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.

Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation

In recent years, immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications. However, IGA with deterministic mutation factor suffers from the problem of premature convergence. In this study, a modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases, in which immune concepts are applied to determine the mutation parameters, is proposed to improve the searching ability of the algorithm and maintain population diversity. Performance comparisons with other well-known population-based iterative algorithms show that the proposed method converges quickly to the global optimum and overcomes premature problem. This algorithm is applied to optimize a feed forward neural network to measure the content of products in the combustion side reaction of p-xylene oxidation, and satisfactory results are obtained.

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.

EVOLUTIONARY FUZZY GUIDANCE LAW WITH SELF-ADAPTIVE REGION

Effective guidance is one of the most important tasks to the performance of air-to-air missile. The fuzzy logic controller is able to perform effectively even in situations where the information about the plant is inaccurate and the operating conditions are uncertain. Based on the proportional navigation, the fuzzy logic and the genetic algorithm are combined to develop an evolutionary fuzzy navigation law with self-adapt region for the air-to-air missile guidance. The line of sight (LOS) rate and the closing speed between the missile and the target are inputs of the fuzzy controller. The output of the fuzzy controller is the commanded acceleration. Then a nonlinear function based on the conventional fuzzy logic control is imported to change the region. This nonlinear function can be changed with the input variables. So the dynamic change of the fuzzy variable region is achieved. The guidance law is optimized by the genetic algorithm. Simulation results of air-to-air missile attack using MATLAB show that the method needs less acceleration and shorter flying time, and its realization is simple.[KH*3/4D]

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.

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.

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.

配电网节能改造优化建模研究

导线改造和无功补偿是实现配电网节能降耗的两项关键措施。提出了一种导线更换和中低压无功配置协同优化的配电网节能改造优化模型,以年总支出费用最小为目标,以导线更换线径、各种无功补偿组数和容量为控制变量,满足各种运行约束和安装维护约束,并采用分组整数编码的灾变遗传算法进行求解。以某地区实际配电线路改造为例,分别计算仅按经济电流密度进行导线更换、仅进行中低压无功补偿和将导线更换与中低压无功配置协同优化这三种改造方案的效益,结果表明提出的模型具有更好的降损效益和应用价值。

基于改进遗传算法的配电网无功优化规划

以年综合费用最小为目标,建立了考虑系统多负荷水平的配电网无功优化规划数学模型;提出了在灵敏度分析的基础上以负荷功率阻抗矩法确定补偿节点,利用灵敏度分析和灾变思想改进遗传算法的配电网无功规划优化方法;该方法加强了遗传交叉、变异操作的针对性,提高了算法的收敛速度;算例结果表明了其有效性和实用性。

灾变算子在遗传算法中的作用研究

模拟生物进化过程中导致大量物种灭绝而个别物种幸存的灾变现象,灾变算子在进化操作几十代后除了当前最好解留下来外,重新随机产生其他个体。该文通过分析和实验表明,采用灾变算子可以提高遗传算法小规模群体的多样性,从而避免早熟收敛。

大变异遗传算法在非线性系统参数估计中的应用

针对传统非线性系统模型参数估计方法的局限性及基本遗传算法存在的"早熟收敛"问题,本文提出了基于大变异操作的改进遗传算法来进行非线性系统参数的辨识,并将其应用到活性污泥动态模型的参数估计中,取得了较好的效果。

一种基于CHC算法的自动组卷方法

利用改进的遗传算法——跨世代异物种重组大变异(cross generation heterogeneous recombination cata-clysmic mutation,CHC)算法提出了一种自动组卷方法。初始种群即初始试卷集利用具有启发式信息的搜索算法产生;适应度函数是用户指定的试卷总体指标与试卷实际指标绝对误差的加权和;选择操作群体为当前群体与上世代群体的群体总和,因为大个体群操作可以更好地保持遗传多样性;交叉操作采用单点交叉方法。变异操作的步骤是:从上世代个体中挑选适应度较差的个体,对其中的若干个个体选择一定比例的基因座,随机地决定它们的位值。由于考虑到了约束条件的限制,从而避免了盲目性且加快了收敛速度。实验结果表明该方法比基本遗传算法要快而且满足最优条件。

解复杂连续函数优化问题的动态量子遗传算法

研究了一种解复杂连续函数优化的动态量子遗传算法(DQGA)。设计一种动态量子旋转角的更新策略及量子门调整策略,以加快算法收敛速度,同时为淘汰适应度差的个体,量子旋转策略表中动态地嵌入了变异算子。在算法进化后期引入灾变算子使算法及时跳出局部最优,避免早熟收敛。五个复杂连续函数的测试实验表明:所提算法对复杂连续函数优化问题的寻优能力较QGA更强,算法的稳定性更高,算法的迭代次数亦优于传统量子遗传算法。

应用于电力系统无功规划的一种新算法

电力系统无功优化规划属于非线性混合优化问题,其控制变量既有连续变量又有离散变量,如果不能实现离散变量的精确处理,将导致优化结果不符合电力系统的实际.遗传算法是解决多目标混合优化问题的全局优化算法,但是具有优化时间长,易于收敛于局部极值点等不足,为此提出了灾变遗传算法,可以显著提高无功优化规划的计算速度和搜索精度,优化结果比传统遗传算法更优.

