Automatically extracting T–S fuzzy models using cooperative random learning particle swarm optimization

This paper proposes a methodology for automatically extracting T–S fuzzy models from data using particle swarm optimization (PSO). In the proposed method, the structures and parameters of the fuzzy models are encoded into a particle and evolve together so that the optimal structure and parameters can be achieved simultaneously. An improved version of the original PSO algorithm, the cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of PSO. CRPSO employs several sub-swarms to search the space and the useful information is exchanged among them during the iteration process. Simulation results indicate that CRPSO outperforms the standard PSO algorithm, genetic algorithm (GA) and differential evolution (DE) on the functions optimization and benchmark modeling problems. Moreover, the proposed CRPSO-based method can extract accurate T–S fuzzy model with appropriate number of rules.     

基于模糊c-means与自适应粒子群优化的模糊聚类算法

已有的粒子群模糊聚类算法需要设置粒子群参数并且收敛速度较慢,对此提出一种基于改进粒子群与模糊c-means的模糊聚类算法。首先,使用模糊c-means算法生成一组起始解,提高粒子群演化的方向性;然后,使用改进的自适应粒子群优化方法对数据进行训练与优化,训练过程中自适应地调节粒子群参数;最终,采用模糊c-means算法进行模糊聚类过程。对比实验结果表明,所提方法大幅度提高了计算速度,并获得了较高的聚类性能。     

自适应变异粒子群算法

为了解决粒子群种群多样性低、容易陷入局部最优的缺点,结合最优粒子和其他粒子在种群中的不同作用,给出了一种自适应变异粒子群算法。算法中最优粒子根据种群进化程度,自适应调整自身搜索邻域大小,增强种群的局部搜索能力;对非最优粒子的位置进行小概率的随机初始化,当其速度为零时,速度自适应变化,以便增强种群多样性和全局搜索能力。仿真实验中,将算法应用于6个典型复杂函数优化问题,并与其他变异粒子群算法比较,结果表明,增强种群多样性的同时提高了局部搜索能力。     

基于粒子群优化算法的PID控制器参数整定

PID控制器的性能完全依赖于其参数的整定和优化，但参数的整定及在线自适应调整对常规的PID控制器是难以解决的问题。根据粒子群算法具有对整个参数空间进行高效并行搜索的特点，提出了一种基于粒子群优化算法整定PID控制器参数的设计方法，并定义了一种新的性能指标函数来评价PID控制器的性能。现以二阶的船舶控制装置为研究对象，运用粒子群优化方法对PID控制器参数进行了寻优研究。仿真结果表明，该方法比一般PID参数整定方法具有更好的控制性能指标，有着一定的工程应用价值。     

基于EHA的车辆主动悬架建模与仿真研究

采用一种新颖的可控减振技术,提出基于EHA的主动悬架系统.文章在建立1/4车辆悬架动力学模型和EHA综合模型的基础上,以天棚阻尼模型作为参考模型,利用模糊控制方法对簧载质量的垂直加速度进行控制并与被动悬架进行了比较.仿真结果表明:在随机和脉冲两种信号的激励下,相对于被动悬架,主动悬架簧载质量的垂直加速度均方根值分别下降了27%、16%,结果证明了所建模型的正确性以及模糊控制方法对改善汽车性能的可行性和有效性.     

Harmony search algorithm-based fuzzy-PID controller for electronic throttle valve

The electronic throttle control (ETC) for a gasoline engine is a typical nonlinear plant because of its nonlinear spring and model-parameter changes caused by external environmental variables. In this paper, a fuzzy proportional-integral-derivative (PID) control strategy is proposed in order to improve the responsiveness of ETC. In the fuzzy-PID scheme, the input variables are the error signal and its derivative, and the output variable is PID gains expressed in terms of fuzzy rules. In this manner, the fuzzy-PID controller has more flexibility and capability than conventional ones. A novel technique to tune the fuzzy rules of fuzzy-PID controller is proposed using a harmony search algorithm, which can search the optimal PID gains for the plant. Simulation and experiment results have shown the effective performance of the proposed controller.     

求解分布式顺序统计CFAR检测器参数的新方法

复杂条件下,分布式顺序统计恒虚警率（OS-CFAR）检测系统的参数选择和检测性能分析是一个典型的非线性优化问题,通常采用数值求解和计算机搜索的方法。但在复杂条件下,特别是当传感器数量较多,或采用分布式OS-CFAR这种双门限参数检测方式时,其计算量会异常庞大。提出了一种基于模拟退火的微粒群优化算法,将模拟退火思想引入到具有杂交和高斯变异的粒子群优化算法中,并采用具有递减w算法,保证算法具有较好的全局搜索能力和较好的收敛性。使用这种方法,在进化100代后,在保证精度达到0.000 001,可使所有的系统参数同时得到优化。仿真结果表明,同遗传算法比,虽然该方法收敛速度稍慢,但是可避免遗传算法的早熟问题,同时该方法实施简单方便,便于工程应用。     

