Genetic evolving ant direction HDE for OPF with non-smooth cost functions and statistical analysis

This paper proposes an evolving ant direction hybrid differential evolution (EADHDE) algorithm for solving the optimal power flow problem with non-smooth and non-convex generator fuel cost characteristics. The EADHDE employs ant colony search to find a suitable mutation operator for hybrid differential evolution (HDE) where as the ant colony parameters are evolved using genetic algorithm approach. The Newton–Raphson method solves the power flow problem. The feasibility of the proposed approach was tested on IEEE 30-bus system with three different cost characteristics. Several cases were investigated to test and validate the robustness of the proposed method in finding optimal solution. Simulation results demonstrate that the EADHDE provides very remarkable results compared to classical HDE and other methods reported in the literature recently. An innovative statistical analysis based on central tendency measures and dispersion measures was carried out on the bus voltage profiles and voltage stability indices.     

Evolving ant direction differential evolution for OPF with non-smooth cost functions

In this paper, an effective and reliable algorithm, termed as evolving ant direction differential evolution (EADDE) algorithm, for solving the optimal power flow problem with non-smooth and non-convex generator fuel cost characteristics is presented. In this method, suitable mutation operator for differential evolution (DE) is found by ant colony search. The genetic algorithm evolves the ant colony parameters and the Newton-Raphson method solves the power flow problem. The proposed algorithm has been examined on the standard IEEE 30-bus and IEEE 57-bus systems with three different objective functions. Different cases were considered to investigate the robustness of the proposed method in finding the global solution. The EADDE provides better results compared to classical DE and other methods recently reported in the literature as demonstrated by simulation results.     

Genetic evolving ant direction particle swarm optimization algorithm for optimal power flow with non-smooth cost functions and statistical analysis

This paper presents an evolving ant direction particle swarm optimization algorithm for solving the optimal power flow problem with non-smooth and non-convex generator cost characteristics. In this method, ant colony search is used to find a suitable velocity updating operator for particle swarm optimization and the ant colony parameters are evolved using genetic algorithm approach. To update the velocities for particle swarm optimization, five velocity updating operators are used in this method. The power flow problem is solved by the Newton–Raphson method. The feasibility of the proposed method was tested on IEEE 30-bus, IEEE 39-bus and IEEE-57 bus systems with three different objective functions. Several cases were investigated to test and validate the effectiveness of the proposed method in finding the optimal solution. Simulation results prove that the proposed method provides better results compared to classical particle swarm optimization and other methods recently reported in the literature. An innovative statistical analysis based on central tendency measures and dispersion measures was carried out on the bus voltage profiles and voltage stability indices.     

A modified particle swarm optimization for economic dispatch with non-smooth cost functions

This paper presents a new approach to economic dispatch (ED) problems with non-smooth cost functions using a particle swarm optimization (PSO) technique. The practical ED problems have non-smooth cost functions with equality and inequality constraints, which makes the problem of finding the global optimum difficult when using any mathematical approaches. Since, standard PSO may converge at the early stage, in this paper, a modified PSO (MPSO) mechanism is suggested to deal with the equality and inequality constraints in the ED problems. To validate the results obtained by MPSO, standard particle swarm optimization (PSO) and guaranteed convergence particle swarm optimization (GCPSO) are applied for comparison. Also, the results obtained by MPSO, PSO and GCPSO are compared with the previous approaches reported in the literature. The results show that the MPSO produces optimal or nearly optimal solutions for the study systems.     

Adaptive nuclear reactor control for integral quadratic cost functions

The problem of optimally controlling the power level changes of a nuclear reactor is considered. The model of an existing power plant is used, which is a ninth-order nonlinear system, having time-varying parameters. A closed form solution of the optimal feedback controller is not known for such a system, and numerical solutions require prohibitive amounts of computer time and/or storage for on-line applications. The approach in the present work is, therefore, to approximate the response of the reactor by that of a low-order linear model in a piece-wise manner. The model parameters are chosen to minimize the deviations, in any desired sense, between the system and model responses. The optimal feedback parameters are readily computed for the model, and the same controller results in suboptimal system performance. The difference, however, has been found negligible in the examples considered. Since the model parameters are updated to reflect the plant nonlinearities as well as changes in the plant parameters, the resultant control system is adaptive. It has been found, that using a second-order model, optimal performance could be approached to within practical limits, and the necessary computer time is realizable for on-line applications.     

