Application of improved PSO to power transmission congestion management optimization model

The parameters of particles were encoded firstly, then the constraint conditions and fitness degree were processed, and the calculation steps of the improved PSO algorithm were presented. Finally, the issues with the adoption of the improved PSO algorithm were solved and the results were analyzed. The results show that it is beneficial to obtaining the optimal solution by increasing the number of particles but that will also increase the operation time. On the aspects of solving continuous differentiable non-linear optimization model with equality and inequality constraints, the optimization result of PSO algorithm is the same as that of the interior point method. Compared with genetic algorithms (GA), PSO algorithm is more effective in the local optimization, and unlike GA, it will not be early maturity. Meanwhile, PSO algorithm is also more effective in the boundary optimization than genetic algorithm.     

Genetic algorithms in bidisciplinary (aerodynamics/electro-magnetism ) optimization

The genetic algorithm(GA) is a non-traditional, probability search and global optimization method similar to natural selection and evolution. The key points and control parameters of this method are briefly discussed. To apply it to a multiobjective and multidisciplinary optimization problem a kind of fitness function is suggested, in which the requirements of multiobjects and multiconstraints are considered and the nondimensional coefficients and panalty coefficients of the constraint function are also introduced . Numerical results of bidisciplinary optimization calculation show that the present method is effective, applicable, and robust.     

Robot stereo vision calibration method with genetic algorithm and particle swarm optimization

Accurate stereo vision calibration is a preliminary step towards highprecision visual positioning of robot. Combining with the characteristics of genetic algorithm (GA) and particle swarm optimization (PSO), a threestage calibration method based on hybrid intelligent optimization is proposed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the first stage. Then in the second stage, two cameras’ parameters are optimized separately. Finally, the integrated optimized calibration of two models is obtained in the third stage. Direct linear transformation (DLT), GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find nearoptimal solution and it can be used to initialize the next stage. Simulation analysis and actual experimental results indicate that this calibration method works more accurate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.     

An extended particle swarm optimization algorithm based on coarse-grained and fine-grained criteria and its application

In order to study the problem that particle swarm optimization （PSO） algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using the information in the iterative process of more particles was analyzed and the optimal system of particle swarm algorithm was improved. The extended particle swarm optimization algorithm （EPSO） was proposed. The coarse-grained and fine-grained criteria that can control the selection were given to ensure the convergence of the algorithm. The two criteria considered the parameter selection mechanism under the situation of random probability. By adopting MATLAB7.1, the extended particle swarm optimization algorithm was demonstrated in the resource leveling of power project scheduling. EPSO was compared with genetic algorithm （GA） and common PSO, the result indicates that the variance of the objective function of resource leveling is decreased by 7.9%, 18.2%, respectively, certifying the effectiveness and stronger global convergence ability of the EPSO.     

Joint adaptive waveform and baseline range design for bistatic radar

The problems of joint adaptive waveform design and baseline range design for bistatic radar to maximize the practical radar resolution were considered. Distinguishing from the conventional ambiguity function （AF）-based resolution which is only related with the transmitted waveform and bistatic geometry and could be regarded as the potential resolution of a bistatic radar system, the practical resolution involves the effect of waveform, signal-to-noise ratio （SNR） as well as the measurement model. Thus, it is more practical and will have further significant application in target detection and tracking. The constraint optimization procedure of joint adaptive waveform design and baseline range design for maximizing the practical resolution of bistatic radar system under dynamic target scenario was devised. Simulation results show that the range and velocity resolution are enhanced according to the adaptive waveform and bistatic radar configuration.     

An adaptive waveform-detection threshold joint optimization method for target tracking

The joint optimization of detection threshold and waveform parameters for target tracking which comes from the idea of cognitive radar is investigated for the modified probabilistic data association （MPDA） filter. The transmitted waveforms and detection threshold are adaptively selected to enhance the tracking performance. The modified Riccati equation is adopted to predict the error covariance which is used as the criterion function, while the optimization problem is solved through the genetic algorithm （GA）. The detection probability, false alarm probability and measurement noise covariance are all considered together, which significantly improves the tracking performance of the joint detection and tracking system. Simulation results show that the proposed adaptive waveform-detection threshold joint optimization method outperforms the adaptive threshold method and the fixed parameters method, which will reduce the tracking error. The average reduction of range error between the adaptive joint method and the fixed parameters method is about 0.6 m, while that between the adaptive joint method and the adaptive threshold only method is about 0.3 m. Similar error reduction occurs for the velocity error and acceleration error.     