一种多尺度协同变异的粒子群优化算法

为了改善粒子群算法易早熟收敛、精度低等缺点,提出一种多尺度协同变异的粒子群优化算法,并证明了该算法以概率1收敛到全局最优解.算法采用多尺度高斯变异机制实现局部解逃逸.在算法初期阶段,利用大尺度变异及均匀变异算子实现全局最优解空间的快速定位;随着适应值的提升,变异尺度随之降低;最终在算法后期阶段,利用小尺度变异算子完成局部精确解空间的搜索.将算法应用6个典型复杂函数优化问题,并同其他带变异操作的PSO算法比较,结果表明,该算法在收敛速度及稳定性上有显著提高.     

基于Bloch 球面坐标编码的量子粒子群算法及应用

为提高粒子群算法的优化效率,在分析粒子群优化算法的基础上,提出了一种基于Bloch球面坐标编码的量子粒子群优化算法。该算法每个粒子占据空间三个位置,每个位置代表一个优化解。采用传统粒子群优化方法的搜索机制调整量子位的两个参数,可以实现量子位在Bloch球面上的旋转,从而使每个粒子代表的三个优化解同时得到更新,并快速逼近全局最优解。标准测试函数极值优化和模糊控制其参数优化的实验结果表明,与同类算法相比,该算法在优化能力和优化效率两方面都有改进。     

Optimal fuzzy controller parameters using PSO for speed control of Quasi-Z Source DC/DC converter fed drive

The conventional controller suffers from uncertain parameters and non-linear qualities of Quasi-Z Source converter. However they are computationally inefficient extending to optimize the fuzzy controller parameters, since they exhaustively search the optimal values to optimize the objective functions. To overcome this drawback, a PSO based fuzzy controller parameter optimization is presented in this paper. The PSO algorithm is used to find the optimal fuzzy parameters for minimizing the objective functions. The feasibility of the proposed PSO technique has been simulated and tested. The results are bench marked with conventional fuzzy controller and genetic algorithm for two types of DC/DC converters namely double input Z-Source converter and Quasi-Z Source converter. The results of both the DC/DC converters for several existing methods illustrate the effectiveness and robustness of the proposed algorithm.     

A novel parallel hybrid intelligence optimization algorithm for a function approximation problem

A novel parallel hybrid intelligence optimization algorithm (PHIOA) is proposed based on combining the merits of particle swarm optimization with genetic algorithms. The PHIOA uses the ideas of selection, crossover and mutation from genetic algorithms (GAs) and the update velocity and situation of particle swarm optimization (PSO) under the independence of PSO and GAs. The proposed algorithm divides the individuals into two equation groups according to their fitness values. The subgroup of the top fitness values is evolved by GAs and the other subgroup is evolved by the PSO algorithm. The optimal number is selected as a global optimum at every circulation which shows better results than both PSO and GAs, then improves the overall performance of the algorithm. The PHIOA is used to optimize the structure and parameters of the fuzzy neural network. Finally, the experimental results have demonstrated the superiority of the proposed PHIOA to search the global optimal solution. The PHIOA can improve the error accuracy while speeding up the convergence process, and effectively avoid the premature convergence to compare with the existing methods.     

Krill herd algorithm for optimal location of distributed generator in radial distribution system

Distributed generator (DG) is recognized as a viable solution for controlling line losses, bus voltage, voltage stability, etc. and represents a new era for distribution systems. This paper focuses on developing an approach for placement of DG in order to minimize the active power loss and energy loss of distribution lines while maintaining bus voltage and voltage stability index within specified limits of a given power system. The optimization is carried out on the basis of optimal location and optimal size of DG. This paper developed a new, efficient and novel krill herd algorithm (KHA) method for solving the optimal DG allocation problem of distribution networks. To test the feasibility and effectiveness, the proposed KH algorithm is tested on standard 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results indicate that installing DG in the optimal location can significantly reduce the power loss of distributed power system. Moreover, the numerical results, compared with other stochastic search algorithms like genetic algorithm (GA), particle swarm optimization (PSO), combined GA and PSO (GA/PSO) and loss sensitivity factor simulated annealing (LSFSA), show that KHA could find better quality solutions.     

