进化论动态参量自适应消噪算法

由于以输出信号平均能量的倒数为适应度函数、以及定值克隆系数和定值匹配系数的因素,制约了传统进化论自适应消噪算法的收敛特性,影响了传统进化论自适应算法降噪的有效性。针对该问题,提出了基于动态适应度函数的进化论变系数自适应消噪算法。新算法中,匹配系数与进化代数相关联,克隆系数由进化代数和适应度值决定,并采用动态适应度函数,改善了滤波器的收敛特性和噪声抑制能力。模拟仿真分析表明,新算法较同类进化论算法有较快的收敛速度和良好的消噪效果。物理台架实验数据验证了该算法对故障信号的有效提取能力。     

基于遗传算法的自适应噪声抵消

当有参考噪声信号时，自适应噪声抵消的实质就是求参考噪声输入通路的逆滤波器，LMS自适应滤波问题就是一个多变量函数的极值问题。LMS算法因其具有算法简单，容易实现的优点而为常用，但是算法的收敛特性和失调量受到步长参数μ的影响。而步长参数μ和最优值不易确定。遗传算法是一种应用于大规模搜索空间的有效方法，它不要求函数的解析表达式，只根据已知的测量数据便可以求得全局极值。本文以FIR滤波器为例。采用改进的实值编码遗传算法，将遗传算法用于逆滤波器的求解。计算机仿真结果表明该算法对噪声抵消取得了较满意的效果。     

人工神经网络应用于适应滤波的研究

目的 用神经网络实现Adaline自适应滤波权值调节。方法 在Adaline自适应滤波器中,滤波器的权值调节是通过LMS自适应算法进行调整的,但LMS的自适应算法的收敛速度慢,从而大大影响了Adaline自适应滤波器的滤波性能,作者利用TH网络进行TH－Adaline滤波器系统辨识。并给出了Adaline滤波器权值输出变化曲线的仿真结果。结果与结论 利用TH神经网络来实现滤波器权值调节的Adaline自适应滤波器运算速度比运用LMS算法的Adaline自适应滤波器的收敛速度快。     

自适应BPSK解调方法研究

提出了一种新型的基于自适应滤波算法的解调二进制相移键控（BPSK）信号的方法。以常用的最小均方误差自适应算法（LMS）为例，讨论了新型的BPSK自适应解调的过程及其性能。该解调算法不需要自适应滤波器完成收敛，从而降低了采样频率。给出的理论性能与仿真结果表明，BPSK自适应解调的误码率仿真结果与理论值吻合非常好；而且该方法具有抗干扰性能强、输出响应快、便于数字信号处理（DSP）技术实现等特点，在相同的采样频率下其误码率优于相关解调的误码率。     

一种变步长LMS自适应滤波算法研究

为了解决最小均方(LMS)算法的稳定性及收敛速度(自适应速度)和稳态误差(自适应滤波器的精度)之间的矛盾,本文提出了一种自适应变步长的LMS算法,它的权系数的调整取决于误差曲面在新权值点上的梯度.分析了新算法的收敛特性以及参数选择对算法性能的影响.该算法具有较快的收敛速度、鲁棒稳定性且运算小易于实现的特点.计算机仿真的结果证实了该算法的收敛性能优于标准的LMS算法并且具有较好的实用性.     

一种自适应EPF算法及其在光纤水听器中的应用

基于椭圆拟合的相位生成载波(Phase Generated Carrier,PGC)解调方法是消除非线性因素对光纤水听器PGC解调结果影响的一种有效手段,椭圆曲线参数的最优估计问题是实现该方法的关键。扩展卡尔曼粒子滤波(Extended Kalman Particle Filter,EPF)是解决此类非线性估计问题的一种常用的最优估计算法。但传统的EPF算法在用于常参数过程方程的参数或状态估计问题时,过程噪声的方差通常设置为一个常量,这使得算法难以兼顾收敛速度和估计精度,一定程度上限制了算法的整体性能。为了解决这个问题,文章对现有的EPF进行了改进,提出了一种自适应扩展卡尔曼粒子滤波(Adaptive Extended Kalman Particle Filter,AEPF)算法。模拟仿真和实验结果表明,文中所提出的AEPF算法能根据基于椭圆拟合的PGC解调方法有效地解调出待测声信号,相比EKF算法和EPF算法,AEPF算法的收敛速度和估计精度都得到了提升。此外,文章所提出的AEPF算法也适用于其他具有常参数过程方程的参数或状态估计问题,具有一定的通用性。     

QR分解的最小二乘格型自适应滤波算法在噪声主动控制中应用

在噪声主动控制系统中?,滤波-x递归最小二乘(FxRLS)算法收敛速度快但计算量大。本文提出了格型联合估计滤波器结构与基于QR分解的最小二乘格型(QRD-LSL)自适应滤波算法相结合的噪声控制方法,该方法对联合估计过程进行了改进并得到了基于各阶估计误差的联合过程估计权系数更新关系,格型联合估计器结构简单,QRD-LSL自适应滤波算法数值稳定性好。仿真结果表明本文提出的噪声控制方法有良好的噪声控制效果,收敛速度快,计算量小,稳态误差小,跟踪性能好。     

