求解非线性方程组系统的改进蝴蝶优化算法

针对当前算法求解非线性方程组系统存在求解个数不完整、速度慢和精度低等问题,提出一种改进蝴蝶优化算法.首先重新定义蝴蝶优化算法的局部迭代公式,然后再结合改进的反向学习算法和二次插值方法增强算法的搜索能力.通过9个非线性方程组的仿真实验,结果表明该算法能有效搜索到非线性方程组的较多解,并与其他算法进行比较,该算法在解的数量、速度和质量上具有绝对优势.     

基于量子粒子群算法的电力系统负荷多目标优化分配

针对电力系统经济负荷优化分配问题,提出了一种基于量子粒子群的多目标优化算法.该算法通过将改进后的量子进化算法融合到粒子群中,采用量子位对粒子的当前位置进行编码,用量子旋转门实现对粒子最优位置的搜索,用量子非门实现粒子位置的变异以避免早熟收敛.这种搜索机制能够遍历解空间,增强种群的多样性,并能用量子位的概率幅将最优解表述为解空间中的多种表述形式,从而增强全局最优的可能性.最后,通过算例进行仿真分析,结果表明算法的搜索能力和优化效率均优于普通粒子群算法.     

解线性等式约束优化问题的模式搜索过滤集方法（英文）

提出一个解线性等式约束无导数优化的模式搜索过滤集算法,该算法将过滤集技术嵌入无导数优化算法中以改善算法的效率.建立了新算法的总体收敛性,初步的数值试验结果表明新算法是有效的.     

解线性等式约束优化问题的模式搜索过滤集方法

提出一个解线性等式约束无导数优化的模式搜索过滤集算法,该算法将过滤集技术嵌入无导数优化算法中以改善算法的效率. 建立了新算法的总体收敛性, 初步的数值试验结果表明新算法是有效的.     

一个基于离散填充函数的整数线性规划方法

本文提出了一个有效的解决整数线性规划的新算法.如果离散化的局部搜索过程陷入局部最优解,则构造相应的离散填充函数,引导搜索过程跳出局部最优解并得到更好的解.该方法是在离散空间中进行优化的,无需增加新的约束,且一直保持整数可行性,收敛的速度非常快.该方法也为一般整数规划提出了一种新的途径.数值实例表明,与现有的方法相比,该算法能够较快的找到最优解.     

求解全局优化问题的改进蝙蝠算法[英文]

蝙蝠算法(BA)是一类基于试探技巧的群智能优化算法,该算法已被广泛用于诸多领域问题的求解.本文提出一个改进的蝙蝠算法NIBA.在算法中,为了加强蝙蝠算法的局部和全局搜索能力,提出了三个改进策略.首先,为了改进蝙蝠的局部搜索能力,在当前最优解处给出了一个新的搜索方程.其次,为了改进算法的全局搜索能力,平衡算法的开发能力和探索能力,算法吸收并改进了和声搜索机制.最后,为了进一步提高NIBA算法的搜索能力,在当前最优解处,算法采用了混沌搜索机制.为了验证算法的性能,针对18个标准测试函数进行了数值实验.与其它算法的比较结果显示,NIBA算法具有更好的稳定性,且效率更高.     

利用Fisher函数解非线性不等式约束优化问题的梯度投影算法

本文将利用梯度投影与Fisher函数提出一个新的二阶段搜索方向,给出相应的解非线性不等式约束优化问题的梯度投影算法,并证明了该算法具有全局收敛性.     

考虑加工效率变化的变速机干扰管理研究

在变速机生产排序中, 受来自企业外部可改变机器加工效率的突发性干扰事件影响, 初始最小化企业生产成本的加工时间表不再最优,需要对其调整并在生产成本和干扰事件扰动之间进行权衡。建立了同时考虑生产成本和干扰事件扰动的重排序模型, 生产成本为所有机器的负载之和, 干扰事件的扰动为工件在不同机器之间重新安排所产生的运输费用和。设计了求解该重排序问题有效前沿的算法, 以及利用决策者对两个目标的偏好将双目标转化成一个二元非线性函数后, 求解优化该函数的有效解的算法。通过数值算例验证与整个有效前沿相比,优化二元函数的算法只需搜索部分有效前沿即可求出最优解,降低了有效解的搜索比例和运行时间,提高了干扰管理问题的处理效率。     

