基于有效抽头和进化规划算法的自适应水声信道估计

为了实现稀疏水声信道的快速高效估计,提出一种新的基于有效抽头的信道参数模型降低运算复杂性,并采用进化算法以最小二乘误差为适应度函数对信道响应中有效抽头的阶数、位置和权重同时进行进化操作寻优,避免了有色输入造成的相邻抽头耦合导致的性能下降。仿真实验结果表明:与传统LMS算法及基于有效抽头检测的稀疏信道估计方法相比,该算法在有色输入信号下具有更优越的估计性能和收敛速度,为稀疏水声信道处理提供了一类新方法。     

基于进化规划的自适应高斯神经网络及其在被动声纳目标识别中的应用

自适应高斯神经网络能够对目标信号的功率谱有效识别特征进行自动提取和分类,但此网络使用BP算法,其误差能量函数是一个不规则的超曲面,容易陷入局部极小值.因此,提出了一种使用进化规则来设计和训练自适应高斯神经网络的新方法,该方法能够自动的确定网络的最优结构和联结权值,同时避免网络的局部优化.将该方法用于被动声呐目标的分类识别,实验结果表明基于进化规则的自适应高斯神经网络能够有效的克服局部最小问题,具有更好的识别率.     

量子进化算法

进化算法是解决优化问题的一种有效方法,但在实际应用中也存在收敛速度慢、早熟等问题,大大影响了其应用效果。本文将进化算法和量子理论结合,提出一种新的理论框架-量子进化理论及其学习算法。算法借鉴量子理论,采用量子染色体的表示形式,能使一个染色体同时表示多个状态;模拟量子坍塌的随机观察能带来丰富的种群;同时构造具有量子特点的交叉变异算子,能在防止算法早熟的同时使算法更快收敛。本文不仅从理论上证明了这一理论框架的全局收敛性, 仿真计算也表明了此算法的优越性。     

神经网络的进化学习方法研究

提出了两种用于前向神经网络的进化学习算法,实验证明它们能有效地网络权值空间中寻找全局最优解。在比较实验的基础上,得出了在神经网络的进化学习过程中变异是起主导作用的遗传算子的结论,并以此为指导配置算法的各个关键参数。通过对ＸＯＲ问题和ＩＲＩＳ模式分类问题的学习证明,这两种算法均能获得远高于传统的ＢＰ算法的性能。     

基于进化规划的自适应高斯神经网络

自适应高斯神经网络能够对目标信号的功率谱有效识别特征进行自动提取和分类，但此网络使用BP算法，其误差能量函数是一个不规则的超曲面，容易陷入局部极小值．因此，提出了一种使用进化规则来设计和训练自适应高斯神经网络的新方法．该方法能够自动地确定网络的最优结构和联结权值，同时避免网络的局部优化．实验结果表明，将该方法用于被动声纳目标的分类识别，能够有效地克服局部最小问题，具有更好的识别率．     

高斯变异差分进化算法及其应用研究

为平衡算法收敛速度和全局搜索能力,克服差分进化算法易“早熟”的缺陷,在分析引起种群多样性下降及个体进化停滞原因的基础上,通过引入高斯变异操作,提出了基于高斯变异改进的差分进化算法(modified differential evolution base on Gauss mutation,GMDE).数值仿真及2个工程优化问题的求解结果表明本文算法能有效避免“早熟”收敛,且在算法收敛速度和全局搜索能力上取得了较好的平衡.     

非线性混合整数规划问题的改进差分进化算法

针对非线性混合整数规划问题,本文采用非固定多段映射罚函数法处理约束条件、用混合整数编码技术处理连续变量和整数变量,并在基本差分进化算法中加入一种新型的凸组合变异算子和一种指数递增交叉算子,由此构造出了一种求解非线性混合整数规划问题的改进差分进化算法。实验表明,所提出的算法全局收敛速度快,精度高,鲁棒性强。     

进化计算在生产调度问题中的应用

本文首先简要介绍了进化计算方法的基本思想和特点,然后对遗传算法求解度问题的某些策略和基本步骤作了简要的归纳和总结,并且初步提出了应用进化策略和进化规划求解调度问题的一般方法。     

弯矩剪力等比数列收敛法

本文所介绍的弯矩剪力等比数列收敛法是以弯矩剪力分配法为基础,通过采用适当的弯矩、剪力的传递方式.用一组等比数列对结构中各杆端的弯矩、剪力进行收敛计算求得精确解的方法;还可解决在弯矩剪力分配法进行结构分析计算时,结构杆端的弯矩,剪力收敛速度慢的问题。     

天然材料，动力与进化并存

很明显，除去对人造产品的渴望之外，对自然产品的创新也可以让我们利用这些产品在越来越多的地方满足我们的需要。[编者按]     

Optimum setting of PID controllers based on using evolutionary programming algorithm

Abstract

This paper is concerned with the optimum setting problem for deriving gains in proportional‐integral‐derivative (PID) controllers. The evolutionary programming (EP) algorithm has been considered as a useful technique for finding global optimization solutions for certain complicated functions in recent years. Therefore, in this paper, we attempt to use the EP algorithm in PID control systems design for deriving optimal or near optimal PID control gains such that a performance index of integratedabsolute error (IAE) is minimized. It can be easily seen from the simulation results that the proposed method will have better performance than those presented in other studies by testing a fourth‐order system with time delay.     