变步长自适应萤火虫群多模态函数优化算法

针对萤火虫群优化（GSO）算法优化多模态函数存在收敛速度慢和求解精度不高等缺陷,提出一种变步长自适应萤火虫群优化算法（CSGSO）。该算法主要思想是在GSO算法中引入搜索成功与失败概念,在每次迭代中萤火虫个体据其搜索成功或失败,加大或减小其搜索步长,使算法具有动态自适应性。实验结果表明,该算法可有效地解决GSO算法优化多模态函数存在收敛速度慢和求解精度不高的问题,增强了GSO算法优化多模态函数的性能;与其他算法相比,提出的算法具有操作简单、容易理解、收敛速度快和求解精度高等优点。     

一种非光滑损失坐标下降算法

针对非光滑损失问题提出一种新的坐标下降算法,采用排序搜索的方式求解子问题解析解。分析了算法的时间复杂度,并给出了三种提高收敛速度的实用技巧。实验表明算法对正则化Hinge损失问题具有良好的性能,达到了预期的效果。     

融合色调统计特征的自适应区域增长算法

针对区域增长算法种子点选取及生长条件对分割效果影响大,但难以有效、准确确定的缺点,提出了一种融合色调、色调频度及其曲率的自适应区域增长算法(HARG)。通过曲率求取分割对象的色调频度曲线峰值,以峰值为中心进行加窗累加,当和值满足门限时,峰值作为区域增长的种子点,门限作为区域增长的生长条件。将算法应用于显微镜细胞图像的单个细胞内部分割,实验结果表明,HARG算法自适应确定了种子点及生长条件,能够实现多区域分割,分割精度得到改善。     

基于改进当前统计模型的自适应无源跟踪算法

针对无源跟踪中,标准当前统计模型无法自适应调整加速度极限值的缺点,设计了一种修正系数来通过机动目标的当前加速度自适应调整模型的加速度极限值,同时利用模糊控制的方法对修正系数的取值进行实时调整,实现了对当前统计模型的改进。最后结合容积卡尔曼滤波算法构造基于改进当前统计模型的自适应无源跟踪算法。仿真结果表明,相比基于标准当前统计模型的自适应跟踪算法,新算法对非机动目标、弱机动目标以及强机动目标都有更好的跟踪效果。     

An approximate Newton method for solving non-smooth equations with infinite max functions

In this paper, a new version of an approximate Newton method for solving non-smooth equations with infinite max function is presented. This method uses a difference approximation of the generalized Jacobian based on a weak consistently approximated Jacobian. Numerical example is reported for the generalized Newton method using two versions of approximation.     

A limited-memory quasi-Newton algorithm for bound-constrained non-smooth optimization

We consider the problem of minimizing a continuous function that may be non-smooth and non-convex, subject to bound constraints. We propose an algorithm that uses the L-BFGS quasi-Newton approximation of the problem's curvature together with a variant of the weak Wolfe line search. The key ingredient of the method is an active-set selection strategy that defines the subspace in which search directions are computed. To overcome the inherent shortsightedness of the gradient for a non-smooth function, we propose two strategies. The first relies on an approximation of the ε-minimum norm subgradient, and the second uses an iterative corrective loop that augments the active set based on the resulting search directions. While theoretical convergence guarantees have been elusive even for the unconstrained case, we present numerical results on a set of standard test problems to illustrate the efficacy of our approach, using an open-source Python implementation of the proposed algorithm.     

Local anisotropy analysis for non-smooth images

Identification of local anisotropy and determination of principal axes are addressed through different methods that are designed to be tolerant to the non-smooth character of images on the pixel scale. These different tools are validated on various examples, and their performances are compared. The most powerful, robust and accurate method consists in computing the curvature tensor of the auto-correlation function of regularized images using fast Fourier transforms.     

Adaptive statistical algorithms in network reliability analysis

The paper is concerned with introducing novel algorithms, such as adaptive approximation and deterministic radial basis function (RBF) method, for calculating the average loss (AL). Different approximators are trained to approximate the loss function and, after a short learning period, AL can be evaluated analytically with fast calculations. An improvement of the Li–Silvester (LS) method is also presented which yields a sharper lower bound on AL. The efficiency of the new methods are proven by theoretical analysis as well as demonstrated by excessive simulations.     