一种改进的GDF多目标优化算法

本文提出一种交互式非线性多目标优化算法，该算法是ＧＤＦ多目标优化算法的改进，具有这样的特点：算法采用了既约设计空间策略，具有良好的收敛性；算法生成的迭代点是有效解；算法具有多种一维搜索准则；对于线性多目标问题，算法只需一次交互迭代即可示出多目标问题的最优解。     

基于模拟退火的CRS算法

本文是对非线性规划问题提出的一种算法,该算法把模拟退火算法应用到CRS算法中,根据模拟退火算法每一次迭代都体现集中和扩散两个策略的平衡的特点,使CRS算法更能够搜索到全局最优解,而不会陷入局部最优解。最后把提出的算法应用到两个典型的函数优化问题中,结果表明,算法是可行的、有效的     

Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling

This paper decomposes a Hammerstein nonlinear system into two subsystems, one containing the parameters of the linear dynamical block and the other containing the parameters of the nonlinear static block, and presents a hierarchical multi-innovation stochastic gradient identification algorithm for Hammerstein systems based on the hierarchical identification principle. The proposed algorithm is simple in principle and easy to implement on-line. A simulation example is provided to test the effectiveness of the proposed algorithm.     

Degradation modeling with subpopulation heterogeneities based on the inverse Gaussian process

This study proposes a random effects model based on inverse Gaussian process, where the mixture normal distribution is used to account for both unit-specific and subpopulation-specific heterogeneities. The proposed model can capture heterogeneities due to subpopulations in the same population or the units from different batches. A new Expectation-Maximization (EM) algorithm is developed for point estimation and the bias-corrected bootstrap is used for interval estimation. We show that the EM algorithm updates the parameters based on the gradient of the loglikelihood function via a projection matrix. In addition, the convergence rate depends on the condition number that can be obtained by the projection matrix and the Hessian matrix of the loglikelihood function. A simulation study is conducted to assess the proposed model and the inference methods, and two real degradation datasets are analyzed for illustration.     

Prediction of soil water retention curve using Bayesian updating from limited measurement data

A soil water retention curve is one of the fundamental elements used to describe unsaturated soil. The accurate determination of soil water retention curve requires sufficient available information. However, the amount of measurement data is generally limited due to the restriction of time or test apparatus. As a result, it is a challenge to determine the soil water retention curve from limited measurement data. To address this problem, a Bayesian framework is proposed. In the Bayesian framework, Bayesian updating can be employed using the posterior distribution that is obtained by the Markov chain Monte Carlo sampling method with the Delayed Rejection Adaptive Metropolis algorithm. The parameters of soil water retention curve model are represented by the sample statistics of updating posterior distribution. A new updating algorithm based on Bayesian framework is proposed to predict the soil water retention curve using the ideal data and the limited measurement data of the granite residual soil and sand. The results show that the proposed prediction algorithm exhibits an excellent capability for more accurately determining the soil water retention curve with limited measured data. The uncertainty of updating parameters and the influence of the prior knowledge can be reduced. The converged results can be derived using the proposed prediction algorithm even if the prior knowledge is incomplete.     

考虑资源约束和数量折扣的联合补货-选址库存协同优化研究

本文构建了一种基于联合补货策略的配送中心选址-库存协同优化新模型,该模型允许缺货,有资源约束且考虑数量折扣;同时设计了一种融合模拟退火思想的双种群独立进化的自适应差分算法(Adaptive Simulated Annealing Differential Algorithm,ASADE)对该模型进行求解,并通过算例与自适应差分算法、改进的蛙跳算法进行对比,证实了ASADE算法的有效性。最后进行了敏感性分析,讨论相关参数变动对总成本的影响,可为管理者更好决策提供有益的依据。     

Genetic algorithm and M-estimator based robust sequential estimation of parameters of nonlinear sinusoidal signals

Estimation of parameters of nonlinear superimposed sinusoidal signals is an important problem in digital signal processing. In this paper, we consider the problem of estimation of parameters of real valued sinusoidal signals. We propose a real-coded genetic algorithm based robust sequential estimation procedure for estimation of signal parameters. The proposed sequential method is based on elitist generational genetic algorithm and robust M-estimation techniques. The method is particularly useful when there is a large number of superimposed sinusoidal components present in the observed signal and is robust with respect to presence of outliers in the data and impulsive heavy tail noise distributions. Simulations studies and real life signal analysis are performed to ascertain the performance of the proposed sequential procedure. It is observed that the proposed methods perform better than the usual non-robust methods of estimation.     

