基于混沌遗传算法的PID参数优化

随着计算机技术的飞跃发展和人工智能技术渗透到自动控制领域,各种先进PID控制器参数整定方法层出不穷,给PID控制器参数整定的研究带来了无限活力和契机;然而很多先进的PID参数整定方法并没有像预期的那样产生完美的控制效果.将遗传算法和混沌优化方法智能集成,利用混沌序列的"遍历性、随机性、规律性"的特点生成初始种群,在遗传操作中加入混沌细搜索,大大提高了局部搜索能力,能有效防止遗传算法陷入局部最优和发生早熟现象,仿真表明,混沌遗传算法优化结果相当理想,效果令人满意,优于常规的遗传算法.     

基于遗传算法的延迟反馈法控制Lorenz混沌

该文将遗传算法与混沌的延迟反馈控制方法相结合,提出了基于遗传算法的延迟反馈控制方法。它利用遗传算法强大的寻优能力来整定延迟反馈控制中的控制刚度矩阵参数,从而克服了延迟反馈控制中控制刚度难以确定的问题。对混沌Lorenz系统的仿真控制说明该算法具有快速性好和抗干扰能力强等优点。     

基于在线自适应遗传算法的PID参数整定和优化

研究自动控制系统优化问题,控制系统响应特性取决于系统参数的调整.针对传统的PID控制算法参数较多,且难以整定,使得控制效果不理想等问题,提出了自适应遗传算法的PID参数整定.根据遗传算法具有在线性差的问题,对遗传算法作了改进.通过自适应遗传算法对PID参数进行整定与寻优,选择自适应度大的个体所对应的PID控制参数作为采样时间下的PID控制参数.改进后的算法有效提高了遗传算法的寻优能力,提高了算法的收敛的速度,在一定的范围可以求得最优全局解.在MATLAB上仿真结果表明,在PID参数的寻优过程中,自适应遗传算法具有更强的寻优能力,提高了控制系统的自适应性,为优化控制系统设计提供了依据.     

基于混沌免疫遗传算法整定PID参数

针对传统免疫遗传算法PID参数整定速度慢的缺点,通过引入了混沌增殖思想和隔离小生境技术,结合免疫遗传算法的特点,设计了一种智能的PID参数整定方法。该方法利用混沌增殖对初值的敏感性以及随机性、遍历性、规律性,使免疫遗传算法能够更加有效地跳出局部收敛区域而以更快的速度向全局最优值收敛,进而较好地处理了通常遗传算法中遇到的“早熟”问题。通过隔离小生境技术的引入使得子种群的进化不仅同整个种群的进化密切相关,还有自身进化的独立性,这有利于种群个体多样性的保持。通过实际PID参数整定的例子,结果表明该算法能明显改善免疫遗传算法的收敛性能,搜索效率也得到了显著提高。     

基于人工免疫多目标寻优算法的PID自整定

提出了一种基于人工免疫多目标寻优算法(AIMOA)的PID参数自适应整定的设计方法.利用生物免疫系统的免疫机理设计系统响应的目标函数,再通过AIMOA算法搜索PID控制器的优化参数组,最后将基于AIMOA算法同基于遗传算法(GA) 和齐格勒-尼柯尔斯(Zi-Ni)方法的PID自整定进行了仿真比较.结果表明:AIMOA算法具有快速收敛性,能够较快地搜索到PID参数自适应整定的最优或者次最优解,体现了算法的优越性、实用性和有效性.     

一种改进混沌鱼群算法及其在四旋翼滑模控制中的应用

针对四旋翼飞行器滑模控制参数难以确定的问题,提出使用一种结合混沌策略的改进鱼群算法对参数进行优化;将Tent映射和鱼群算法结合,参考目标函数值对步长进行修正,获得了改进混沌鱼群算法,并将算法运用到滑模控制器参数整定过程中;结合四旋翼飞行器模型,进行了数据仿真实验,获得的控制器参数在响应速度和能耗方面都优于常规方法整定的参数,可实现飞行器的稳定控制;结果表明,改进的混沌鱼群算法能够有效收敛到全局最优解,在维度为10的算例中,能够实现多目标全局优化。     

基于ICAS算法的原动机仿真系统PI参数整定

原动机仿真系统是模拟原动机调速系统对电力系统机电暂态影响的重要装置,其速度电流调节器的参数设定长久以来主要依靠工程经验和反复修正得到,不仅耗时费力、准确度差,而且对装置的运行造成很大影响。新引入的遗传算法又存在早熟、收敛性差且不易于工程人员理解和参数辨识等问题。本文在分析得出速度电流综合调节器的本质为PI调节器的基础上,提出一种改进的混沌蚁群算法,以ITAE指标为目标函数对其比例系数K_p和时间常数τ_L进行整定。针对原动机仿真系统装置运用MATLAB编程比较传统算法、遗传算法和ICAS算法的控制效果,验证了该算法对PI参数整定的优越性。     