Research on generalized optimization process for mechanical product

The generalized optimization process for mechanical product is proposed which includes functional optimization phase, conceptual design optimization phase, technical design optimization phase (that is further divided into product modeling phase, optimization process scheduling phase, optimization modeling phase and multi-computer collaborative optimizing phase), and result analysis and evaluation phase. The characteristics of the generalized optimization are incarnated such as oriented to the design of entire system, whole process and overall performance of a product and combined with human intelligent and artificial intelligent optimization. The functions and the achieved strategies of each key phase in the generalized optimization process are discussed. A prototype of the generalized optimization supported system for mechanical product is preliminarily established.     

Inclusion''''s Removel Mechanism in Liquid Steel of Continuous Casting Tundish

Three dimensional velocity distribution and turbulence energy dissipation of the steel in tundish are calculated. Then, the mathematical model of collision removal and flotation removal is established. In this collision mode,the rate constants of all types of collision are concluded, the change rate of inclusion number density is obtained.The quantitative calculation shows that it is violent turbulence at the inlet area of tundish and that collision is the main removal type to those inclusions with radius from 0 to 25μm.The collision of inclusions has two effect: one is to decrease the number of small inclusions, the other is to increase the number of big inclusions.To calculate the removal of inclusions, both collision and flotation should be considered. At last, the result of industrial experiment proves the collisien model is true.     

Using Adaptive Gain Scheduling LQR Method Control of Arm Driven Inverted Pendulum System Based on PIC18F4431

The arm driven inverted pendulum system is a highly nonlinear model, muhivariable and absolutely unstable dynamic system so it is very difficult to obtain exact mathematical model and balance the inverted pendulum with variable position of the ann. To solve this problem, this paper presents a mathematical model for arm driven inverted pendulum in mid-position configuration and an adaptive gain scheduling linear quadratic regulator control method for the stabilizing the inverted pendulum. The proposed controllers for arm driven inverted pendulum are simulated using MATLAB-SIMULINK and implemented on an experiment system using PIC 18F4431 mieroeontroller. The result of experiment system shows the control performance to be very good in a wide range stabilization of the arm position.     

Application of experimental design in optimization of crude oil adsorption from saline waste water using raw bagasse简

Experimental design was applied in the optimization of crude oil adsorption from saline waste water using raw bagasse.The application of response surface methodology（RSM） was presented with temperature,salinity of water,pH,adsorbent dose,and initial oil content as factors.A quadratic model could be used to approximate the mathematical relationship of crude oil removal on the five significant independent variables.Predicted values and experimental values are found to be in good agreement with R2 of 97.44%.The result of optimization shows that the maximum crude oil removal is equal to 67.38% under the optimal condition of temperature of 46.53 °C,salinity of 37.2 g/L,pH of 3,adsorbent dose of 9 g/L and initial oil content of 300×10-6.     

超远程火炮弹药的无控及滑翔弹道优化研究

研究并揭示超远程炮弹无控段及滑翔段的弹道特性.建立无控弹道数学模型和大气数学模型,研究初始射角和发动机点火时间的双参数寻优以及弹道下落段最佳起始滑翔点的确定,建立弹道滑翔段最优控制数学模型,研究最优滑翔弹道.超远程弹弹道无控段采用初始射角和发动机点火时间的双参数寻优使无控射程最远,弹道下滑段根据弹道高度确定最佳起始滑翔点,通过将变终值时间的双边界值最优控制问题转化为方便求解的固定终值时间的优化问题,提出求解超远程弹最优滑翔弹道实际工程问题的有效方法.     

Design of Robust-Adaptive Controller of Extended Range Guided Munition

Finite state machine theory (FSM) is introduced and applied to global control of electric vehicle. Theoretical adaptation for application of FSM in control of electric vehicle is analyzed. Global control logic for parts of electric vehicle is analyzed and built based on FSM. Using Matlab/Simulink, BJD6100-HEV global control algorithm is modeled and prove validity by simulation.     