一种新型自适应复合控制器在锂电池充电系统中的应用

为了解决锂电池充电系统的不确定性和参数整定困难的问题,本文提出了一种基于蚁群算法优化的模糊＋变论域模糊PID复合控制器的新方法。该控制器在系统波动频繁时,采用模糊控制使其具有最优的动态性能;当系统进入稳定阶段,采用PID参数自适应的变论域模糊控制以提高准确度。而用蚁群算法对PID参数值进行离线优化,并将优化后的值作为在线调节的初值,使系统更加稳定。将提出的复合控制策略应用于锂电池充电控制系统中。仿真结果表明,该系统具有良好的抗干扰性和鲁棒性。     

求解TSP问题的模糊自适应粒子群算法

由于惯性权值的设置对粒子群优化（PSO）算法性能起着关键的作用，本文通过引入模糊技术，给出了一种惯性权值的模糊自适应调整模型及其相应的粒子群优化算法，并用于求解旅行商（TSP）问题。实验结果表明了改进算法在求解组合优化问题中的有效性，同时提高了算法的性能，并具有更快的收敛速度。     

基于模糊PID控制器的空压机恒压供气系统的设计

针对空压机供气系统具有大滞后特性及系统稳定性差的问题,介绍了一种基于模糊PID控制器的空压机恒压供气系统的设计。该系统中的模糊PID控制器结合了传统PID控制与模糊控制技术,可完成整个系统的参数自动调整,最终实现系统恒压供气的目的。实际运行结果表明,该系统能根据供气设备及环境的变化做出最优控制,有效提高了整个系统的可靠性、稳定性、动态响应速度及节能效果等。     

Teaching–learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system

This paper deals with the design of a novel fuzzy proportional–integral–derivative (PID) controller for automatic generation control (AGC) of a two unequal area interconnected thermal system. For the first time teaching–learning based optimization (TLBO) algorithm is applied in this area to obtain the parameters of the proposed fuzzy-PID controller. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the fuzzy-PID controller. The superiority of proposed approach is demonstrated by comparing the results with some of the recently published approaches such as Lozi map based chaotic optimization algorithm (LCOA), genetic algorithm (GA), pattern search (PS) and simulated algorithm (SA) based PID controller for the same system under study employing the same objective function. It is observed that TLBO optimized fuzzy-PID controller gives better dynamic performance in terms of settling time, overshoot and undershoot in frequency and tie-line power deviation as compared to LCOA, GA, PS and SA based PID controllers. Further, robustness of the system is studied by varying all the system parameters from −50% to +50% in step of 25%. Analysis also reveals that TLBO optimized fuzzy-PID controller gains are quite robust and need not be reset for wide variation in system parameters.     

基于一种改进粒子群算法的SVM参数选取

支持向量机作为一个新兴的数学建模工具已经被广泛地应用到很多工业控制领域中,其良好的泛化能力和预测精度在很大程度上受到其参数选取的影响.根据智能群体进化模式改进粒子群优化算法.利用模糊C均值聚类算法分类粒子群体,并用子群体最优点取代速度更新公式中的个体历史最优点,并利用该算法搜索支持向量机的最优参数组合.对比仿真实验表明:所提优化算法是支持向量机参数选取的有效算法,在非线性函数估计中体现出优良的性能.     

基于遗传交叉和多混沌策略改进的粒子群优化算法

为有效改进基本PSO算法的搜索能力,提出了一种基于遗传交叉和多混沌方式改进的粒子群算法。该算法为获得比当前群体更优的最优解,采用了以下四种措施：其一,对当前群体中的最优解和每个粒子最优解进行遗传交叉操作;其二,用混沌系统动态地调整PSO算法的惯性权重;其三,对整个解空间进行混沌全局搜索;最后,对当前群体中最优解进行多维和单维的混沌局部搜索。仿真实验结果表明：与其它三种算法相比,提出的算法在解决8个整数和混合整数非线性规划问题时不仅收敛速度最快,而且具有100%的成功率。     

多策略粒子群优化算法

为了克服粒子群优化算法易早熟、局部搜索能力弱的问题,提出了一种改进的粒子群优化算法--多策略粒子群优化算法。在群体寻优过程中,各粒子根据搜索到的最优位置的变动情况,从几种备选的策略中抉择出当代的最优搜索策略。其中,最优粒子有最速下降策略、矫正下降策略和随机移动策略可以选择,非最优粒子有聚集策略和扩散策略可以选择。四个典型测试函数的数值实验结果表明,新提出的算法比标准粒子群优化算法具有更强和更稳定的全局搜索能力。     

改进PSO算法的性能分析与研究*

分析了粒子群优化(PSO)算法的进化式,针对其容易发生早熟、收敛速度慢、后期搜索性能和个体寻优能力降低等缺点,结合遗传算法的思想,提出一种新的混合PSO算法——遗传PSO(GAPSO)。该算法是在PSO算法的更新过程中,对粒子速度引入遗传算法的变异操作,对粒子位置引入遗传算法交叉操作。对速度的变异降低了算法后期因种群过于密集而陷入局部最优的可能,对位置的交叉使得父代中优良个体的基因能够更好地遗传给下一代,从而得到更优、更多样化的后代,加快进化过程,提高了收敛速度和群体搜索性能。选取了其他几种典型的改进PS