GMDH topology, which leads to a new hybrid AI model called as ANFIS-GMDH-pso. The derived database provides information related to pile load tests, in situ field CPT data, and soil-pile information for introducing the proposed hybrid neural system. The cross-section of the pile toe, average cone tip resistance along embedded pile length, and sleeve frictional resistance along the shaft had been considered as input parameters for the proposed network. The results of this research indicated that the proposed ANFIS-GMDH-pso model predicted the UPBC with an acceptable precision compared to various CPT methods, including Schmertmann, De Kuiter & Bringen, and LPC/LPCT methods. Moreover, ANFIS-GMDH-pso network model performance was compared to CPT-based models in terms of statistical criteria in order to achieve a best fitted model. From the statistical results, it was found that the developed ANFIS-GMDH-pso model has achieved a higher accuracy level in terms of statistical indices compared to CPT-based empirical methods, such as Schmertmann model, De Kuiter & Beringen model, and Bustamante & Gianeselli for predicting driven pile ultimate bearing capacity.

关键词： ANFIS algorithm CPT-based models GMDH type neural network pile bearing capacity pso algorithm

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Research on equipment fault diagnosis method based on random stochastic adaptive particle swarm optimization

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The Journal of China Universities of Posts and Telecommunications 2020年第4期27卷 17-25,42页

作者： Yang Jianjian Zhang Qiang Wang Xiaolin Du Yibo Wang Chao Wu Miao Department of Mechanical Electrical and Information EngineeringChina University of Mining and Technology(Beijing)Beijing 100083China Tian Di Science&Technology Co. LtdBeijing 100013China

The traditional fault diagnosis method of industrial equipment has low accuracy and poor applicability. This paper proposes a equipment fault diagnosis method based on random stochastic adaptive particle swarm optimization(RSApso). The entire model is validated by using the data of healthy bearings collected by Case Western Reserve University. Different gradient descent algorithms and standard particle swarm optimization(pso) algorithms in a back propagation(BP) network are compared experimentally. The results show that the RSApso algorithm has a higher accuracy of weight threshold updating than the gradient descent algorithm and does not easily fall into a local optimum. Compared with pso, it has a faster optimization speed and higher accuracy. Finally, the RSApso algorithm is validated with the data of bearings collected from the laboratory rotating machinery test bench and motor data collected from the tower reflux pump. The average recognition rate of the four kinds of bearing data constructed is 97.5%, and the average recognition rate of the two kinds of motor data reaches 100%, which prove the universality of the method.

关键词： fault diagnosis BP network gradient descent pso algorithm

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Directional Signage Location Optimization of Subway Station Based on Big Data

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IEEE ACCESS 2020年 8卷 12354-12363页

作者： Tu, Qun Zhang, Zhenji Zhang, Qianqian Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China

The imperfection of the guide signs in the subway will lead to many difficulties for passengers, which directly affects the operation efficiency of subway stations. In this paper, we use big data to analyze the problem of signages in Beijing subway, and propose the optimization model of signages in subway stations based on particle swarm optimization algorithm. The experimental results of Dongzhimen subway station in Beijing show that the model has strong robustness in optimization, and the global best position can be found 100%.

关键词： pso algorithm signage location optimization subway station big data

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An improved ant colony optimization algorithm based on particle swarm optimization algorithm for path planning of autonomous underwater vehicle

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第8期11卷 3349-3354页

作者： Che, Gaofeng Liu, Lijun Yu, Zhen Xiamen Univ Dept Automat Xiamen Fujian Peoples R China

The motion control of autonomous underwater vehicle (AUV) has got more and more attention because AUV has been used in many applications in recent years. In order to find the optimal path for AUV to reach the specified destination in complex undersea environment, an improved ant colony optimization (ACO) algorithm based on particle swarm optimization (pso) algorithm is proposed. Due to the various constraints, such as the limited energy and limited visual distance, the improved ACO algorithm uses improved pheromone update rule and heuristic function based on pso algorithm to make AUV find the optimal path by connecting the chosen nodes of the undersea environment while avoiding the collision with the complex undersea terrain (static obstacles). The improved ACO algorithm based on pso algorithm can overcome disadvantages of the traditional ACO algorithm, such as falling into local extremum, poor quality, and low accuracy. Experiment results demonstrate that improved ACO algorithm is more effective and feasible in path planning for autonomous underwater vehicle than the traditional ant colony algorithm.

关键词： AUV Path planning ACO algorithm pso algorithm Pheromone Path point

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INFLUENCE OF MOISTURE OF WHEAT ON ACOUSTIC WAVE PROPAGATION CHARACTERISTICS

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TRANSACTIONS OF THE ASABE 2020年第5期63卷 1315-1326页

作者： Zhang, C. Dai, A. Yang, H. Wu, Z. Zhou, X. Jayas, D. S. Beijing Univ Posts & Telecommun Dept Automat Beijing Peoples R China Qingdao Agr Univ Dept Sci & Informat Qingdao Peoples R China Chinese Cereals & Oil Assoc Beijing Peoples R China Univ Manitoba Dept Biosyst Engn Winnipeg MB Canada Beijing Univ Posts & Telecommun Beijing Peoples R China