基于改进的粒子群和遗传算法的混合优化算法

分析粒子群算法在求解组合优化问题中的运行原理,对警车分布的优化问题建立了粒子群优化的数学模型,对基本粒子群优化算法中的速度范围、惯性权重等参数进行了改进,并通过仿真与基本粒子群算法比较,显示改进的粒子群算法,提高了优化结果.在改进的粒子群算法中引入遗传算法,将形成的新混合算法应用到求解警车最优执勤地点的分布问题,并与遗...     

基于NN-GA的优化设计方法

针对优化设计中,传统的建模方法无法建立复杂系统的精确模型和传统优化算法无法获得满意优化效果的问题,将神经网络(NN)和遗传算法(GA)应用到设计中,构成一种基于NN-GA的优化方法.利用神经网络建立系统的数学模型,用遗传算法对参数进行优化,得出系统的最优值,并经过计算实例验证取得了较好效果.     

Genetic algorithm-based evaluation of spatial straightness error

~~Genetic algorithm-based evaluation of spatial straightness error@崔长彩$Dept. of Automation Measurement and Control,Harbin Institute of Technology!Harbin 150001,China @车仁生$Dept. of Automation Measurement and Control,Harbin Institute of Technology!Harbin 150001,China @黄庆成$Dept. of Automation Measurement and Control,Harbin Institute of Technology!Harbin 150001,China @叶东$Dept. of Automation Measurement and Control,Harbin Institute of Technology!Harbin 150001,Chi…     

遗传算法优化结构元素的红外小目标检测方法

提出了一种基于遗传算法优化结构元素,从而对红外小目标进行检测的方法。数学形态学中结构元素选取的正确与否,决定着小目标检测的好坏。利用遗传算法可以在全局范围内搜索到最优值的特点,对结构元素进行优化选取,而无需根据先验知识来确定结构元素。实验表明,该方法不仅可以准确地检测出红外小目标,方法简单,而且自适应强,不用人为的干预,完全可以实现自动的对小目标进行检测.     

基于Matlab遗传工具箱的高强混凝土配合比优化

利用Matlab提供的遗传工具箱来解决高强混凝土配合比的优化设计问题.从成功的配合比数据中,归纳出各组分用量的取值范围,以1 m3混凝土成本为优化目标.将带非线性约束的数学规划问题转化成线性不等式、区间约束和非线性约束函数,设置遗传算法的各相关参数,完成遗传操作获得最优解.经实配试验其结果与工程实例中提供的设计参数相比,减少费用17.2%.     

遗传算法在曲柄摇杆机构优化设计中的应用

本文采用遗传算法 ,对曲柄摇杆机构再现已知运动规律问题进行了优化设计。文中对优化问题的数学模型和遗传算法在解决此问题时的计算过程进行了比较全面的描述。通过与传统的惩罚函数法在同一问题中应用结果的对比 ,表明了该算法的有效性     

基于遗传算法的网格结构的拓扑优化

遗传算法是一种新兴的基于遗传进化机理的寻优技术。它与常规算法的不同之处在于不受初始值影响、从多个初始点开始寻优，并采用交叉、变异和移民算子避免过早地收敛到局部最优解，可获得全局最优解。该算法不必求导计算，编程简单、快捷，尤其适用于具有离散变量的结构优化设计。本文利用遗传算法对在应力、位移约束下的网格结构进行拓扑优化，利用对称性对杆件进行分组，使优化后的结构模型仍然保持对称，具有工程应用价值，并达到降低造价的目的。计算实例表明了该算法的有效性。     

一种求解TSP问题的改进遗传算法

旅行商问题(Traveling Salesman Problem TSP)是一个典型的组合优化问题,但应用基本遗传算法求解TSP问题时存在许多不足.结合TSP问题的特点,提出一种改进的遗传算法:应用贪心策略初始化种群,用2-opt对其进行优化,使得在初始个体中就包含较优子路径,在一定程度上加快算法收敛性,防止早熟和近亲繁殖.对交叉算子和变异算子进行改进后,既能维持种群的多样性,也保留了父代个体大部分优良性能.应用改进的算法对20个城市的TSP问题进行求解,结果表明该算法求解速度快而且求解的质量较好.