Self-adaptive PID controller of microwave drying rotary device tuning on-line by genetic algorithms

The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.

Generalized Self-Adaptive Genetic Algorithms

In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm generalized self-adaptive genetic algorithm (GSAGA) is presented. (1) Evenly distributed initial population is generated. (2) Superior individuals are not broken because of crossover and mutation operation for they are sent to subgeneration directly. (3) High quality im- migrants are introduced according to the condition of the population schema. (4) Crossover and mutation are operated on self-adaptation. Therefore, GSAGA solves the coordination problem between convergence and searching performance. In GSAGA, the searching per- formance and global convergence are greatly improved compared with many existing genetic algorithms. Through simulation, the val- idity of this modified genetic algorithm is proved.

Modeling and Adaptive Self-Tuning MVC Control of PAM Manipulator Using Online Observer Optimized with Modified Genetic Algorithm

In this paper, the application of modified genetic algorithms (MGA) in the optimization of the ARX Model-based observer of the Pneumatic Artificial Muscle (PAM) manipulator is investigated. The new MGA algorithm is proposed from the genetic algorithm with important additional strategies, and consequently yields a faster convergence and a more accurate search. Firstly, MGA-based identification method is used to identify the parameters of the nonlinear PAM manipulator described by an ARX model in the presence of white noise and this result will be validated by MGA and compared with the simple genetic algorithm (GA) and LMS (Least mean-squares) method. Secondly, the intrinsic features of the hysteresis as well as other nonlinear disturbances existing intuitively in the PAM system are estimated online by a Modified Recursive Least Square (MRLS) method in identification experiment. Finally, a highly efficient self-tuning control algorithm Minimum Variance Control (MVC) is taken for tracking the joint angle position trajectory of this PAM manipulator. Experiment results are included to demonstrate the excellent performance of the MGA algorithm in the NARX model-based MVC control system of the PAM system. These results can be applied to model, identify and control other highly nonlinear systems as well.

Modified Shuffled Frog Leaping Algorithm for Solving Economic Load Dispatch Problem

In the recent restructured power system scenario and complex market strategy, operation at absolute minimum cost is no longer the only criterion for dispatching electric power. The economic load dispatch (ELD) problem which accounts for minimization of both generation cost and power loss is itself a multiple conflicting objective function problem. In this paper, a modified shuffled frog-leaping algorithm (MSFLA), which is an improved version of memetic algorithm, is proposed for solving the ELD problem. It is a relatively new evolutionary method where local search is applied during the evolutionary cycle. The idea of memetic algorithm comes from memes, which unlike genes can adapt themselves. The performance of MSFLA has been shown more efficient than traditional evolutionary algorithms for such type of ELD problem. The application and validity of the proposed algorithm are demonstrated for IEEE 30 bus test system as well as a practical power network of 203 bus 264 lines 23 machines system.

Aquifer parameter identification by the best chromosome clone plus younger generation chromosome prepotency genetic algorithm

This paper developed an improved combinatorial method called the best chromosome clone plus younger generation chromosome prepotency genetic algorithm (BCC-YGCP-GA) to evaluate aquifer parameters. This method is based on a decimal simple genetic algorithm (SGA). A synthetic example for unsteady-state flow in a two-dimensional, inhomogeneous, confined aquifer containing three hydraulically distinct zones, is used to develop data to test the model. The simulation utilizes SGA and BCC-YGCP-GA coupled to the finite element method to identify the mean zonal hydrau-lic conductivities, and storage coefficients of the three-compartment model. For this geometrically simple model, used as a prototype of more complex systems, the SGA does not reach convergence within 100 generations. Conversely, the convergence rate of the BCC-YGCD-GA model is very fast. The objective function value calculated by BCC-YGCD-GA is reduced to 1/1 000th of the starting value within 100 generations, and the hydraulic conductivity and storage of three zones are within a few percent of the “true” values of the ideal model, highlighting the power of the method for aquifer parameterization.

Best compromising crashworthiness design of automotive S-rail using TOPSIS and modified NSGAⅡ

In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.

