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.

An Improved Catastrophic Genetic Algorithm and Its Application in Reactive Power Optimization

This paper presents an Improved Catastrophic Genetic Algorithm (ICGA) for optimal reactive power optimization. Firstly, a new catastrophic operator to enhance the genetic algorithms’ convergence stability is proposed. Then, a new probability algorithm of crossover depending on the number of generations, and a new probability algorithm of mutation depending on the fitness value are designed to solving the main conflict of the convergent speed with the global astringency. In these ways, the ICGA can prevent premature convergence and instability of genetic-catastrophic algorithms (GCA). Finally, the ICGA is applied for power system reactive power optimization and evaluated on the IEEE 14-bus power system, and the application results show that the proposed method is suitable for reactive power optimization in power system.

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.

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]

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.

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.

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.

基于塔架主动阻尼控制的风电机组变速控制参数优化策略研究

考虑风电机组控制参数相互耦合,为解决控制参数不精确以及控制目标难以兼顾问题,提出一种基于塔架主动阻尼控制的自适应变速控制参数优化方法。首先,基于转矩-转速系统和塔架侧向主动阻尼控制完成数学和动力学建模,并计算主动阻尼增益初始值。其次,为便于整定PI控制参数和分析计算,针对惯性较大的转矩-转速系统采用Routh法将其辨识为低阶惯性系统,进而采用时间加权绝对误差积分准则整定变速系统PI控制参数初始值。最后,为保持最优控制状态,基于灾变遗传算法优化各风速点变速系统PI控制参数和主动阻尼增益,并将其分别与风速拟合构建自适应控制。算例分析通过比较频域特性、控制精度、塔架振动和载荷情况验证了所提方法的有效性。

基于关联规则和遗传算法的服装辅料储位优化

针对服装行业受时尚潮流影响,物料型号更新迅速,导致仓库库存结构混乱、作业效率低下的问题,设计一种将Apriori算法同改进遗传算法结合(AIGA)的储位优化方法。首先Apriori算法挖掘物料组间关联规则,将相关性较强物料组合并形成大类库区,按照大类拣货频次动态调整库区位置;其次结合物料相关性和拣货频次,以最小化拣货距离为主要优化目标建立储位分配模型。通过遗传算法进行储位分配搜索,并改进遗传算法的初始化、交叉和变异算子,同时设计灾变机制,提高算法搜索性能。结果表明,与现有储位分配方案相比,拣货距离平均缩短23.85%,有效提高仓库作业效率。

高峰时段郊区线考虑列车容量约束的停站方案优化方法研究

大城市郊区线路的客流往往具有明显的长距离出行和时空分布不均衡特征,尤其在高峰时段存在因列车容量约束导致乘客滞留站台的现象,而开行不同站停方式的列车是解决上述问题的有效手段。在计算上下车、进站/换乘及留乘等客流数量基础上,本文建立以包括候车时间和旅行时间在内的乘客总出行成本最小为目标的优化模型;以是否停站的0-1变量对所有列车进行统一编码,通过设置最大触发灾变次数,设计基于灾变思想的改进遗传算法;定义研究时段相邻列车在各车站发车间隔的偏差均值和偏差离差为评价运行图发车间隔均衡性的指标,实现考虑列车容量约束的停站方案优化。以北京地铁15号线为案例,结果表明:改进遗传算法通过灾变操作可有效跳出局部最优;算法可得到设计的3种场景条件下的最优停站方案,相较于场景1,场景2和场景3目标值分别提升12.20%和4.28%;采用跳停策略,可以有效减少列车运行时间,相较于场景1,场景2和场景3的下行方向在到达终点站时分别节省17.49 s和17.97 s。

基于改进遗传算法的AUV动态目标搜索算法

针对水下动态目标搜索问题,提出一种基于改进遗传算法的AUV动态目标搜索算法。采用蒙特卡罗统计方法生成大量目标运动轨迹,作为计算适应度依据;结合水声模型提出一种新型的累积探测概率计算方式;种群选择采用灾变思想与精英主义相结合方法,保证种群的非劣性和多样性,加速跳出局部极值;采用混沌序列方式选择交叉和变异点,增加种群随机性;采用动态自适应的交叉概率与变异概率,减少经验依赖,保证后期种群多样性。仿真实验结果表明,相较于传统算法和经典遗传算法,改进遗传算法能有效避免陷入局部极值,提高搜索概率。

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.