基于浮点数编码遗传算法的PI参数优化算法

本文介绍了基于浮点数编码遗传算法寻优的PID参数优化方法,采用误差绝对值时间平方积分性能指标作为参数选择的目标函数,利用遗传算法的全局搜索能力,实现对全局最优解的寻优,以降低PID参数整定的难度,达到总体提高系统性能的目的.仿真结果表明,通过浮点数编码遗传算法进行PI参数优化可使系统具有很好的动态品质和稳态特性.     

基于DESO的Mean Shift目标跟踪算法研究

将基于DESO的运动预测算法和Mean Shift算法相结合,形成一种新的基于Mean Shift的快速目标跟踪算法.该算法以DESO预测位置作为Mean Shift算法下一帧候选模型的计算中心,实现了对快速运动目标的跟踪,并通过DESO对目标运动轨迹进行预测,较好地解决了目标完全遮挡时的跟踪问题.实验结果表明,该算法具有预测精度高、实时性好、抗遮挡能力强的优点.     

一种基于改进粒子群算法的PID参数整定方法

粒子群算法是一种基于仿生学的全局优化算法,具有方法简单,易于实现,寻优效果好等优点,是工业PID控制参数整定的常用方法。本文针对标准粒子群算法的PID整定方法易于陷入局部最优,引入遗传算法中杂交的思想,通过引入杂交算子改善早熟问题,同时针对粒子群算法样本集随机产生,无法保证样本集多样性的缺点,特引入混沌理念,对样本集采用混沌序列法生成,提高样本的多样性,随机性。仿真结果表明,所采用改进算法在PID参数整定中,超调小,收敛快,较好的弥补了标准粒子群算法的弊端。     

Evolution or revolution: the critical need in genetic algorithm based testing

Software testing is one of the most inevitable processes in software development. The field of software testing has seen an extensive use of search based techniques in the last decade. Among the search based techniques, it is the metaheuristic techniques such as genetic algorithm that has garnered the major share of attention from researchers. Looking at the large body of work that has happened and is happening in this field, we feel that it is high time someone studied how well genetic algorithm based techniques fare in practical testing process. Method: In this work, we present a roadmap to the future of genetic algorithm based software testing, based on a review of literature. We have mainly reviewed the works which use genetic algorithm for software test data generation. This independent review is designed to direct the attention of future researchers to the deficiencies of genetic algorithm based testing, their possible solutions and the extent to which they are correctable. The observations from the selected primary studies highlight the issues faced when genetic algorithm is applied in software testing. The observations form the review reveal that the type of genetic algorithm used, fitness function design, population initialization and parameter settings does impact the quality of solution obtained in software testing using genetic algorithm. From the review we conclude that, more generalized approaches can make genetic algorithm based software testing one of the strongest methods in practical software testing. We hope that, this review will be a major breakthrough in genetic algorithm based software testing field.     

遗传算法在跨超声速风洞总压控制中的应用

总压作为风洞控制中的重要流场参数,其调节性能是风洞控制系统能否满足试验要求的重要指标,为提高跨超声速风洞的总压控制水平,需对总压控制策略进行设计。针对某跨超声速风洞对总压控制系统提出的快速性和精确性要求,提出串级控制、智能PID控制和总压分段控制等方法,并利用MATLAB系统辨识工具箱对流场调节阶段的总压系统模型进行了辨识。提出将遗传算法应用于风洞流场调节阶段的PID控制器参数整定中,重点对基于遗传算法的PID控制原理和参数整定步骤进行介绍,并针对遗传算法的遗传算子进行了设计。系统仿真和风洞实际运行情况表明：该方法较常规PID参数整定与优化方法,具有更好的控制性能指标,满足总压控制系统精确性、快速性、鲁棒性等要求,为后续风洞建设和设备改造提供了新方法。     

基于改进遗传算法的立体视觉系统标定

立体视觉系统的摄像机标定是一个多参数、非线性的复杂函数优化问题,传统优化方法很难解决。论文对标准遗传算法的编码方式进行了改进,经过改进后的遗传算法具有变量搜寻区间的自适应调整能力,在保持染色体编码长度不变的情况下,能同时满足变量搜索空间大小和编码精度的要求。利用改进了的遗传算法对双目视觉系统摄像机进行标定的结果表明,该算法能有效地实现高维寻优空间的近优解搜索。     

