PSONN在可编程控制系统PID参数优化中的应用

研究可编程控制系统优化问题,可编程控制系统具有非线性、时变性等特点,传统PID控制器优化方法难以建立精确的数学模型,使得系统参数设定困难,导致可编程控制系统的控制效果不理想。为了解决传统的PID算法所带来的问题,利用RBF神经网络非线性、自学习能力,提出一种基于粒子群神经网络的PID参数优化算法。将粒子群和神经网络相结合,形成了一种智能控制算法,并将应用于可编程控制系统。测试结果表明,粒子群神经网络提高了PID控制参数优化速度,提高了可编程控制系统可靠性和鲁棒性,具有一定的理论和实用价值。     

引入多级扰动的混合型粒子群优化算法

为解决粒子群优化算法易陷入局部最优值的问题,提出一种引入多级扰动的混合型粒子群优化算法.该算法结合两种经典改进粒子群优化算法的优点,即带惯性参数的标准粒子群优化算法和带收缩因子的粒子群优化算法,在此基础上,引入多级扰动机制：在更新粒子位置时,引入一级扰动,使粒子对解空间的遍历能力得到加强;若优化过程陷入“局部最优”的情况,则引入二级扰动,使得优化过程继续,从而摆脱局部最优值.使用了6个测试函数——Sphere函数、Ackley函数、Rastrigin函数、Styblinski-Tang函数、Duadric函数及Rosenbrock函数来对所提出的混合型粒子群优化算法进行仿真运算和对比验证.模拟运算的结果表明：所提出的混合型粒子群优化算法在对测试函数进行仿真时,其收敛精度和收敛速度都优于另外两种经典的改进粒子群优化算法;另外,在处理多峰函数时,本算法不易被局部最优值所限制.     

针对图像分类场景的粒子群算法优化方法

文章针对图像分类任务中特征选择的问题,提出一种基于粒子群算法的优化方法。首先,文章深入研究粒子群算法的基本原理;其次,引入粒子群优化算法进行特征选择;最后,使用支持向量机进行图像分类。实验结果表明,所提出的粒子群优化方法显著提高了图像分类的准确性,且该方法的一致性和稳健性较好。     

电力系统节能优化控制过程仿真分析

研究电力系统节能优化控制问题.传统的PSO节能控制方法以电力系统总发电成本消耗最小为寻优目标,但是在线路阻抗角较小等特殊情况下,输电线路传输的功率与成本之间呈现弱耦合性,此时把功率与成本关联计算,会引起控制过程的不收敛,控制结果效果差.提出采用多重自适应粒子群的电力系统节能控制算法.算法首先生成大量随机粒子,然后根据当前成本与能耗的最优位置和全局最优位置更新粒子的位置和速度,同时在迭代过程中不断调整调度优化的惯性权重和学习因子,使得所有粒子不断逼近节能调度的全局最优值.仿真结果表明,新的粒子群算法在函数最优值上的搜索精度高于同时期的两种粒子群算法.将改进算法用于电力系统优化仿真中,可以有效的优化电力能耗,实用性高.     

基于改进粒子群算法的水轮机调速器参数优化

提出一种改进的粒子群算法,即将微分进化算法与粒子群算法相结合,在更新粒子位置之前,加入微分进化算法,微分进化算法在变异时,考虑了粒子群算法中当前所寻找到的个体粒子所经过的最优位置及其整个粒子群所经历过最优位置,使粒子的进化具有了一定的方向性.利用典型函数证明了该方法具有较好的全局收敛性和收敛精度.将其应用在水轮机的调速系统参数寻优中,通过二次优化,有效地改善水轮机控制系统过渡过程的动态性能,很好地缓解了该工况下稳定性与抗负荷扰动能力的矛盾.     

一种基于差异演化变异的粒子群优化算法

为了保持粒子种群的多样性而避免发生“早熟”的问题,提出一种基于差异演化变异的粒子群优化算法（PSO）,该方法通过粒子聚集性判断如果粒子群中的粒子过于聚集,则使用差异演化算法对PSO算法中各个粒子的自身历史最佳位置进行变异,以实现保持粒子群种群多样性的目的。对4种常用函数的优化问题进行测试并进行比较,结果表明：所改进的粒子群优化算法比标准粒子群优化算法更容易找到全局最优解,优化效率和优化性能明显提高。     

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。     

带压缩因子的粒子群算法在汽包压力控制系统中的应用

针对基本粒子群算法的原理,阐述了一种改进算法(带压缩因子的粒子群算法),简述了PID控制器的工作原理、粒子群参数优化方法的实现,并举例说明此改进算法在某汽包压力控制系统中的应用,利用matlab进行仿真优化,证明此改进算法优化的性能优于基本的粒子群优化算法,有很好的工程应用前景。     

