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Eigenvalue spread criteria in the particle swarm optimization algorithm for solving of constraint parametric problems

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APPLIED MATHEMATICS AND COMPUTATION 2007年第1期192卷 40-50页

作者： Yazdi, Hadi Sadoghi Effati, Sohrab Tarbait Moallem Univ Sabzevar Dept Engn Sabzevar Iran Tarbait Moallem Univ Sabzevar Dept Math Sabzevar Iran

This paper presents an alternative and efficient method for solving a class of constraint parametric optimization problems using particle swarm optimization algorithm (pso). In this paper, for the first time pso is used for solving convex parametric programming, but pso must be adaptive for doing it. So, for obtaining particles velocities, adaptation weight and velocity boundaries in the updating of velocities are calculated in the recursive form. Computational complexity of the pso algorithm is decreased based on uniformity of population of particles, the uniformity of which is obtained using eigenvalue spread of covariance of population. Two simple examples are provided for showing the efficiency of the proposed method. For solving these examples, eigenvalue spread criteria applied in the pso algorithm decrease 78% of computations. (c) 2007 Elsevier Inc. All rights reserved.

关键词： eigenvalue spread pso algorithm constraint parametric programming

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Multi-period semi-variance portfolio selection: Model and numerical solution

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APPLIED MATHEMATICS AND COMPUTATION 2007年第1期194卷 128-134页

作者： Yan, Wei Miao, Rong Li, Shurong China Univ Petr Coll Informat & Control Engn Dongying Shandong 257061 Peoples R China

Variance is substituted by semi-variance in Markowitz's portfolio selection model. Moreover, one period portfolio selection is extended to multi-period. In this paper, a class of multi-period semi-variance model is formulated originally. Besides, a hybrid genetic algorithm (GA), which makes use of the position displacement strategy of the particle swarm optimizer (pso) as a mutation operation, is applied to solve the multi-period semi-variance model. For this class of portfolio model, numerical results show that the hybrid GA with pso is effective and feasible. (c) 2007 Elsevier Inc. All rights reserved.

关键词： semi-variance multi-period portfolio selection efficient frontier pso algorithm

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Infrared image segmentation with 2D OTSU method based on particle swarm optimization

Infrared image segmentation with 2D OTSU method based on par...

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5th International Symposium on Multispectral Image Processing and Pattern Recognition

作者： Zhao, Guangzhou Zhu, Guangxi Zeng, Yurong Zhang, Tianxu Xu, Huazhong Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Dept Elect & Informat Wuhan 430074 Peoples R China Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China Hubei Univ Econ Sch Comp Wuhan 430205 Peoples R China Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469519

This paper proposes a global threshold selection method to do infrared image segmentation, which uses both gray-level distribution and spatial information, namely, two-dimensional OTSU method (2D OTSU). It often gets better anti-noise performance. What's more, taking consideration of the complexity of its computation, we introduce a new heuristic optimization algorithm, called the particle swarm optimization (pso) algorithm to search the result. So an algorithm for pso-based 2D Otsu segmentation is proposed. The experiments of segmentation the infrared images are illustrated to show that the proposed method can get ideal segmentation result with less computation cost.

关键词： infrared image segmentation 2D OTSU pso algorithm threshold technique real time processing

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Multiobjective evolutionary optimization design of vehicle magnetorheological fluid damper

Multiobjective evolutionary optimization design of vehicle m...

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International Conference on Smart Materials and Nanotechnology in Engineering

作者： Zhao, Qiang Wang, Yang Gao, Fang Northeast Forestry Univ Sch Traff & Transportat Engn 26 Hexing Rd Harbin 150040 HLJ Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China

ISBN: (纸本)9780819465368

Structure design and parameters selection are crucial steps in developing magnetorheological fluid (MRF) damper for vehicle semi-active suspension system. Most traditional methods for deciding structure parameters by experiential expressions are unilateral and imprecise. In this paper, a multiobjective evolutionary optimization approach will be used to solve the optimization design problem. Based on Bingham fluid models, a structure design for MRF damper with shearing valve mode is completed for vehicle suspension. To reduce the dynamic response time and to enlarge the range the controllable damping force are taken as the optimization objectives. Three crucial parameters, including gap width, effective axial pole length and coil turns number are taken as optimization variables, a hybrid evolutionary algorithm combining particle swarm optimization (pso) and crossover is employed to search for the Pareto solutions, According to the optimized results, a new type MRF damper design is accomplished for a pickup truck suspension system. The proposed method and analysis present a beneficial reference for MRF damper design.

关键词： vehicle damper magnetorheological fluid evolutionary optimization pso algorithm crossover

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Annual Electricity Demand Prediction for Iranian Agriculture Sector Using ANN and pso

Annual Electricity Demand Prediction for Iranian Agriculture...

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2007 IEEE Canada Electrical Power Conference (EPC 2008)

作者： Seyyed Ali Pourmousavi Kani Nima Farrokhzad Ershad Department of Electrical Engineering Amirkabir University of Technology슠 Tehran Iran

In this study, we used pso algorithm and ANN to predict annual electricity consumption in Iranian agriculture sector. The economic indicators used in this paper are price, value added, number of customers and consumption in the previous periods. To predict the future values, a linear-logarithmic model of electrical energy demand is considered. The pso algorithm applied in this study has been tuned for all its parameters and the best coefficients with minimum error are identified, while all parameter values are tested concurrently. Consumption in the previous periods has been used for testing estimated model. The estimation errors of pso algorithm are less than that of estimated by genetic algorithm and regression method. In addition, ANN is used to forecast each independent variable and then electricity consumption is forecasted up to year 2010. Electricity consumption in Iranian agriculture sector from 1981 to 2005 is considered as the case for this study.