基于混合神经网络与遗传算法方法的注塑参数优化

建立了基于混合神经网络与遗传算法方法的注塑工艺参数优化系统，用Matlab语言编制了应用程序，对神经网络的参数预测与遗传算法的优化过程进行求解。将网络预测结果与CAE模拟结果进行比较和误差分析，显示出BP网络的稳定性和可靠性；优化结果经CAE模拟和实验验证，证明是正确的，表明基于混合神经网络与遗传算法方法的注塑工艺参数优化方法是可行的。     

A study on flowshop scheduling problem combining Taguchi experimental design and genetic algorithm

As genetic algorithm parameters vary depending on different problem types when applying genetic algorithm to reach global optimum, appropriate design value selection has significant impact on the efficiency of genetic algorithm. However, most users adjust parameters manually based on the reference values of previous literature. Such trial-and-error method is time-consuming, ineffective, and often it could not locate the optimal combination. Therefore, in flowshop scheduling problems, this research anticipates to complete optimal parameter combination design in genetic algorithm using Taguchi experimental design. According to the research results, different ways of producing initial solution have significant influence on this research topic. Consequently, confirmation experiment is conducted using the optimal parameter combination obtained from the research results. It is found that the predicted value of signal-to-noise ratio (S/N ratio) and its actual value exists deviation of 0.238%, indicating repetitiveness and robustness of the obtained parameter combination. Hence, this research method can effectively reduce time spent on parameter design using genetic algorithm and increase efficiency of algorithm.     

Hybrid intelligent parameter estimation based on grey case-based reasoning for laminar cooling process

In this paper, a hybrid intelligent parameter estimation algorithm is proposed for predicting the strip temperature during laminar cooling process. The algorithm combines a hybrid genetic algorithm (HGA) with grey case-based reasoning (GCBR) in order to improve the precision of the strip temperature prediction. In this context, the hybrid genetic algorithm is formed by combining the genetic algorithm with an annealing and a local multidimensional search algorithm based on deterministic inverse parabolic interpolation. Firstly, the weight vectors of retrieval features in case-based reasoning are optimised using hybrid genetic algorithm in offline mode, and then they are used in grey case-based reasoning to accurately estimate the model parameters online. The hybrid intelligent parameter estimation algorithm is validated using a set of operational data gathered from a hot-rolled strip laminar cooling process in a steel plant. Experiment results show the effectiveness of the proposed method in improving the precision of the strip temperature prediction. The proposed method can be used in real-time temperature control of hot-rolled strip and has potential for parameter estimation of different types of cooling process.     

时滞系统控制参数优化的遗传模拟退火算法

该文基于遗传模拟退火算法，提出一种时滞系统的控制参数优化方法，同时对Matlab遗传算法工具箱GAOT进行改进，使之适用于PID参数的优化。该文所采用的算法保留了遗传算法和模拟退火算法分别在全局和局部搜索能力强的优点，能克服常规遗传算法中解的早熟现象、局部寻优能力差，难以保证对参数优化的计算效率和可靠性要求等缺陷。研究表明，改进后的遗传模拟退火算法是一种行之有效的方法，具有实用价值。     

利用改进遗传算法的参数估计

基于极大似然法的参数估计实质上是一个复杂的非线性优化问题，传统的优化方法计算效率较低且容易陷入局部极值。而遗传算法是一种有导向的随机搜索方法，能以较大的概率收敛到全局最优解。本文将单纯形法引入到并行遗传算法中，提出了一种改进的遗传算法，可以有效地提高算法的收敛速度、防止搜索过程中的早熟现象。应用于系统初始状态未知时的参数估计问题，获得了满意的结果。     

Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks — A Case Study for the Optimal Ordering of Tables

Parameter setting for evolutionary algorithms is still an important issue in evolutionary computation. There are two main approaches to parameter setting: parameter tuning and parameter control. In this paper, we introduce self-adaptive parameter control of a genetic algorithm based on Bayesian network learning and simulation. The nodes of this Bayesian network are genetic algorithm parameters to be controlled. Its structure captures probabilistic conditional (in)dependence relationships between the parameters. They are learned from the best individuals, i.e., the best configurations of the genetic algorithm. Individuals are evaluated by running the genetic algorithm for the respective parameter configuration. Since all these runs are time-consuming tasks, each genetic algorithm uses a small-sized population and is stopped before convergence. In this way promising individuals should not be lost. Experiments with an optimal search problem for simultaneous row and column orderings yield the same optima as state-of-the-art methods but with a sharp reduction in computational time. Moreover, our approach can cope with as yet unsolved high-dimensional problems.