A self-adaptive stochastic resonance system design and study in chaotic interference

The us of stochastic resonance （SR） can effectively achieve the detection of weak signal in white noise and colored noise. However, SR in chaotic interference is seldom involved. In view of the requirements for the detection of weak signal in the actual project and the relationship between the signal, chaotic interference, and nonlinear system in the bistable system, a self-adaptive SR system based on genetic algorithm is designed in this paper. It regards the output signal-to-noise ratio （SNR） as a fitness function and the system parameters are jointly encoded to gain optimal bistable system parameters, then the input signal is processed in the SR system with the optimal system parameters. Experimental results show that the system can keep the best state of SR under the condition of low input SNR, which ensures the effective detection and process of weak signal in low input SNR.

Self-adaptive mechanism based genetic algorithms for combinatorial optimization problems

To improve the evolutionary algorithm performance,especially in convergence speed and global optimization ability,a self-adaptive mechanism is designed both for the conventional genetic algorithm(CGA)and the quantum inspired genetic algorithm(QIGA).For the self-adaptive mechanism,each individual was assigned with suitable evolutionary parameter according to its current evolutionary state.Therefore,each individual can evolve toward to the currently best solution.Moreover,to reduce the running time of the proposed self-adaptive mechanism-based QIGA(SAM-QIGA),a multi-universe parallel structure was employed in the paper.Simulation results show that the proposed SAM-QIGA have better performances both in convergence and global optimization ability.

基于纹理分析的柴油发动机故障诊断方法

柴油发动机故障特征提取是对其故障识别过程中的关键步骤,直接关系到识别的准确性和时效性。将纹理分析理论应用于柴油发动机故障特征提取,提出基于改进层次分解(Modified Hierarchical Decomposition,MHD)和灰度图像处理的故障特征提取方法。使用MHD将单个一维的振动信号样本分解为多个一维子信号并分别转换成灰度图像;加速分割测试获得特征(Features from Accelerated Segment Test,FAST)算法被用于检测灰度图像的特征点;通过Gabor滤波器组的实部对图像进行卷积,利用特征点的响应计算直方图作为特征向量。为检验通过该方法提取的故障特征对柴油发动机不同故障类型的识别能力,引入非支配排序遗传算法Ⅱ(Non-dominated Sorting Genetic Algorithm-Ⅱ,NSGA-Ⅱ)和支持向量机(Support Vector Machine,SVM)进行故障状态识别。通过实验台开展预置故障实验,将提出的方法与传统方法对比。实验结果表明:该方法的故障诊断准确率最高,为开展柴油发动机故障诊断提供一种新的思路。

基于改进遗传退火算法的10 kV配电网规划优化研究

在考虑配电网输电综合网损、年折旧费、馈线建设费用的基础上,建立10 kV配电网规划目标优化模型;引进改进遗传退火算法,进行10 kV配电网规划目标优化模型中相关参数条件的训练,以此实现对模型约束条件的设计;采用保存精英群的方式,进行10 kV配电网规划优化方案的全局寻优,完成配电网规划优化方法的设计。选择某地区大型10 kV配电网作为实验研究的主要对象,设计实例应用实验。实验结果表明:设计的优化方法在实际应用中的效果良好,可有效提高配电网线路负载率均衡度。

Capability Analysis of Chaotic Mutation and Its Self-Adaption

Through studying several kinds of chaotic mappings' distributions of orbital points, we analyze the capabilityof the chaotic mutations based on these mappings. Nunerical experiments support our conclusions very well. Thecapability analysis also led to a self-adaptive mechanism of chaotic mutation. The introducing of the self-adaptivechaotic mutation can improve the performance of genetic algorithm very prominently.

A Novel Evolutionary-Fuzzy Control Algorithm for Complex Systems

This paper presents an adaptive fuzzy control scheme based on modified genetic algorithm. In the control scheme, genetic algorithm is used to optimze the nonlinear quantization functions of the controller and some key parameters of the adaptive control algorithm. Simulation results show that this control scheme has satisfactory performance in MIMO systems, chaotic systems and delay systems.