模糊自适应滤波的主动控制方法研究

由于主动控制中广泛使用的自适应滤波 - x L MS算法只适用于线性控制问题 ,针对一些非线性问题 ,本文提出利用一种非线性自适应滤波方法——基于模糊逻辑系统的自适应滤波方法来解决一类参考信号与外扰呈非线性函数关系的前馈主动控制问题。仿真结果表明 ,该模糊自适应滤波器优于线性滤波器的控制效果     

自适应稀疏贝叶斯滤波在轴承故障提取中的应用

稀疏贝叶斯滤波作为一种简单、新颖的滤波器，对噪声中的步进动态具有较好鲁棒性。同时，该滤波器引入一种L1正则化，其稀疏解可通过标准凸优化方法快速获得，因此它也具有较高的运算效率。但是在原始的稀疏贝叶斯滤波中，正则化参数必须提前设定，而该种参数的选择主要依靠人为经验，这就可能导致所选择的参数无法满足要求。针对现有不足，提出一种基于樽海鞘群优化算法的自适应稀疏贝叶斯滤波的轴承故障提取方法。该种自适应滤波方法采用轴承故障信号的包络谱峭度和负熵为目标函数选择最优的正则化参数，从而得到最优的滤波信号。最后通过包络分析得到轴承故障特征频率。通过模拟数据和真实数据证明该方法的有效性和优越性。     

约束自适应桁架优化设计方法

为求解多峰值、高度非线性桁架尺寸及形状优化问题,减少算法参数设置的盲目性,将Oracle罚函数与启发式算法相结合,提出可自适应处理约束列式的优化算法Ω-CMA-ES。该算法在处理各类复杂桁架优化问题时仅需设置一个参数Ω。测试算例表明,该算法对参数Ω具有良好的鲁棒性,可有效处理各类动态约束;且在探索全局最优解时体现出较高潜力,优化质量及收敛速度优于既有结果。     

Hessian approximation algorithms for hybrid optimization methods

This article introduces Hessian approximation algorithms to estimate the search direction of the quasi-Newton methods for solving optimization problems of continuous parameters. The proposed algorithms are quite different from other well-known quasi-Newton methods, such as symmetric rank-one, Davidon–Fletcher–Powell, and Broyden–Fletcher–Goldfarb–Shanno, in that the Hessian matrix is not calculated from the gradient information, rather directly from the function values. The proposed algorithms are designed for a class of hybrid algorithms that combine evolutionary search with the gradient-based methods of quasi-Newton type. The function values calculated for the evolutionary search are used for estimation of the Hessian matrix (or its inverse) as well as the gradient vector. Since the estimation process of the Hessian matrix is independent of that of the gradient vector, more reliable Hessian estimation with a small population is possible compared with the previous methods based upon the classical quasi-Newton methods. Numerical experiments show that the proposed algorithms are very competitive with state-of-the-art evolutionary algorithms for continuous optimization problems.     

Moments-based clustering techniques for manufacturing cell formation

Clustering and data organization algorithms and array sorting methods are most widely used in solving machine-part grouping problems in Group Technology (GT). One of the most recent search algorithms for part-family identification is the close-neighbour search algorithm. However the method does not suggest a solution that tries to eliminate exceptional elements after the block diagonal structure is produced. This paper presents a Method of Moments approach such as Weighted Moment, End Load Ratio and Differential Moment Methods to solve this problem. The effectiveness of these algorithms is tested against sixteen problems from the literature. The results show that the algorithms are very efficient and reliable.     

结合广义Armijo步长搜索的一类记忆梯度算法及其收敛特征

本文对求解无约束规划的超记忆梯度算法中线搜索方向中的参数,给了一个假设条件,从而确定了它的一个新的取值范围,保证了搜索方向是目标函数的充分下降方向,由此提出了一类新的记忆梯度算法.并在去掉迭代点列有界和广义Armijo步长搜索下,讨论了算法的全局收敛性,且给出了结合形如共轭梯度法FR,PR,HS的记忆梯度法的修正形式,数值实验表明,新算法比Armijo线搜索下的FR,PR,HS共轭梯度法和超记忆梯度法更稳定、更有效.     

A constraint consensus memetic algorithm for solving constrained optimization problems

Constraint handling is an important aspect of evolutionary constrained optimization. Currently, the mechanism used for constraint handling with evolutionary algorithms mainly assists the selection process, but not the actual search process. In this article, first a genetic algorithm is combined with a class of search methods, known as constraint consensus methods, that assist infeasible individuals to move towards the feasible region. This approach is also integrated with a memetic algorithm. The proposed algorithm is tested and analysed by solving two sets of standard benchmark problems, and the results are compared with other state-of-the-art algorithms. The comparisons show that the proposed algorithm outperforms other similar algorithms. The algorithm has also been applied to solve a practical economic load dispatch problem, where it also shows superior performance over other algorithms.     