基于PID控制器的铝热连轧张力控制系统的设计与实现

针对PID控制器在铝热连轧张力控制系统中收敛速度慢的问题,提出一种自适应权值粒子群算法优化神经网络PID控制器的设计方法,该方法采用自适应权值粒子群算法优化神经网络的权值和阈值,使它们在调节PID控制器时找到最优参数,仿真结果表明,在铝热连轧张力控制系统中,自适应粒子群算法优化的神经网络PID控制器与其他PID控制器相比能更快地使张力达到稳定状态、缩短响应时间、改善板形。     

基于多模型粒子群优化的PID参数鲁棒整定

针对常规粒子群优化算法存在的鲁棒性能差的问题，提出一种基于多模型的粒子群优化方法．将其应用于对PID控制器参数的优化，有效地避免了PID控制器设计中复杂的参数调试．即使在模型失配的情况下，控制系统仍保持了良好的控制品质和鲁棒性．通过对几个典型被控对象的仿真实验，证明了所提出的优化算法的实用性、有效性和优越性．     

基于CRQP的多小区OFDMA系统联合资源分配算法

针对OFDMA多小区系统中相邻小区同频干扰下的吞吐量最大化问题,在系统功率的约束条件下,基于协同量子粒子群算法提出一种子载波和功率联合分配的协同随机量子粒子群算法（CRQP）。分别利用粒子群算法独立优化子载波的功率分配,并利用改进的量子遗传算法独立优化用户的子载波分配。在独立优化的同时,通过随机协同策略避免陷入局部最优解,达到全局最优。仿真结果表明,与传统的分步求解算法相比,CRQP算法能获得更多的系统吞吐量和更高的资源利用率。     

Study of differential search algorithm based automatic generation control of an interconnected thermal-thermal system with governor dead-band

An attempt has been made to the effective application of a recently introduced, powerful optimization technique called differential search algorithm (DSA), for the first time to solve load frequency control (LFC) problem in power system. In this paper, initially, DSA optimized classical PI/PIDF controller is implemented to an identical two-area thermal-thermal power system and then the study is extended to two more realistic power systems which are widely used in the literature. To assess the usefulness of DSA, three enhanced competitive algorithms namely comprehensive learning particle swarm optimization (CLPSO), ensemble of mutation and crossover strategies and parameters in differential evolution (EPSDE), and success history based DE (SHADE) are studied in this paper. Moreover, the superiority of proposed DSA optimized PI/PID/PIDF controller is validated by an extensive comparative analysis with some recently published meta-heuristic algorithms such as firefly algorithm (FA), bacteria foraging optimization algorithm (BFOA), genetic algorithm (GA), craziness based particle swarm optimization (CRPSO), differential evolution (DE), teaching-learning based optimization (TLBO), particle swarm optimization (PSO), and quasi-oppositional harmony search algorithm (QOHSA). A case of robustness and sensitivity analysis has been performed for the concerned test system under parametric uncertainty and random load perturbation. Furthermore, to demonstrate the efficacy of proposed DSA, the system nonlinearities like reheater of the steam turbine and governor dead band are included in the system modeling. The extensive results presented in this article demonstrate that proposed DSA can effectively improve system dynamics and may be applied to real-time LFC problem.     

混沌粒子群优化的纹理合成算法研究

粒子群算法在搜索后期由于搜索空间有限,容易陷入局部极值,过早地进入早熟状态。针对这种情况,将混沌优化搜索技术用于粒子群算法,利用混沌运动的通历性、随机性等特点,提出了一种混沌粒子群优化的块采样纹理合成算法。实验结果表明,混沌粒子群算法比粒子群算法具有更好的全局寻优能力,克服了粒子群算法的缺点,得到了较高质量的纹理合成图像。     

电动汽车分时租赁用户预约分配问题的新型电磁场优化算法求解

针对电动汽车电量对于行驶里程的限制问题,建立用户预约分配模型,以得到利润最大时的订单分配结果。该类问题属于NP-hard问题,求解具有一定困难。因此,设计一种新型离散电磁场优化算法求解方法。在基本电磁场优化算法的基础上使用二进制编码方式,改变粒子移动方式,并对负电磁场中的电磁粒子增加更新过程。将提出的新算法与遗传算法、二进制粒子群算法、改进二进制布谷鸟算法及二进制狮群算法进行对比,数值实验表明新算法具有更高的计算效率。此外,与传统分配模型相比,新订单分配模型能够获得更高的利润,说明了该模型的有效性。     

A swarm system design based on a modified particle swarm algorithm for a self-organizing scheme