关键词： Artificial Neural Networks Electricity demand Linear-logarithmic model Prediction pso algorithm

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Optimization on a period of flying orbit of hypersonic vehicle

Shiyou Huagong Gaodeng Xuexiao Xuebao/Journal of Petrochemic...

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Shiyou Huagong Gaodeng Xuexiao Xuebao/Journal of Petrochemical Universities 2007年第3期20卷 37-40页

作者： Wu, Di Hu, Peng Lin, Xiao-Xu Yao, Zong-Xin Department of Computer Science and Technology Dalian University of Technology Dalian 116024 China Shenyang Aircraft Design and Research Institute Shenyang 110035 China

The magnitude of the original quality and original angle of hypersonic vehicle can influence flying orbit and other design-parameters greatly. Firstly, according to flying process, the flying orbit was divided into six states analyzed by three models. Then a modified particle swarm optimization (pso) algorithm was used to test the relationship among original quality, original angle and level distance. Referring to the test result, optimization research on a period of flying orbit of hypersonic vehicle was performed. Finally, the method has been successfully used and validated its rationality and validity by testing and analyzing the example.

关键词： Flying orbit Optimization Hypersonic vehicle pso algorithm

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Research on DO intelligent control system in fermentation

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26th Chinese Control Conference

作者： Zhu Xiaoliu Jiang Dingguo Zhang Yulin Xu Baoguo So Yan Univ Sch Commun & Control Engn Wuxi 214122 Peoples R China

ISBN: (纸本)9787811240559

The parameter of Dissolved Oxygen is one of great importance in the process of microbe fermentation. A normal PID is difficult to solve the nonlinear, time-delay characteristics. A intelligent PID controller is designed based on dynamic inertia factor pso, which can adaptively adjust the parameters of PID and is used in the control of DO. Compared with normal PID controller, the new controller is of small overshoot and quick response, improved stability of the system and increase the yield of products.

关键词： dissolved qxygen PID dynamical interact weight pso algorithm

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A New RBF Neural Network Training algorithm Based on pso

A New RBF Neural Network Training Algorithm Based on PSO

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2006年国际电子、工程及科学领域的分布式计算和应用学术研讨会

作者： Dingxue Zhang,Xinzhi Liu,Zhihong Guan Control science and technology Department,Huazhong University of Science & Technology Wuhan,Hubei,430074,China

Based on the study of Radial Basis Function(RBF) neural network training algorithm and Particle Swarm Optimization(pso) algorithm,a new RBF neural network training algorithm with modified pso algorithm is formulated,in which a control gene is introduced into basis pso *** algorithm can determine network structure and parameters,such as centers and widths of hidden units by combining with least square *** new training algorithm is applied to the nonlinear system identification problem,comparing with hierachical genetic algorithm and orthogonal least squares algorithm(OLS),the simulation results illustrate its efficiency.

关键词： RBF neural network pso algorithm least square method nonlinear system identification

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Application of a microhabitat particle swarm algorithm in transformer substation optimization

Application of a microhabitat particle swarm algorithm in tr...

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International Conference on Power Systems Technology (POWERCON)

作者： Wu, Fangjie Zhang, Chengxue Zhang, JingChao Duan, Zhiyuan Wuhan Univ Sch Elect Engn Luojia Hill Wuhan 430072 Peoples R China

ISBN: (纸本)9781424401109

This paper provides a algorithm based on the microhabitat theory and particle swarm for the problem of Transformer Substation Optimiziation. This method can locate the substation according to muti-objects model of the problem, give the best scheme in a certain load level, and reduce the difficulties and works. In the end of paper, a real project example is showed to prove the validity and feasibility.

关键词： distribution network optimization pso algorithm microhabitat

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Drilling path optimization based on swarm intelligent algorithm

Drilling path optimization based on swarm intelligent algori...

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IEEE International Conference on Robotics and Biomimetics (ROBIO 2006)

作者： Zhu, Guang Yu Fuzhou Univ Coll Mech Engn & Automat Fuzhou Fujian Province Peoples R China

ISBN: (纸本)9781424405701

Drilling path optimization is the key problem in holes machining. This paper presents a swarm intelligent approach based on the particle swarm optimization (pso) algorithm for solving the drilling path optimization problem. Because the standard pso algorithm is not guaranteed to be global convergence or local convergence, the algorithm is improved by adopting the method of generating the stop evolution particle over again to get the ability of convergence on the global optimization solution. And the operators are improved by establishing the order exchange unit and the order exchange list to satisfy the need of integer coding in drilling path optimization. The experimentations indicate that the improved algorithm has the characteristics of easy realization, fast convergence speed, and better global converging capability. Hence the new pso can play a role in solving the problem of drilling path optimization.

关键词： driling path optimization swarm intelligent algorithm pso algorithm convergence

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