A new multi-objective particle swarm optimizer using empirical movement and diversified search strategies

Most real-world optimization problems involve the optimization task of more than a single objective function and, therefore, require a great amount of computational effort as the solution procedure is designed to anchor multiple compromised optimal solutions. Abundant multi-objective evolutionary algorithms (MOEAs) for multi-objective optimization have appeared in the literature over the past two decades. In this article, a new proposal by means of particle swarm optimization is addressed for solving multi-objective optimization problems. The proposed algorithm is constructed based on the concept of Pareto dominance, taking both the diversified search and empirical movement strategies into account. The proposed particle swarm MOEA with these two strategies is thus dubbed the empirical-movement diversified-search multi-objective particle swarm optimizer (EMDS-MOPSO). Its performance is assessed in terms of a suite of standard benchmark functions taken from the literature and compared to other four state-of-the-art MOEAs. The computational results demonstrate that the proposed algorithm shows great promise in solving multi-objective optimization problems.     

Real-parameter quantum evolutionary algorithm for economic load dispatch

A novel real-parameter optimisation algorithm called the 'real-parameter quantum evolutionary algorithm' is presented. The algorithm pieces together the ideas from evolutionary algorithms (EA) and quantum computing to provide a robust optimisation technique that can be utilised to optimise highly constrained non-linear real-parameter functions. Quantum bits have immense representational power due to their being in superposition of all the basic states at the same time. New quantum operators designed in this work enable the search to effectively handle the twin objectives of exploitation and exploration. This enables the search to be pursued with small population sizes, thereby speeding up the search process and also ensuring that there is no problem of premature convergence that often plagues pure EA implementations. The power of the proposed algorithm is demonstrated by solving the economic load dispatch (ELD) in power systems. ELD is to find the optimal loadings on the generators so as to achieve minimum operating cost while satisfying various system and unit-level constraints. The proposed method has been applied to standard load dispatch problems reported in the literature including the IEEE 30 bus system, IEEE 57 bus system and a 110-generator problem, and its performance has been compared with the results obtained by other methods. The results adequately demonstrate the enhanced search power of the proposed algorithm in terms of obtaining better solutions and provide motivation for its application to other real-parameter optimisation problems in power systems.     

基于空间收缩的并行演化算法

提出了一种基于空间收缩的求解MINLP问题的新算法。算法应用了快速有效的不完全演化搜索较优解的分布信息，通过分布信息定位最优解的可能分布，再由精英个体信息决定下次搜索空间。仿真结果表明该算法在搜索效率、应用范围、解的精确性和鲁棒性上都优于其他现存演化算法。     

Two efficient algorithms for constructing almost even approximations of the Pareto front in multi-objective optimization problems

In this article, two algorithms are proposed for constructing almost even approximations of the Pareto front of multi-objective optimization problems. The first algorithm is a hybrid of the ε-constraint and Pascoletti–Serafini scalarization methods for solving bi-objective problems. The second is a modification of the successive Pareto optimization (SPO) algorithm for solving three-objective problems. In these algorithms, the MATLAB fmincon solver is used to solve single-objective optimization problems, which returns a local optimal solution. Some metrics are considered to evaluate the quality of approximations obtained by the suggested algorithms on six test problems, and their results are compared with other algorithms (normal constraint, weighted constraint, SPO, differential evolution, multi-objective evolutionary algorithm/decomposition–differential evolution, non-dominated sorting genetic algorithm-II and S-metric selection evolutionary multi-objective algorithm). Experimental results show that the proposed algorithms provide almost even approximations of the whole Pareto front, and better quality of approximation and CPU time compared with established algorithms.     

A discrete hybrid differential evolution algorithm for solving integer programming problems

Differential evolution (DE) is one of the most prominent new evolutionary algorithms for solving real-valued optimization problems. In this article, a discrete hybrid differential evolution algorithm is developed for solving global numerical optimization problems with discrete variables. Orthogonal crossover is combined with DE crossover to achieve crossover operation, and the simplified quadratic interpolation (SQI) method is employed to improve the algorithm's local search ability. A mixed truncation procedure is incorporated in the operations of DE mutation and SQI to ensure that the integer restriction is satisfied. Numerical experiments on 40 test problems including seventeen large-scale problems with up to 200 variables have demonstrated the applicability and efficiency of the proposed method.     

混沌优化法在机械设计中的应用

针对一般的二次载波的混沌优化方法收敛慢的弱点,提出了一些改进的方法.主要是利用当前解的信息,自动改变最优点的搜索路径,能明显提高首次载波寻找最佳点大概位置的速度和效能;同时能更快更精确实现二次载波的精细搜索.将该算法用于一机械设计问题———箱形盖板优化设计计算之中,取得了优于常规方法———Powell法的结果,说明该方法在机械优化设计中具有较好的应用价值.