Tensor objects in finite element programming

This paper describes a novel programming tool, nDarray, which is designed using an Object Oriented Paradigm (OOP) and implemented in the C++ programming language. Finite element equations, represented in terms of multidimensional tensors are easily manipulated and programmed. The usual matrix form of the finite element equations are traditionally coded in FORTRAN, which makes it difficult to build and maintain complex program systems. Multidimensional data systems and their implementation details are seldom transparent and thus not easily dealt with and usually avoided. On the other hand, OOP together with efficient programming in C++ allows building new concrete data types, namely tensors of any order, thus hiding the lower level implementation details. These concrete data types prove to be quite useful in implementing complicated tensorial formulae associated with the numerical solution of various elastic and elastoplastic problems in solid mechanics. They permit implementing complex nonlinear continuum mechanics theories in an orderly manner. Ease of use and the immediacy of the nDarray programming tool in constitutive driver programming and in building finite element classes will be shown. © 1998 John Wiley & Sons, Ltd.     

Investigating replenishment policies for centralised and decentralised supply chains using stochastic programming approach

In this paper, a multiple period replenishment problem based on (s, S) policy is investigated for a supply chain (SC) comprising one retailer and one manufacturer with uncertain demand. Novel mixed-integer linear programming (MILP) models are developed for centralised and decentralised decision-making modes using two-stage stochastic programming. To compare these decision-making modes, a Monte Carlo simulation is applied to the optimization models’ policies. To deal with demand uncertainty, scenarios are generated using Latin Hypercube Sampling method and their number is reduced by a scenario reduction technique. In large test problems, where CPLEX solver is not able to reach an optimal solution in the centralised model, evolutionary strategies (ES) and imperialist competitive algorithm (ICA) are applied to find near optimal solutions. Sensitivity analysis is conducted to show the performance of the proposed mathematical models. Moreover, it is demonstrated that both ES and ICA provide acceptable solutions compared to the exact solutions of the MILP model. Finally, the main parameters affecting difference between profits of centralised and decentralised SCs are investigated using the simulation method.     

Reliability and decomposition techniques to solve certain class of stochastic programming problems

Reliability based techniques has been an area of active research in structural design during the last decade, and different methods have been developed. The same has occurred with stochastic programming, which is a framework for modeling optimization problems involving uncertainty. The discipline of stochastic programming has grown and broadened to cover a wide range of applications, such as agriculture, capacity planning, energy, finance, fisheries management, production control, scheduling, transportation, water management, etc., and because of this, techniques for solving stochastic programming models are of great interest for the scientific community. This paper presents a new approach for solving a certain type of stochastic programming problems presenting the following characteristics: (i) the joint probability distributions of random variables are given, (ii) these do not depend on the decisions made, and (iii) random variables only affect the objective function. The method is based on mathematical programming decomposition procedures and first-order reliability methods, and constitutes an efficient method for optimizing quantiles in high-dimensional settings. The solution provided by the method allows us to make informed decisions accounting for uncertainty.     

A non‐dominance‐based online stopping criterion for multi‐objective evolutionary algorithms

A non‐dominance criterion‐based metric that tracks the growth of an archive of non‐dominated solutions over a few generations is proposed to generate a convergence curve for multi‐objective evolutionary algorithms (MOEAs). It was observed that, similar to single‐objective optimization problems, there were significant advances toward the Pareto optimal front in the early phase of evolution while relatively smaller improvements were obtained as the population matured. This convergence curve was used to terminate the MOEA search to obtain a good trade‐off between the computational cost and the quality of the solutions. Two analytical and two crashworthiness optimization problems were used to demonstrate the practical utility of the proposed metric. Copyright © 2010 John Wiley & Sons, Ltd.     

动态规划在运动图像分析中的应用

对运动图像序列中的目标进行运动跟踪和估计,可以分别采用变形模型和弹性配准的方法,两问题的求解均可归纳为成本函数的最优化问题。提出了应用动态规划算法求解离散成本函数最优化的方法,把问题分解成多个子问题分别求解,并存储子问题的解以避免重复计算。该方法不仅可保证解的全局最优性,而且计算量小,有利于实现实时处理。在对临床X射线冠状动脉造影图像序列的实验中得到了满意的结果,匹配大约2000个点的血管骨架只需要一秒钟左右的时间。对运动场已知的模拟图像的实验证明运动估计误差小于1个像素（1像素=0．3mm）。     

A tutorial on geometric programming

A geometric program (GP) is a type of mathematical optimization problem characterized by objective and constraint functions that have a special form. Recently developed solution methods can solve even large-scale GPs extremely efficiently and reliably; at the same time a number of practical problems, particularly in circuit design, have been found to be equivalent to (or well approximated by) GPs. Putting these two together, we get effective solutions for the practical problems. The basic approach in GP modeling is to attempt to express a practical problem, such as an engineering analysis or design problem, in GP format. In the best case, this formulation is exact; when this is not possible, we settle for an approximate formulation. This tutorial paper collects together in one place the basic background material needed to do GP modeling. We start with the basic definitions and facts, and some methods used to transform problems into GP format. We show how to recognize functions and problems compatible with GP, and how to approximate functions or data in a form compatible with GP (when this is possible). We give some simple and representative examples, and also describe some common extensions of GP, along with methods for solving (or approximately solving) them.     

A convergence analysis of the performance of the DRM‐MD boundary integral approach

In this article, we study the performance of the dual reciprocity multi‐domains approach (DRM‐MD) when the shape functions of the boundary elements, for both the approximation of the geometry and the surface variables of the governing equations, are quadratic functions. A series of tests are carried out to study the consistency of the proposed boundary integral technique. For this purpose a limiting process of the subdivision of the domain is performed, reporting a comparison of the computed solutions for every refining scheme. Furthermore, the DRM‐MD is solved in its dual reciprocity approximation using two different radial basis interpolation functions, the conical function r plus a constant, i.e. (1+r), and the augmented thin plate spline. Special attention is paid to the contrast between numerical results yielded by the DRM‐MD approach using linear and quadratic boundary elements towards a full understanding of its convergence behaviour. Copyright © 2006 John Wiley & Sons, Ltd.