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7th World Congress on Intelligent Control and Automation

作者： Xia, Kewen Zhang, Ling Wu, Pinghui Zhang, Xinying Hebei Univ Technol Sch Informat Engn Tianjin 300401 Peoples R China

ISBN: (纸本)9781424421138

Attribute reduction is one of the important topics in the research on rough set theory. In confrontation with dynamic data, common methods of attributes reduction have such disadvantages as unstable reduction results, intensive computation and the difficulty in meeting the need of real-time processing. To solve these problems, a method of dynamic attributes reduction with improved PSO algorithm is proposed based on the research of particle swarm optimization. The concrete work is the following: firstly, the traditional PSO algorithm is improved to enhance the global search ability, which increases the diversity of particle population distribution. Secondly, the information decision system data extraction is divided into some subdecision tables, which are reducted by used of improved PSO algorithm. Finally, each reduction results are intersected and get the most minimal reduction. Simulation and experimental results show the dynamic reduction algorithm can overcome the shortcomings of common attribute reduction which possesses the significant effect and rapid computation.

关键词： Rough Set Dynamic Attributes Reduction particle swarm optimization algorithm

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Optimum Design of PI Controller for Brushless DC Motor Based on PSO-CS algorithm 22

Optimum Design of PI Controller for Brushless DC Motor Based...

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

作者： Silin Cui Xing Liu Shoushan Jiang Wei Wu School of Mechanical Engineering Northwestern Polytechnical University China School of Electronic Information Engineering Xi'an Technological University China

ISBN: (纸本)9781450399548

Aiming at the problems of slow response and low accuracy of traditional brushless DC motor (BLDCM) speed control system, the control strategy for the BLDCM based on PSO-CS fusion optimization algorithm to optimize the parameters of motor PI controller is proposed. Firstly, the BLDCM's double closed-loop control system mathematical model is established. Secondly, based on PSO-CS fusion optimization algorithm, a PI controller parameter optimization method is designed to determine the optimal parameters for motor speed control. Finally, the BLDCM control system model is built in MATLAB / Simulink. The current loop adopts traditional PI control, and the speed loop is controlled by traditional PI, PSO PI and PSO-CS PI respectively. The operation of the motor under different working conditions is simulated. The research shows that compared with the basic PSO algorithm, the PSO-CS fusion optimization algorithm has higher computational accuracy, and the parameter values obtained by using PSO-CS algorithm to optimize PI controller are better. At the same time, the PSO-CS PI controller has a better effect on BLDCM speed control. Also, it has low overshoot and short adjusting time. It shows that the proposed control strategy can make the BLDCM system has good robustness and stability.

关键词： BLDCM Cuckoo search algorithm PI control particle swarm optimization algorithm

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Non-uniformly spaced linear antenna array design by means of PEEC approach applying Cheetah optimization algorithm

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2019年第1_SUPPL期60卷 S15-S24页

作者： Moreno, Sinvaldo R. Klein, Carlos E. Mariani, Viviana C. Coelho, Leandro dos S. Alotto, Piergiorgio Torchio, Riccardo Bauernfeind, Thomas Baumgartner, Paul Univ Fed Parana Dept Elect Engn Curitiba Parana Brazil Pontificia Univ Catolica Parana Ind Syst Engn Grad Program Curitiba Parana Brazil Pontificia Univ Catolica Parana Dept Mech Engn Curitiba Parana Brazil Univ Padua Dipartimento Ingn Ind Padua Italy Graz Univ Technol Inst Fundamentals & Theory Elect Engn Graz Austria

This paper presents the application of the Partial Element Equivalent Circuit (PEEC) approach, which is a full wave electromagnetic modelling technique for conductors embedded in arbitrary dielectrics based on equivalent circuits, to the optimal design of antennas with non-uniform spacing between the array elements. The design optimization problem is solved by means of the new nature-inspired Cheetah metaheuristic. The main aim of this paper is to introduce the Cheetah optimization algorithm to the electromagnetics and antenna community. The results are compared to two well-known optimization algorithms and to show the effectiveness of the proposed algorithm on a realistic benchmark problem.

关键词： Antenna array Cheetah optimization algorithm genetic algorithm partial element equivalent circuit particle swarm optimization algorithm

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Design optimization of Truss Structures Using a Graph Neural Network-Based Surrogate Model

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algorithmS 2023年第8期16卷 380-380页

作者： Nourian, Navid El-Badry, Mamdouh Jamshidi, Maziar Univ Calgary Dept Civil Engn Calgary AB T2N 1N4 Canada

One of the primary objectives of truss structure design optimization is to minimize the total weight by determining the optimal sizes of the truss members while ensuring structural stability and integrity against external loads. Trusses consist of pin joints connected by straight members, analogous to vertices and edges in a mathematical graph. This characteristic motivates the idea of representing truss joints and members as graph vertices and edges. In this study, a Graph Neural Network (GNN) is employed to exploit the benefits of graph representation and develop a GNN-based surrogate model integrated with a particle swarm optimization (PSO) algorithm to approximate nodal displacements of trusses during the design optimization process. This approach enables the determination of the optimal cross-sectional areas of the truss members with fewer finite element model (FEM) analyses. The validity and effectiveness of the GNN-based optimization technique are assessed by comparing its results with those of a conventional FEM-based design optimization of three truss structures: a 10-bar planar truss, a 72-bar space truss, and a 200-bar planar truss. The results demonstrate the superiority of the GNN-based optimization, which can achieve the optimal solutions without violating constraints and at a faster rate, particularly for complex truss structures like the 200-bar planar truss problem.