基于改进遗传算法的BISQ模型多参数反演方法研究

BISQ模型反演在油气资源与海洋地震勘探中具有广阔的应用前景与实用价值.为解决具高维参数与多峰值的BISQ模型参数反演问题,本文在传统的遗传算法基础上添加插入了全局保优机制、Boltzmann生存机制、小生境之DC漂移技术和群体灾变等方法与技术,使改进组合的遗传算法能在高维参数与多峰值的反问题中搜索得到全局最优解.结合海底多频原位沉积物声学测量数据,应用本文方法,得到了与声波传播速度相似性最优模型的各参数值,效果明显.

基于CHC算法的无人机航迹规划方法

利用改进的遗传算法——跨世代异物种重组大变异(CHC,Cross generationHeterogeneous recombination Cataclysmic mutation)算法提出了一种无人机的航迹规划方法.初始种群即初始航线集利用具有启发式信息的搜索算法产生;适应度函数为距离指标与威胁指标的组合形式;选择操作群体为当前群体与上世代群体的群体总和,由于大个体群操作,可以更好地保持遗传多样性;交叉操作采用单点交叉方法,交叉点取为2条航线中距离最近的2个点;变异操作的步骤是:首先在航线中搜索出2个点,然后算出这2个点之间的直线距离与实际航线距离的比值,如果这个比值小于某一阈值则以这2个点为端点重新规划一条航线.由于考虑到了无人机约束条件的限制,从而避免了盲目性且加快了收敛速度.仿真结果表明该方法比基本遗传算法要快而且满足最优条件.

地区电网日有功调度模型探讨

厂网分开后,电网公司有功调度已由单纯的电能供需平衡,过渡到以追求全网总购电成本最小为目标。这里提出一种以电网在购买机组有功出力时,单位时间内花费最小为目标函数,加上各种物理和安全约束的有功调度模型。通过灾变遗传算法进行求解算例,证明模型具有简明、合理的特点,对电网有功优化经济调度具有参考意义。

基于自适应灾变遗传-循环神经网络的锂离子电池SOC估计

锂离子荷电状态(State of charge,SOC)的精准估计是锂离子电池安全稳定运行的基础。传统的误差反向传播(Back propagation,BP)神经网络估计SOC的精度不高,而循环神经网络(Recurrent neural network,RNN)也容易陷入局部最优。针对这些问题,提出了自适应灾变遗传-循环神经网络(ACGA-RNN)联合算法,将自适应灾变遗传算法(Adaptive cataclysm genetic algorithm,ACGA)用于优化RNN的初始权值和阈值,提高了最优权值和阈值的全局搜索能力,从而有效提升锂离子电池SOC的估计精度。基于锂离子电池充放电的试验数据,将所提ACGA-RNN联合算法与RNN、GA-RNN算法分别用于锂离子电池的SOC估计。测试结果显示,相较于传统的RNN算法与GA-RNN算法,提出的ACGA-RNN联合算法获得了最佳的SOC估计精度,在DST工况下的估计平均绝对误差为1.74%,低于传统RNN和GA-RNN的估计精度3.68%和2.49%;另外,在45℃和0℃条件下,ACGA-RNN联合算法估计的平均绝对值误差分别为1.75%和2.05%,符合国家标准要求。因此,提出的ACGA-RNN联合算法在锂电池的SOC估计方面具有良好的应用价值。

一种求解Job Shop调度问题的改进遗传算法

传统遗传算法在求解Job Shop调度问题时存在收敛速度慢,易于早熟的缺点。在病毒遗传算法(VEGA)和灾变遗传算法的基础上提出了一种带有灾变因子的病毒遗传算法(IVEGA-C)。该算法在传统遗传算法的基本结构上加入了病毒感染操作和灾变操作,病毒感染操作实现了同代个体之间横向传递进化信息,灾变操作采用灭绝操作。正是这种改进加快了遗传算法的收敛速度,避免了早熟现象和陷入局部最优解。通过仿真实验验证了IVEGA-C算法在解决Job Shop调度问题中的性能优于传统GA算法和VEGA算法。最后给出了应用该算法的一个实例。