融合BFGS的自适应蜂群算法在谐波平衡分析中的应用

针对谐波平衡分析中传统算法存在初值限制,以及智能算法收敛速度慢的缺点,提出一种基于BFGS（Broyden-Fleteher-Goldfarl-Shanno）算法局部搜索策略的自适应蜂群算法。该算法在基本蜂群算法的基础上引入非线性的动态调整因子代替蜂群算法搜索公式中的随机变量,增加搜索的自适应性,并将BFGS算法运用到自适应蜂群算法后期求解,提高其局部搜索能力。实验结果表明,改进算法较标准蜂群算法迭代次数减少51.9%,相对于传统BFGS算法和部分改进智能算法均表现出较好收敛性能。     

抗统计分析的BPCS隐写算法

位平面复杂度分割（BPCS）隐写在复杂度较高的位面小块嵌入秘密信息,有较好的隐蔽性,但会引起图像位面小块复杂度的统计特性的变化。通过深入分析图像位平面上相邻像素之间的相关性,本文提出了一种改进的BPCS隐写法。它更好地利用人的视觉特性,结合图像的局部特征来嵌入秘密信息。实验结果表明,改进后的算法能够抵御针对原BPCS隐写的分析方法,有较大的嵌入量,并对原图像所作的改变具有更好的不可见性。     

An algorithm for clustering based on non-smooth optimization techniques

The problem of cluster analysis is formulated as a problem of non‐smooth, non‐convex optimization, and an algorithm for solving the cluster analysis problem based on non‐smooth optimization techniques is developed. We discuss applications of this algorithm in large databases. Results of numerical experiments are presented to demonstrate the effectiveness of this algorithm.     

An algorithm for insertion of idle time in the single-machine scheduling problem with convex cost functions

This paper addresses the problem of optimally inserting idle time into a single-machine schedule when the sequence is fixed and the cost of each job is a convex function of its completion time. We propose a pseudo-polynomial time algorithm to find a solution within some tolerance of optimality in the solution space, i.e., each completion time will belong to a small time interval z within which the optimal solution lies. Letting H be the planning horizon and |J| the number of jobs, the proposed algorithm is superior to the current best algorithm in terms of time-complexity when |J|<H/z.     

Interval extensions of non-smooth functions for global optimization and nonlinear systems solvers

Most interval branch and bound methods for nonlinear algebraic systems have to date been based on implicit underlying assumptions of continuity of derivatives. In particular, much of the theory of interval Newton methods is based on this assumption. However, derivative continuity is not necessary to obtain effective bounds on the range of such functions. Furthermore, if the first derivatives just have jump discontinuities, then interval extensions can be obtained that are appropriate for interval Newton methods. Thus, problems such as minimax orl ₁-approximations can be solved simply, formulated as unconstrained nonlinear optimization problems. In this paper, interval extensions and computation rules are given for the unary operation |x|, the binary operation max{x, y} and a more general “jump” function χ(s,x,y). These functions are incorporated into an automatic differentiation and code list interpretation environment. Experimental results are given for nonlinear systems involving max and |o| and for minimax andl ₁-optimization problems.     

时空众包环境下基于统计预测的自适应阈值算法

针对时空众包环境下任务分配随机性过高且效用值不理想的问题,提出一种基于统计预测的自适应阈值算法。首先,实时统计众包平台中空闲的任务、工人及工作地点的数量以设置阈值;其次,通过历史数据分析将任务与工人的分布分为均衡的两个部分,并用Min-max normalization方法为每个任务匹配一个确定的工人;最后,计算匹配到的工人出现的概率,以验证任务分配的有效性。使用相同真实数据的实验结果证实,与随机阈值算法相比,基于统计预测的自适应阈值算法的效用值提升了7%;与贪心算法相比,其效用值提升了10%。实验结果表明,基于统计预测的自适应阈值算法能够减少任务分配过程中的随机性并提高效用值。     

Multiobjective combinatorial optimization problems with a cost and several bottleneck objective functions: An algorithm with reoptimization

This paper addresses multicriteria combinatorial optimization problems involving one cost and several bottleneck objective functions. An algorithm is developed which generates the minimal complete set of Pareto-optimal solutions. This algorithm runs in polynomial time as long as the single objective problem considering only the cost function can be solved polynomially. A reoptimization procedure is used to accelerate the convergence of the algorithm. Applications are given. Computational results on randomly generated instances and planar grid graphs concerning the minimum cost spanning tree and the shortest path problem are presented.