基于学习的进化规划算法

提出基于学习的进化规划算法，用以改进普通进化规划算法的性能，该算法－方面通过学习种群整体的进化信息用以改善种群整体性能，具有大范围快速搜索的特点，另一方面该算法强调学习种群中个体的进化信息，单一个体以当前代的最优化个体作为学习目标，用以加大当前最优解附近的搜索力度，具有局部“细搜”的特点，该进化规划算法不仅能够加快算法的收敛速度，而且能够有效地保证种群的多样性，用该方法可求解具有多个极值点的函数优化问题，计算要仿真实验结果表明该方法是非常有效的。     

基于进化规划的区间系统卡尔曼滤波

对不确定系统的最优鲁棒滤波问题, 提出了一种新的方法———进化规划(EP)卡尔曼滤波. 该方法采用EP全局寻优技术, 搜索最优估计区间; 也采用EP全局最优技术, 确定工程中可实现的最优估计标称值. 这种新方法的假设条件与标准的卡尔曼滤波完全相同, 并具有标准的卡尔曼滤波相同的递推结构、相同的最优性. 最后, 给出计算仿真的例子, 并与文 [1]的仿真结果进行了比较. 结果表明, 本文提出的新方法更精确, 有较少的保守性.     

Evolutionary Computation Techniques for Nonlinear Programming Problems

This paper presents several evolutionary computation techniques and discusses their applicability to nonlinear programming problems. On the basis of this presentation we discuss also a construction of a new hybrid optimization system, Genocop 11, and present its experimental results on a few test cases (nonlinear programming problems).     

进化规划算法的时间复杂度分析

进化规划算法是求解连续优化问题的一类进化算法,是进化计算的一个重要分支.在进化规划算法的理论研究上,已有学者证明了其收敛性.然而,进化规划算法的时间复杂度分析是进化计算领域一大难题,目前相关的研究成果很少.基于吸收态Markov过程模型,以期望收敛时间作为研究进化规划算法时间复杂度的指标,提出了进化规划算法期望收敛时间的估算方法,并以此作为算法时间复杂度分析的理论依据.最后分析了Gauss变异进化规划算法的期望收敛时间,作为提出理论的应用举例.     

Evolutionary Modeling of Systems of Ordinary Differential Equations with Genetic Programming

This paper describes an approach to the evolutionary modeling problem of ordinary differential equations including systems of ordinary differential equations and higher-order differential equations. Hybrid evolutionary modeling algorithms are presented to implement the automatic modeling of one- and multi-dimensional dynamic systems respectively. The main idea of the method is to embed a genetic algorithm in genetic programming where the latter is employed to discover and optimize the structure of a model, while the former is employed to optimize its parameters. A number of practical examples are used to demonstrate the effectiveness of the approach. Experimental results show that the algorithm has some advantages over most available modeling methods.     

解决混合整数非线性规划问题的混沌局部搜索差分进化算法

为提高差分进化算法的局部搜索能力和避开罚函数方法中罚参数选择问题,提出一种混沌局部搜索策略的差分进化算法(CLSDE)用于解决非线性混合整数规划问题.CLSDE中,只对目标函数中的变量进行编码,约束条件函数中的变量随机产生,每代进化完毕后,对最优个体进行混沌局部搜索.6个基本的测试函数实验结果证明CLSDE比MIHDE具有较好的寻优能力.     

基因表达式程序设计在信息系统建模预测中的应用

基因表达式程序设计(GEP)是一种基于基因组和表现型组的新型遗传算法,该算法具有很好的健壮性和高效性采用基因表达式的方法进行演化建模,实例测试的结果表明使用基因表达式程序设计的方法得到的模型要优于普通的线性回归方法和传统的遗传程序设计方法得到的模型,提高了拟合和预测精度。     

进化规划模糊算法在PID控制中的应用

针对非线性控制系统,利用进化规划模糊算法,对PID控制器规则基的参数进行离线优化,优化后的模糊控制规则可对系统实现实时控制。该控制算法无需任何先验知识和量化因子,具有很强的数据挖掘能力,且模糊规则基的寻优速度较快。通过对非线性系统进行仿真,验证了该算法的有效性,与传统固定参数PID控制方法及遗传算法整定参数PID控制方法相比,明显地提高了系统的稳定性和动态性能。     

