Banach空间Fuzzy多值隐拟变分包含

在Banach空间中并在不具紧性的条件下，引入和研究了一类fuzzy多值隐拟变分包含及相应的fuzzy隐预解算子方程，借助预解算子技巧，建立了Banach空间中fuzzy多值隐拟变分包含与预解算子方程的等价性，得到了该类fuzzy多值隐拟变分包含的迭代算法与某些解的存在性定理及解的迭代逼近。     

基于模糊度测度和改进遗传算法的图像分割

为了利用模糊集理论进行图像分割,本文提出了一种模糊度测度函数,它衡量了背景模糊集和某一个二值图像的相似性:模糊度越小,这种相似程度越大;并且对模糊度的计算只需要进行简单的比较运算和加法运算,便于硬件实现。本文利用遗传算法求取最佳阈值,针对遗传算法的某些不足之处.我们利用精英策略和模拟退火的思想,对其做了一些修正。实验结果表明:相对于Shannon熵方法和模糊熵方法,本文方法取得了较好的分割结果,并且具有较强的抗噪声能力。     

Design optimization of 3D steel structures: Genetic algorithms vs. classical techniques

In this paper an elitist genetic algorithm (GA) developed by the authors is compared with common commercial solutions for complex structural optimization. After its prior validation on two-dimensional (2D) structures, the GA was tuned and improved in order to obtain structural elements with minimum weights that satisfy the ultimate limit states of the applicable building code. Subsequently, the same spatial structures were optimized using a commercial structural analysis program. Finally, the cost and weight improvements obtained using the GA, although at a higher computational cost, are discussed.     

基于Petri网的供需链设计优化研究

供需链设计优化问题由于其自身的复杂性而成为一个极具挑战性的研究课题。本文把Petri网与遗传算法结合起来，用于研究供需链设计优化，构建一个供需链原型测试系统，并对小汽车制造供需链系统进行了实例分析。     

油田注水地面系统拓扑布局优化新方法

介绍了油田地面注水系统拓扑布局优化的数学模型及计算方法,举例计算表明,用遗传算法解决油田注水布局能取得良好的结果。     

Multiplicative Operator Splittings in Nonlinear Diffusion: From Spatial Splitting to Multiple Timesteps

Operator splitting is a powerful concept used in many diversed fields of applied mathematics for the design of effective numerical schemes. Following the success of the additive operator splitting (AOS) in performing an efficient nonlinear diffusion filtering on digital images, we analyze the possibility of using multiplicative operator splittings to process images from different perspectives.We start by examining the potential of using fractional step methods to design a multiplicative operator splitting as an alternative to AOS schemes. By means of a Strang splitting, we attempt to use numerical schemes that are known to be more accurate in linear diffusion processes and apply them on images. Initially we implement the Crank-Nicolson and DuFort-Frankel schemes to diffuse noisy signals in one dimension and devise a simple extrapolation that enables the Crank-Nicolson to be used with high accuracy on these signals. We then combine the Crank-Nicolson in 1D with various multiplicative operator splittings to process images. Based on these ideas we obtain some interesting results. However, from the practical standpoint, due to the computational expenses associated with these schemes and the questionable benefits in applying them to perform nonlinear diffusion filtering when using long timesteps, we conclude that AOS schemes are simple and efficient compared to these alternatives.We then examine the potential utility of using multiple timestep methods combined with AOS schemes, as means to expedite the diffusion process. These methods were developed for molecular dynamics applications and are used efficiently in biomolecular simulations. The idea is to split the forces exerted on atoms into different classes according to their behavior in time, and assign longer timesteps to nonlocal, slowly-varying forces such as the Coulomb and van der Waals interactions, whereas the local forces like bond and angle are treated with smaller timesteps. Multiple timestep integrators can be derived from the Trotter factorization, a decomposition that bears a strong resemblance to a Strang splitting. Both formulations decompose the time propagator into trilateral products to construct multiplicative operator splittings which are second order in time, with the possibility of extending the factorization to higher order expansions. While a Strang splitting is a decomposition across spatial dimensions, where each dimension is subsequently treated with a fractional step, the multiple timestep method is a decomposition across scales. Thus, multiple timestep methods are a realization of the multiplicative operator splitting idea. For certain nonlinear diffusion coefficients with favorable properties, we show that a simple multiple timestep method can improve the diffusion process.     

小生境遗传算法AVOA反演

采用具有水平对称轴的横向各向同性(HTI)模型研究裂缝性油气藏,P波振幅随炮检距和方位角变化(AVOA),表现出较强的方位各向异性特征。本文基于Ruger提出的HTI介质模型的P波反射系数公式,利用由基准测线和另外两条与之呈45°、90°测线的P波反射系数的相对差异估算裂缝方位;同时引入小生境遗传算法,对基准测线和与之呈90°测线的P波反射系数差进行反演,得到了较高精度的纵、横波波速比和Thomsen各向异性参数。     

A Multi-Objective Hybrid Genetic Based Optimization for External Beam Radiation

A multi-objective hybrid genetic based optimization algorithm is proposed according to the multi-objective property of inverse planning. It is based on hybrid adaptive genetic algorithm which combines the simulated annealing, uses adaptive crossover and mutation, and adopts niched tournament selection. The result of the test calculation demonstrates that an excellent converging speed can be achieved using this approach.     

Genetic algorithms for symbolic clustering

This paper introduces a novel methodology for clustering of symbolic objects by making use of Genetic Algorithms (GAs). GAs are a family of computational models inspired by evolution. These algorithms encode potential solutions to specific problems on simple chromosome-like data structures and apply recombination operators to these structures so as to preserve critical information. A new type of representation for chromosome structure is presented here along with a new method for mutation. The efficacy of the proposed method is examined by application to numeric data of known number of classes and also to assertion type of symbolic objects drawn from the domain of fat oil, microcomputers, microprocessors and botany. The validity of the clusters obtained is examined.