基于GA-Fuzzy的混沌系统辨识研究

提出用遗传算法优化的Takagi-Sugeno-Kang(TSK)模糊模型对未知或不确定的混沌动力学系统进行辨识。在辨识未知混沌系统的TSK模型过程中，只需利用未知混沌系统的输出时间序列。首先，采用模糊聚类分析方法从训练数据建立其初始TSK模糊模型。然后采用实数编码的遗传算法对初始模型进行优化设计。同时为防止破坏模糊规则的语义属性，对遗传搜索空间采取了适当的限制。用辨识模型重建吸引子方法定性地评价辨识模型，通过计算辨识模型的Lyapunov指数定量地评价辨识模型的性能。仿真结果表明，该辨识模型能很好地逼近原混沌动力学系统，准确地体现原混沌系统的动力学特性。     

Incremental learning of the triangular membership functions based on single-pass FCM and CHC genetic model

In order to improve the efficiency of learning the triangular membership functions (TMFs) for mining fuzzy association rule (FAR) in dynamic database,a single-pass fuzzy c means (SPFCM) algorithm is combined with the real-coded CHC genetic model to incrementally learn the TMFs.The cluster centers resulting from SPFCM are regarded as the midpoint of TMFs.The population of CHC is generated randomly according to the cluster center and constraint conditions among TMFs.Then a new population for incremental learning is composed of the excellent chromosomes stored in the first genetic process and the chromosomes generated based on the cluster center adjusted by SPFCM.The experiments on real datasets show that the number of generations converging to the solution of the proposed approach is less than that of the existing batch learning approach.The quality of TMFs generated by the approach is comparable to that of the batch learning approach.Compared with the existing incremental learning strategy,the proposed approach is superior in terms of the quality of TMFs and time cost.     

实数编码遗传操作机制的研究

分析了典型的实数编码遗传操作机制存在的缺陷，在此基础上提出了两种算法稳定、搜索效率高的遗传操作机制，针对3个著名的测试函数，应用这三种遗传操作机制分别进行了多次的性能测试，通过仿真结果的多方横、纵向比较分析，结果表明改进的遗传操作机制的有效性和合理性。     

Real coded genetic algorithm for stochastic hydrothermal generation scheduling

The intent of this paper is to schedule short-term hydrothermal system probabilistically considering stochastic operating cost curves for thermal power generation units and uncertainties in load demand and reservoir water inflows. Therefore, the stochastic multi-objective hydrothermal generation scheduling problem is formulated with explicit recognition of uncertainties in the system production cost coefficients and system load, which are treated as random variable. Fuzzy methodology has been exploited for solving a decision making problem involving multiplicity of objectives and selection criterion for best compromised solution. A real-coded genetic algorithm with arithmetic-average-bound-blend crossover and wavelet mutation operator is applied to solve short-term variable-head hydrothermal scheduling problem. Initial feasible solution has been obtained by implementing the random heuristic search. The search is performed within the operating generation limits. Equality constraints that satisfy the demand during each time interval are considered by introducing a slack thermal generating unit for each time interval. Whereas the equality constraint which satisfies the consumption of available water to its full extent for the whole scheduling period is considered by introducing slack hydro generating unit for a particular time interval. Operating limit violation by slack hydro and slack thermal generating unit is taken care using exterior penalty method. The effectiveness of the proposed method is demonstrated on two sample systems.     

一种新量子进化算法及其在函数优化中的应用

基于量子计算理论和进化理论,提出了一种新的量子进化算法-基于实数编码的量子进化算法(RQEA).不同于传统进化算法的单点编码和量子进化算法的量子比特编码,算法以实数矩形区域来表示基因,一条染色体携带多个个体信息.利用量子态叠加和相干机理,通过叠加、变异及自学习来完成进化过程.实验表明,该算法在函数优化上具有优异的性能.     

拥挤系统阻碍物位置的遗传算法优化

首先对Helbing等人建立的社会力模型进行了适当修正,建立了人群运动仿真模型;然后采用实数编码遗传算法并设计了自适应的交叉及变异策略,建立了优化阻碍物位置的数学模型;最后通过高性能计算平台进行仿真计算,得到了最有利于提高拥挤人群疏散速度的阻碍物设置.通过优化房间和人群所形成的拥挤系统中阻碍物的大小、数量和位置,有效提高了恐慌发生时房间内人群的逃生效率,该结果对建筑设计具有重要参考价值.