改进灾变遗传算法及其在无功优化中的应用

针对灾变遗传算法的早熟和稳定性问题,提出了一种改进灾变遗传算法,设计了与进化代数相关的改进灾变算子;为了兼顾算法的全局性能和收敛速度,设计了与进化代数相关的交叉概率和与个体适应度相关的变异概率.IEEE14节点和IEEE30节点无功优化算例表明,该改进算法具有良好的全局性能和收敛速度,适合求解电力系统的无功优化问题.

改进灾变遗传算法在无功优化规划中的应用

提出了一种改进灾变遗传算法。该算法针对常规灾变算子局部搜索能力不足和收敛性差的缺点,提出了当前进化中最优个体灾变范围的概念,比常规灾变原理缩小了灾变范围,提高了灾变的针对性。此外,为提高遗传算法的收敛性能,设计了与进化代数相关的交叉概率和与个体适应度相关的变异概率。IEEE30节点系统算例表明,该算法具有良好的全局性能、收敛速度和收敛稳定性,适合求解电力系统的无功优化问题。

灾变遗传算法在配电网开关优化配置中的应用

分析了开关投资、运行维修费用、停电损失和网络损耗的计算方法,基于等年值法建立了开关优化的含约束、非线性的组合优化模型,采用了灾变遗传算法来求解这类优化问题。通过对实例的计算表明:提出的数学模型和算法有较强的工程实用性,灾变遗传算法具有较好的全局收敛性和较快的收敛速度。

突发性灾害救援中心选址优化的模型与算法

提出了一种基于灾害发生概率、灾害扩散函数和救援函数的救援中心选址优化的数学模型.由于灾害的扩散和救援的功效都只能表达为时间的非线性函数,这种嵌入时间函数的优化问题很难由一般数学规划模型求解.提出一种基于嵌入启发式遗传算法作为模型求解方法.通过对大量源于实际的算例进行计算,取得了满意的结果.

遗传-灾变算法及其在非线性控制系统中的应用

本文提出了遣传一灾变算法。在遗传算法的基础上，提出进一步模拟自然界中的灾变现象，以提高遗传算法的性能，尤其是解决重要的不成熟收敛问题。文中介绍了它的基本原理并将其应用于非线性控制系统的优化设计中。

遗传灾变算法在配电网络重构中的应用

将灾变算法与遗传算法相结合,提出了应用于配电网络重构的遗传灾变算法。针对配电网的结构特征,采用基于邻接矩阵的供电孤岛验算方法,排除遗传操作后产生的不可行解,通过精英保留和动态控制变异算子,有效地解决了传统遗传算法的早熟收敛问题。应用所提出的算法对IEEE33节点系统和69节点系统进行了网络重构,并与传统遗传算法进行了比较,重构结果显示了遗传灾变算法的正确性、可行性和寻优突出性。

一种低能耗的片上网络映射算法

对于满足带宽约束的低能耗片上网络映射问题,提出一种基于灾变遗传退火的映射算法.该算法以标准遗传算法为基础,引入Boltzmann选择方法,对遗传操作后的较优个体采用多邻域的模拟退火操作进行优化,对处于停滞状态的种群使用灾变操作重新初始化部分较差个体,跳出局部极值.实验结果表明:与标准遗传算法相比,该算法具有优化性能好,收敛速度快的优点,映射结果比混沌遗传算法平均节能21.7%,有效地降低了片上网络系统通信能耗.