面向多搬运任务的柔性制造车间多载具AGV节能路径规划

自动导引搬运车(automated guided vehicle,AGV)能够灵活、准确、高效地进行物料搬运,被广泛应用于柔性制造车间。多载具AGV能同时搬运多个工件,具有较强的搬运灵活性,其路径规划问题的复杂性和多约束性更强,求解难度更大。针对柔性制造车间多载具AGV节能路径规划问题,首先建立了以能耗和搬运距离为优化目标的AGV节能路径规划模型;然后,提出了一种改进Dijkstra算法和非支配排序遗传算法(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)集成的多载具AGV节能路径规划方法;最后通过案例验证了模型的节能效果和求解方法的有效性。

基于多目标优化的脱硝系统改进自抗扰控制

大范围变工况的燃煤机组脱硝系统因具有大惯性特性、强非线性以及多源扰动等特点,使其控制具有一定的挑战性。为提高脱硝系统的跟踪和抗干扰控制性能,并节约喷氨量,提出了基于控制性能指标和喷氨量指标的多目标优化串级改进自抗扰控制策略。在改进自抗扰控制原理的基础上,通过单一变量法分析改进自抗扰控制参数对控制效果的影响。采用多目标遗传算法优化脱硝系统改进自抗扰控制参数。通过仿真验证所提改进自抗扰控制在设定值跟踪、扰动抑制方面的优势,蒙特卡洛实验验证了所提改进自抗扰控制在应对系统不确定性时具有很强的鲁棒性。仿真结果表明所提改进自抗扰控制具有很好的实际应用潜力。

一种用于主动悬架LQG控制器设计权重优化的改进遗传算法

如何实现在不同行驶条件下汽车两大性能的最佳协调(即乘坐舒适性和操作稳定性)是主动悬架设计的关键。在LQG控制器设计中,性能指标的加权系数决定了汽车在当前条件下的各性能间的最佳平衡。而实际中需要考虑的因素来自多个方面,如不同路面的不平度、车辆行驶速度、车载质量、甚至还可能包括不同驾驶员偏好等,因而使得控制器设计中对权重系数的选取较难处理。针对这一难题,提出了一种改进的遗传算法优化方案。根据驾驶人(或乘员)对不同方面的性能需求,建立包含不同权值系数的适应度函数,针对适应度函数设计了典型的优化模式,并通过对惩罚函数值的适当选取来实现对性能指标加权系数的优化。通过不同工况及要求下的仿真,分析了该算法的可行性和有效性。研究结果表明,所提出的权值适应度函数优化方法简单、可行、有效。所设计的改进遗传算法能够高效地优化性能指标的加权系数以实现汽车在当前条件下的不同性能的最佳平衡,从而为主动悬架LQG控制器的设计提供一个有效的方法。

An Improved Immune Genetic Algorithm for Solving the Optimization Problems of Computer Communication Networks

Obtaining the average delay and selecting a route in a communication networkare multi-constrained nonlinear optimization problems . In this paper, based on the immune geneticalgorithm, a new fuzzy self-adaptive mutation operator and a new upside-down code operator areproposed. This improved IGA is further successfully applied to solve optimal problems of computercommunication nets.

改进量子遗传算法用于多峰值函数优化

传统遗传算法(SGA)在处理多峰值函数优化问题中存在局部收敛性的问题,最初的量子遗传算法(QGA)也存在这一问题。运用一种改进量子遗传算法(MQGA),有效地解决了一些多峰值函数的优化问题。根据几个重要的测试函数进行仿真实验结果证明,与SGA和QGA相比,改进的量子遗传算法(MQGA)在一些多峰值优化问题中更具有效性和可行性。

基于改进的粒子群遗传算法的DNA编码序列优化

在DNA计算中,DNA编码序列的设计是影响DNA计算可靠性的重要手段.在不同的DNA序列设计中,应该选择适当的约束条件,并且根据相应的约束条件提出每个DNA应该相应满足的评估公式.文中从DNA编码设计应满足的多约束条件中选取适当的约束条件,提出评估公式,并采用改进的粒子群遗传算法来解决多目标优化问题.同时根据得到的序列与已有序列在综合适应度函数结果上进行对比,结果证明了该方法的有效性.

基于改进遗传算法的汽车散热器优化设计

根据汽车散热器体积小、耗材少的设计要求,提出了相应的优化目标函数;选定散热器百叶窗翅片的波高、波距、水管横截面长、宽、芯体总宽等结构参数作为决策变量,采用遗传算法对其进行结构优化计算;通过对换热器遗传算法优化过程特点的研究,对基本遗传算法的种群初始化、选择算子、变异算子等作了一定的改进;为验证优化模型和改进遗传算法的正确性,编制了优化设计软件,优化实例显示优化后的散热器的换热面积比原型减少了31.2%,表明改进后的遗传算法用于汽车散热器的结构优化,能够取得较好的优化效果.