In this paper, an attempt has been made to incorporate some special features in the conventional particle swarm optimization (PSO) technique for decentralized swarm agents. The modified particle swarm algorithm (MPSA) for the self-organization of decentralized swarm agents is proposed and studied. In the MPSA, the update rule of the best agent in a swarm is based on a proportional control concept and the fitness of each agent is evaluated on-line. The virtual zone is developed to avoid conflict among the agents. In this scheme, each agent self-organizes to flock to the best agent in a swarm and migrate to a moving target while avoiding obstacles and collision among agents. Aided by these advantages such as cooperative group behaviors, flexible formation and scalability, the proposed approach enables large-scale swarm agents to distribute themselves optimally for a given task. The simulation results have shown that the proposed scheme effectively constructs a self-organized swarm system with the capability of flocking and migration.     

基于改进粒子群算法的整流电路智能故障诊断

针对基本粒子群算法(PSO)收敛精度低、易陷入局部极小值的缺点。对该算法进行改进,采用自适应调整惯性权重的策略,并且引入扰动因子,平衡集中强化搜索和分散多样化的搜索过程;用改进的PSO算法优化BP神经网络的权值和阈值,并应用于整流电路的故障诊断;仿真研究结果表明,该方法与其它方法相比,收敛速度快,诊断精度高,在整流电路故障诊断中具有良好的故障识别率,便于电路故障自动诊断系统的建立。     

基于高斯变异的量子粒子群算法

粒子群算法相对于其他优化算法来说有着较强的寻优能力以及收敛速度快等特点,但是在多峰值函数优化中,基本粒子群算法存在着早熟收敛现象。针对粒子群算法易于陷入局部最小的弱点,提出了一种基于高斯变异的量子粒子群算法。该算法使粒子同时具有良好的全局搜索能力以及快速收敛能力。典型函数优化的仿真结果表明,该算法具有寻优能力强、搜索精度高、稳定性好等优点,适合于工程应用中的函数优化问题。     

Design of reactive power optimization control for electromechanical system based on fuzzy particle swarm optimization algorithm

Due to low precision and premature tendency of traditional particle swarm optimization, the reactive power optimization control of electromechanical system based on fuzzy particle swarm optimization algorithm was designed. The premise is to meet the constraints of operation conditions. The active network loss was reduced and the static reactive power optimization mathematical model of electromechanical system was constructed by changing the voltage and reactive power distribution of system. Meanwhile, the voltage did not exceed the limit, and the discrete control variables were limited by the maximum allowable action times, so that the dynamic reactive power optimization mathematical model of electromechanical system was built by minimizing the sum of network loss in twenty-four hours of a day. The particle swarm algorithm was optimized by adaptive adjustment strategy, and then the particle position of particle swarm optimization algorithm was updated. Moreover, the static and dynamic reactive power optimization mathematical model of electromechanical system was solved. Finally, the reactive power optimization control of the electromechanical system is realized. Experimental results show that the proposed method has high convergence performance, so it is able to realize the precise control of reactive power optimization for electromechanical system and eliminate the voltage exceeding specified limits of electromechanical system. In this way, the node voltage can always be within the specified range.     

一种随机粒子群算法及应用

为提高粒子群算法的优化效率,在分析量子粒子群优化算法的基础上,提出了一种随机粒子群优化算法。该算法只有一个控制参数,搜索步长由一个随机变量的取值动态决定,通过合理设计控制参数的取值,实现对目标位置的跟踪。标准测试函数极值优化和聚类优化的实验结果表明,与量子粒子群和普通粒子群算法相比,该算法在优化能力和优化效率两方面都有改进。     

Self-organization of Decentralized Swarm Agents Based on Modified Particle Swarm Algorithm

In this paper, an attempt has been made by incorporating some special features in the conventional particle swarm optimization (PSO) technique for decentralized swarm agents. The modified particle swarm algorithm (MPSA) for the self-organization of decentralized swarm agents is proposed and studied. In the MPSA, the update rule of the best agent in swarm is based on a proportional control concept and the objective value of each agent is evaluated on-line. In this scheme, each agent self-organizes to flock to the best agent in swarm and migrate to a moving target while avoiding collision between the agent and the nearest obstacle/agent. To analyze the dynamics of the MPSA, stability analysis is carried out on the basis of the eigenvalue analysis for the time-varying discrete system. Moreover, a guideline about how to tune the MPSA's parameters is proposed. The simulation results have shown that the proposed scheme effectively constructs a self-organized swarm system in the capability of flocking and migration.Category (5) – Intelligent Systems / Intelligent Control / Fuzzy Control / Prosthetics / Robot Motion Planning