A multi-objective optimization evolutionary algorithm incorporating preference information based on fuzzy logic

A multi-objective optimization evolutionary algorithm incorporating preference information interactively is proposed. A new nine grade evaluation method is used to quantify the linguistic preferences expressed by the decision maker (DM) so as to reduce his/her cognitive overload. When comparing individuals, the classical Pareto dominance relation is commonly used, but it has difficulty in dealing with problems involving large numbers of objectives in which it gives an unmanageable and large set of Pareto optimal solutions. In order to overcome this limitation, a new outranking relation called “strength superior” which is based on the preference information is constructed via a fuzzy inference system to help the algorithm find a few solutions located in the preferred regions, and the graphical user interface is used to realize the interaction between the DM and the algorithm. The computational complexity of the proposed algorithm is analyzed theoretically, and its ability to handle preference information is validated through simulation. The influence of parameters on the performance of the algorithm is discussed and comparisons to another preference guided multi-objective evolutionary algorithm indicate that the proposed algorithm is effective in solving high dimensional optimization problems.     

A matheuristic algorithm for stochastic home health care planning

Efficient human resource planning is the cornerstone of designing an effective home health care system. Human resource planning in home health care system consists of decisions on districting/zoning, staff dimensioning, resource assignment, scheduling, and routing. In this study, a two-stage stochastic mixed integer model is proposed that considers these decisions simultaneously. In the planning phase of a home health care system, the main uncertain parameters are travel and service times. Hence, the proposed model takes into account the uncertainty in travel and service times. Districting and staff dimensioning are defined as the first stage decisions, and assignment, scheduling, and routing are considered as the second stage decisions. A novel algorithm is developed for solving the proposed model. The algorithm consists of four phases and relies on a matheuristic-based method that calls on various mixed integer models. In addition, an algorithm based on the progressive hedging and Frank and Wolf algorithms is developed to reduce the computational time of the second phase of the proposed matheuristic algorithm. The efficiency and accuracy of the proposed algorithm are tested through several numerical experiments. The results prove the ability of the algorithm to solve large instances.     

Unified impulsive fuzzy-model-based controllers for chaotic systems with parameter uncertainties via LMI

In this paper, a novel technique based on impulsive fuzzy T–S model is proposed for controlling chaotic systems with parameter uncertainties. According to this new model, a unified methodology for establishing robust stability, asymptotic stability and exponential stability of impulsive controllers is developed. Various robust stability conditions are presented in the form of linear matrix inequalities (LMI). A simple iterative algorithm is also provided for calculating design parameters based on LMI techniques. Finally, a typical design procedure is developed by using well-known chaotic systems for illustration, accompanied by several numerical simulations to demonstrate the validity of the proposed methodology.     

Two-stage least squares based iterative estimation algorithm for CARARMA system modeling

For stochastic systems described by the controlled autoregressive autoregressive moving average (CARARMA) models, a new-type two-stage least squares based iterative algorithm is proposed for identifying the system model parameters and the noise model parameters. The basic idea is based on the interactive estimation theory and to estimate the parameter vectors of the system model and the noise model, respectively. The simulation results indicate that the proposed algorithm is effective.     

The use of order statistic numerical simulation algorithms

A modification of the standard algorithm for the simulation of order statistics for a uniform distribution is proposed that uses confidence intervals. It is found that one of the applications of the algorithms for the simulation of order statistics (namely, simulation of the beta distribution with integer parameters) gives more efficient methods for the simulation of order statistics than the algorithm based on confidence intervals. It is shown that the resulting algorithm can be used for the efficient simulation of random variables with polynomial density and of beta distributed random variables with large noninteger parameters.