基于遗传编程的HFC-ADM算法在滤波器进化设计中的应用

该文在电路进化设计中使用遗传编程和键合图结合的进化设计方法,并且引入了分等级公平竞争(HFC)模型,采用自适应阈值控制不同等级之间的迁移,并结合动态的参数与拓扑空间进化设计了一个模拟滤波器。实验结果表明该算法不仅扩大了搜索空间,还有较强的持续进化能力,进一步证明了该方法的可行性和有效性。     

多目标进化算法中选择策略的研究

在多目标进化算法(multiobjective evolutiorlsry algorithms,MOEAs)的文献中,对算法的选择策略进行系统研究的还很少,而MOEAs的选择策略不仅引导算法的搜索过程、决定搜索的方向而且对算法的收敛性有重要的影响,它是算法能否成功求解多目标优化问题的关键因素之一.在统一的框架下,首先讨论了多目标优化问题中适应度函数的构造问题,然后根据MOEAs的选择机制和原理将它们的选择策略重新分成了6种类型.一般文献中很少对多目标进化算法的操作算子采用符号化描述,这样不利于对算子的深层次理解,符号化描述了各类选择策略的操作机制和原理,并分析了各类策略的优劣性.最后,从理论上证明了具备一定特征的多目标进化算法的收敛性,证明的过程表明了将算法运行终止时得到的P known作为多目标优化问题的Pareto最优解集或近似最优解集的合理性.     

基于自适应动态规划的一类带有时滞的离散时间非线性系统的最优控制策略

针对一类状态和控制变量均带有时滞的非线性系统的带有二次性能指标函数最优控制问题, 本文提出了一种基于新的迭代自适应动态规划算法的最优控制方案. 通过引进时滞矩阵函数, 应用动态规划理论, 本文获得了最优控制的显式表达式, 然后通过自适应评判技术获得最优控制量. 本文给出了收敛性证明以保证性能指标函数收敛到最优. 为了实现所提出的算法, 本文采用神经网络近似性能指标函数、计算最优控制策略、求解时滞矩阵函数、以及给非线性系统建模. 最后本文给出了两个仿真例子说明所提出的最优策略的有效性.     

一类改进进化规划及其优化性能分析

为了避免传统进化规划易早熟收敛的缺点,均衡探索和开发能力,论文采用多种群进行并行搜索,采用多变异操作增强搜索模板,采用整体概率性替换策略避免对适配值函数的依赖性以及避免搜索过程陷入局部极小,尤其采用个体重分配策略使各子群进行信息融合来增加多样性,由此提出了一类改进进化规划,并讨论了算法的函数优化和组合优化实现,同时基于典型算例的数值仿真验证了算法的有效性。     

Data-Based Optimal Tracking of Autonomous Nonlinear Switching Systems

In this paper,a data-based scheme is proposed to solve the optimal tracking problem of autonomous nonlinear switching systems.The system state is forced to track the reference signal by minimizing the performance function.First,the problem is transformed to solve the corresponding Bellman optimality equation in terms of the Q-function(also named as action value function).Then,an iterative algorithm based on adaptive dynamic programming(ADP)is developed to find the optimal solution which is totally based on sampled data.The linear-in-parameter(LIP)neural network is taken as the value function approximator.Considering the presence of approximation error at each iteration step,the generated approximated value function sequence is proved to be boundedness around the exact optimal solution under some verifiable assumptions.Moreover,the effect that the learning process will be terminated after a finite number of iterations is investigated in this paper.A sufficient condition for asymptotically stability of the tracking error is derived.Finally,the effectiveness of the algorithm is demonstrated with three simulation examples.     