Acoustic methods have been widely used for the measurement of grain moisture content and temperature as well as for detecting the presence of insects in grain. Therefore, this study was conducted to understand the influence of wheat moisture content on acoustic wave propagation characteristics. A novel device was developed for measuring the acoustic wave propagation constant. The propagation constants of 12 samples of wheat were measured, with each sample having a different moisture content in the range from 1.1% to 21.2% (wet basis). The results showed that the acoustic wave velocity decreased slightly with an increase in moisture from 1.1% to 7.8%, while the attenuation coefficient increased slightly. When the moisture increased from 7.8% to 21.2%, the velocity increased gradually, while the attenuation coefficient decreased. A quadratic equation described the results well. In addition, a Johnson-Champoux-Allard (JCA) model was adopted to study the influence of moisture on the pore structure of wheat samples. With the increase in moisture from 1.1% to 21.2%, the average radius of interconnection between two adjacent pores decreased from 0.275 to 0.262 mm at moisture below 7.8% and then increased to 0.304 mm, and the average radius of the pores decreased from 0.773 to 0.481 mm at moisture below 7.8% and then increased to 1.2 mm.

关键词： Acoustic standing wave tube JCA model Moisture content Propagation constant pso algorithm Wheat

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Optimal optimisation-based microgrid scheduling considering impacts of unexpected forecast errors due to the uncertainty of renewable generation and loads fluctuation

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IET RENEWABLE POWER GENERATION 2020年第2期14卷 321-331页

作者： Lee, Chun-Yao Tuegeh, Maickel Chung Yuan Christian Univ Dept Elect Engn 200 Zhongbei Rd Taoyuan 320 Taiwan

Unlike conventional power plants, wind farm and solar photovoltaic (PV) module operations involved uncontrollable factors such as wind and solar irradiation, making the dispatch problem more complex. This study proposes optimal scheduling using a modification inertia weight of the particle swarm optimisation (pso) algorithm and take into account unexpected forecast errors due to uncertainty in renewable distributed generators and loads in the day-ahead market in a microgrid. Modified inertia weight makes the pso algorithm have a strong global searching ability to solve the scheduling. Meanwhile, the microgrid coordinates the realistic production of its power plant to get the optimal total cost. The uncertainty of renewable distributed generators is modelled based on forecast data. The Simulation result shows the advantages of the proposed method.

关键词： power markets scheduling particle swarm optimisation optimisation search problems wind power plants distributed power generation power generation scheduling conventional power plants renewable generation optimal optimisation-based microgrid scheduling forecast data optimal total cost power plant strong global searching ability pso algorithm modified inertia weight day-ahead market renewable distributed generators account unexpected forecast errors particle swarm optimisation algorithm modification inertia weight optimal scheduling dispatch problem solar irradiation uncontrollable factors solar photovoltaic module operations wind farm

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Optimum Design of Robust Adaptive Controller with Actuator Constraints

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2020年第11期18卷 2734-2741页

作者： Navabi, M. Mirzaei, Hamidreza Davoodi, Ali Shahid Beheshti Univ Fac New Technol Engn Gc Iran Clemson Univ Dept Mech Engn Clemson SC 29634 USA

In this paper, a direct optimal adaptive nonlinear tracking control method for a class of uncertain Multi-Input Multi-Output (MIMO) nonlinear systems with actuator amplitude constraints in presence of unknown disturbance is developed. In order to handle parametric model uncertainties and external disturbance, an adaptive controller is presented. Particle Swarm Optimization (pso) algorithm is employed to prevent constraints violation through determining the optimal adaptive controller parameters. The performance of this control approach is studied using a new index function in time-domain, and the stability analysis is conducted by Lyapunov theory. In addition, indicative robot examples are provided to illustrate how investigated method effectively stabilizes system and confines tracking error to prescribed bounds without control input saturation, particularly with large parametric uncertainties.

关键词： Control input constraint Lyapunov method optimal adaptive control pso algorithm

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Study on Improved Expert PID Controller Based on pso algorithm for Maglev Transportation System with Time Delay

Study on Improved Expert PID Controller Based on PSO Algorit...

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2014 International Conference on Mechanical Design, Manufacture and Automation Engineering(MDMAE 2014)

作者： Rong-rong SONG Zi-li CHEN Wei-hua MA College of Electrical Engineering Southwest Jiaotong University College of Computer Science and Technology Southwest University for Nationality College of Maths Southwest Jiaotong University Traction power state key laboratory Southwest Jiaotong University

Time delay is one of the most important aspects of the suspension control performance impact in maglev transportation system(MTS).By using the traditional PID control,the problem is that it is difficult to confirm the PID parameters and have long settling time,even appear the chaotic ***,using Expert PID(EPID)control method produced over-fitting to initial assignments,local optimum induced easily and slow convergence rate *** on the global optimization feature of pso algorithm,it has been adopted to optimize the initial values of Expert PID control in this *** intelligent control algorithm for the maglev transportation system was put forward based on Expert PID optimized by Particle Swarm Optimization algorithm(EPID-pso).The expert rule is that if the absolute value of the error trended to decrease,the PID current control kept its maintenance;if not,then the PID current control applied strong *** this rule,the dynamic error is reduced,and the performance of time delay is *** results by MATLAB proved that the control scheme compared with EPID control and pso-PID control has good robustness,shorter adjustment time,faster response time,achieving better quality of control.

关键词： Maglev transportation system Expert PID control pso algorithm Time delay MATLAB simulation

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