关键词： artificial neural network design optimization graph neural network particle swarm optimization algorithm size optimization surrogate model truss structures

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The optimization of Partner Selection Based on Multi-Agent

The Optimization of Partner Selection Based on Multi-Agent

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22nd Chinese Control and Decision 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)9781424451821

The application of multi-agent technology to partner selection problem of virtual enterprise in Internet is studied. The partner selection model is constructed based on multi-agent technology. In this construction, each management function, resource and sub-task is defined as an agent. The communication, cooperation and negotiation among agents are analyzed. Immune particle swarm optimization (PSO) algorithm is proposed and applied to the optimization of partner selection and task scheduling agent. The general requirements of partner selection model of virtual enterprise based on multi-agent structure are satisfied.

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

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Iterative PSO algorithms for GRAP Problems

Iterative PSO Algorithms for GRAP Problems

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2019 International Conference on Artificial Intelligence, Control and Automation Engineering(AICAE 2019)

作者： Fang LU Dong-kui LI Faculty of Mathematics Science School Baotou Teachers College Editorial Department of Journal Baotou Teachers College

This paper studies the grap problems of two-state, in which the subsystem allows components to be mixed(i.e. the subsystem selects components from several types of heterogeneous components, and the number of selected components types >=1). Each component has a fixed reliability, weight and price, and determines the number of selected components, so that the system has the greatest reliability under the given cost and weight constraints. The coding method of the solution is that the number of elements of each type of subsystem is a variable, and the whole system is arranged in the order of subsystems to form row vectors. An iterative particle swarm optimization algorithm with fixed compression coefficient and dynamic inertia weight is constructed to solve the problem. Typical improved fyffe problems are tested, and the optimal solutions are obtained, which are consistent with the results given by the substitution constraint method. The pso algorithm presented in this paper can effectively solve the grap problem which is allowed to mix components in subsystems.

关键词： Mixed components The reliability redundancy allocation problem particle swarm optimization algorithm Encoding Optimal solution

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Research on optimization algorithm for MFJSSP

Research on Optimization Algorithm for MFJSSP

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The International Conference on Recent Trends in Materials and Mechanical Engineering (ICRTMME 2011)

作者： Li Li Wang Keqi Yue Qi Information and Computer Engineering College Northeast Forestry University ICECNEFU Forestry Engineering Automation Displine Northeast Forestry University FEADNEFU

A method combined ant colony algorithm with particle swarm optimization algorithm was designed for solving multi-objective flexible job shop scheduling problem in this *** the combined algorithm the start position of ants was marked by particles optimum position obtained by particle swarm optimization *** the traditional ant colony algorithm was improved and was used to search the global optimum *** combined algorithm was validated by practical *** results obtained have shown the proposed approach is feasible and effective for the multi-objective flexible job shop scheduling problem.

关键词： multi-objective flexible job shop scheduling particle swarm optimization algorithm ant colony algorithm

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Economic and environmental optimal operation of isolated multi-element complementary microgrid based on PSO with adaptive mutation

Economic and environmental optimal operation of isolated mul...

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2015 3rd International Conference on Machinery,Materials and Information Technology Applications(ICMMITA 2015)

作者： Zhu Weiping Yan Shuchang Wang Yulong Li Peng Han Pengfei Jiangsu Electric Power Company Research Institute State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University

Standalone multi-element complementary microgrid plays a very important role in solving the electricity problems of many areas, which are rich in renewable energy but have problems in the power supply of traditional power grid. To ensure the reliability of power supply while improving overall economic and environmental operation in micro-grid system, we have to optimize the operation of the system according to actual conditions of the system. This paper considered operating costs of the system and gas pollution emissions, established an optimization model. And the dispatch strategies are designed. Finally, utilized adaptive mutation particle swarm optimization algorithm to solve operational problems. Specific case study results verified the reasonableness and effectiveness of the algorithm.

关键词： isolated multi-element complementary microgrid economic and environmental operation particle swarm optimization algorithm adaptive mutation

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Research on Path optimization with PSO for Unmanned Vehicle

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INTERNATIONAL JOURNAL OF ONLINE ENGINEERING 2015年第8期11卷 21-24页

作者： Cai, Li Jia, Jianping Lei, Juan Wuhan Donghu Univ Wuhan 430070 Peoples R China

The development of autonomous unmanned vehicles is of high interest to many organizations around the world and path planning is the key point of the navigation for the autonomous unmanned vehicle. Intelligent algorithms have been applied in this field and an essential aspect of unmanned vehicles autonomy is the ability for automatic path planning. In this paper, particle swarm optimization algorithm as one of new swarm intelligent optimization methods is introduced into a path planning for autonomous vehicle, which is constructed of a particle representation methods for vehicle routing problem with fast convergence speed. The results show that the particle swarm optimization algorithm can obtain the solution of the vehicle routing problem quickly and effectively. It is a good method for solving the vehicle routing problem.

关键词： Path optimization particle swarm optimization algorithm Wheeled vehicle

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Cooperative Evolutionary Neural Network and Its Application

Cooperative Evolutionary Neural Network and Its Application

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22nd Chinese Control and Decision 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)9781424451821

On the basis of analyzing the particle swarm optimization (PSO) algorithm, a cooperative evolutionary algorithm (SAPSO) based on PSO and simulated annealing (SA) algorithm is proposed. It can validly overcome the premature problem in PSO through cooperative search between PSO and SA. Then, SAPSO is employed to train artificial neural network and applied to soft-sensing of melt-index of High Pressure Low-Density Polyethylene yield. The simulation results demonstrate that the model has effective generalization performance, higher precision and engineering practicability.

关键词： particle swarm optimization algorithm simulated annealing cooperative search soft-sensor

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