Contribution to Design of Complex Mechatronic Systems. An Approach through Evolutionary Optimization

This paper describes a new evolutionary methodology aimed at optimizing various and heterogeneous data in common evolution. The representation of solutions uses mixed-integer genotypes and variable-length chromosomes to face a complex problem of task decomposition and high-level control generation. A memory operator is introduced to face convergency uncertainties issued from the irregularities of both discontinuous evaluation function and heterogeneous solution representation. The stability of the evolutionary algorithm is analyzed with dimension changes in the optimization problem.     

应用线性规划模型调整作物生产结构

东陵区是沈阳市郊区。过去,由于农作物生产结构不合理,致使农产品的商品率和经济效益都比较低,1983年以来,进行了资源调查和农业区划。1986年又在调查和区划所收集的资料和数据基础上,应用线性规划建立数学模型,调整作物结构,形成了新的作物生产规划。按照新的规划全区种植业净产值可达11309.9万元,比1983～1985年三年平均净产值增加2004.9万元,粮豆总产可达4.3亿斤,蔬菜总产量可达6.3亿斤以上,保证了满足供应城市和自给生产的需要。     

Towards an Efficient Path-Oriented Tool for Bounded Reachability Analysis of Linear Hybrid Systems using Linear Programming

The existing techniques for reachability analysis of linear hybrid automata do not scale well to problem sizes of practical interest. Instead of developing a tool to perform reachability check on all the paths of a linear hybrid automaton, a complementary approach is to develop an efficient path-oriented tool to check one path at a time where the length of the path being checked can be made very large and the size of the automaton can be made large enough to handle problems of practical interest. This approach of symbolic execution of paths can be used by design engineers to check important paths and thereby, increase the faith in the correctness of the system. Unlike simple testing, each path in our framework represents a dense set of possible trajectories of the system being analyzed. In this paper, we develop the linear programming based techniques towards an efficient path-oriented tool for the bounded reachability analysis of linear hybrid systems.     

Time-Optimal Coordination of Flexible Manufacturing Systems Using Deterministic Finite Automata and Mixed Integer Linear Programming

Automation and flexibility are often mentioned as key concepts in modern production industry. To increase the level of flexibility, deterministic finite automata (DFA) can be used to model, specify and verify the production systems. Often, it is also desirable to optimize some production criteria, such as for example the cycle time of a manufacturing cell. In this paper, a method for automatic conversion from DFA to a mixed integer linear programming (MILP) formulation is first presented. This conversion is developed for a number of DFA structures that have shown to be useful in practical applications. Special attention is paid to reducing the search region explored by the MILP solver. Second, a conversion from the MILP solution to a DFA supervisor is described. This allows to combine the advantages of DFA modeling with the efficiency of MILP and supervisory control theory to automatically generate time-optimal, collision-free and non-blocking working schedules for flexible manufacturing systems.

变结构控制实现非线性系统的输出调节和鲁棒输出调节

对于确定性的和受到结构扰动的非线性系统，用变结构控制设计的控制律能够满足到达条件，构造的切换面能产生稳定的滑动模态。当非线性系统在切换面上运动时，可以实现输出调节和鲁棒输出调节。     

用遗传网络校正传感器非线性误差的研究

文章描述了一种用反函数校正传感器非线性误差的方法。阐述了校正原理，提出了利用BP神经网络和遗传算法相结合，拟合传感器传输特性反应函数的算法，该算法可将传感器传输特性的非线性模型，改造成为与实际物理过程相一致的不失真的线性模型，给出了一个应用实例，其结果表明，可使传感器的非线性误差有较大的减少。     

An Adaptive Control Using Fuzzy Basis Function Expansions for a Class of Nonlinear Systems

An adaptive control using fuzzy basis function expansions is proposed for a class of nonlinear systems in this paper. It is shown that two system uncertainty bounds are approximated in a compact set by using fuzzy basis function expansion networks in the Lyapunov sense, and the outputs of the fuzzy networks are then used as the parameters of the controller to adaptively compensate for the effects of system uncertainties. Using this scheme, not only strong robustness with respect to unknown system dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference output can be guaranteed to asymptotically converge to zero. Simulation results are provided to demonstrate the effectiveness, simplicity and practicality of the proposed control scheme.