A NEW HYBRID EVOLUTIONARY OPTIMIZATION ALGORITHM FOR DISTRIBUTION FEEDER RECONFIGURATION

This article extends a hybrid evolutionary algorithm to cope with the feeder reconfiguration problem in distribution networks. The proposed method combines the Self-Adaptive Modified Particle Swarm Optimization (SAMPSO) with Modified Shuffled Frog Leaping Algorithm (MSFLA) to proceed toward the global solution. As with other population-based algorithms, PSO has parameters which should be tuned to have a suitable performance. Thus, a self-adaptive framework is proposed to adjust the parameters dynamically. In SAMPSO, the PSO learning factors are considered to be the new control variables and are changed in the evolutionary process. To enhance the quality of the solutions, the SAMPSO is combined with MSFLA and a new hybrid algorithm is proposed to minimize the electrical energy losses of the distribution system by feeder reconfiguration. The effectiveness of the proposed method is demonstrated through two test systems.     

A NEW HYBRID ALGORITHM FOR MULTI-OBJECTIVE DISTRIBUTION FEEDER RECONFIGURATION

This article proposes an efficient hybrid algorithm for multi-objective distribution feeder reconfiguration. The hybrid algorithm is based on the combination of discrete particle swarm optimization (DPSO), ant colony optimization (ACO), and fuzzy multi-objective approach called DPSO-ACO-F. The objective functions are to reduce real power losses, deviation of nodes voltage, the number of switching operations, and the balancing of the loads on the feeders. Since the objectives are not the same, it is not easy to solve the problem by traditional approaches that optimize a single objective. In the proposed algorithm, the objective functions are first modeled with fuzzy sets to calculate their imprecise nature and then the hybrid evolutionary algorithm is applied to determine the optimal solution. The feasibility of the proposed optimization algorithm is demonstrated and compared with the solutions obtained by other approaches over different distribution test systems.     

Encoding subcomponents in cooperative co-evolutionary recurrent neural networks

Cooperative coevolution employs evolutionary algorithms to solve a high-dimensional search problem by decomposing it into low-dimensional subcomponents. Efficient problem decomposition methods or encoding schemes group interacting variables into separate subcomponents in order to solve them separately where possible. It is important to find out which encoding schemes efficiently group subcomponents and the nature of the neural network training problem in terms of the degree of non-separability. This paper introduces a novel encoding scheme in cooperative coevolution for training recurrent neural networks. The method is tested on grammatical inference problems. The results show that the proposed encoding scheme achieves better performance when compared to a previous encoding scheme.     

浅析网络重构方法提高配电系统可靠性

本文提出一种通过网络优化重构来提高配电系统可靠性的概念,建立了以可靠性指标最优为目标函数的数学模型。根据配电网络的特点,首次将Tabu搜索方法应用于网络重构的寻优。结果表明,Tabu搜索方法非常适合于求解配网重构问题,配网重构也能有效提高系统可靠性。     

基于改进粒子群算法的配电网重构策略

针对有源配电网对安全可靠性的要求较高,而现有的配电网重构算法精度低、速度低的问题,提出了基于蛙跳分组思想的自适应惯性权重的全信息简化粒子群算法。首先,从降低网络有功功率损耗、提高电压稳定性、均衡馈线负荷三个角度考虑,建立配电网多目标数学模型;然后,通过基于Pareto支配原则,采用模糊隶属函数的标准化满意度将多目标转化为相同量纲、同一属性、相同数量级的单目标,弥补加权法带有主观性、量纲不统一的弊端;最后,为保证种群多样性,避免随机初始化产生大量不可行解,结合蚁群优化（ACO）算法随机生成树和改进粒子群算法制定出一种针对含分布式电源（DG）的多目标配电网重构策略。通过对含DG的IEEE33节点配电网系统仿真验证,实验结果表明,与标准粒子群优化（PSO）算法相比,该重构策略寻优效率提高了41.0%,与重构前相比,该重构策略降低配电网有功损耗41.47%,降低电压偏移指数57.0%,改善系统负荷均衡度31.25%。该重构策略有效提高了寻优精度,提高了寻优速度,从而提高了配电网运行的安全可靠性。     

The use of local radial point interpolation method for solving two-dimensional linear fractional cable equation

An efficient numerical technique is formulated to solve two-dimensional time fractional cable equation. The fractional cable equation is an important mathematical model for describing anomalous diffusion processes in biological systems. The proposed computational technique is based on the combination of time stepping method and meshless weak formulation. At the first step, some implicit difference schemes are used to discrete the appearing integer and fractional time derivatives. Then, local radial point interpolation method (LRPI) is extended and used to solve the semi-discretized problem. The main aim of the paper is to show that the LRPI method is a powerful alternative computational technique to solve complicated fractional problems with high accuracy and low complexity. The performance and accuracy of the method are studied and verified through numerical experiments. Moreover, the convergence rate of the temporal discretization scheme is investigated numerically.

     

An improved cuckoo search algorithm for the problem of electric distribution network reconfiguration

Power loss reduction has an important role in operating electric distribution network system. There are a lot of methods for reduction power loss such as capacitor placement, distributed generation placement and electric distribution network reconfiguration (EDNR). Among these methods, the EDNR is an efficient technique to reduce power loss due to without taking any costs. However, the EDNR problem is a nonlinear, discrete problem and lots of extreme points. Therefore, it is necessary to have efficient methods for solving the EDNR problem. In this paper, an improved cuckoo search algorithm (ICSA) is proposed for solving the EDNR problem. In which, based on disadvantages of exploration and exploitation process of cuckoo search algorithm (CSA) for solving the EDNR problem, a local search mechanism is added to exploit candidate solutions existing around the current best solution. The calculated results on the simple distribution networks to complex distribution networks show that ICSA has ability for finding the global optimal solution with much smaller iterations and better quality of obtained solution compared with CSA and some other improved versions of CSA. The performance comparisons with other existing methods available in previous studies and the software of Power System Simulator/Advanced Distribution Engineering Productivity Tool (PSS/ADEPT) also lead to the better electric distribution network configuration with smaller total power losses. As a result, ICSA is a potential and reliable method for solving the EDNR problems.     

基于eFPGA 的通信基带加速器的逻辑重构设计

为解决CMOS器件特征尺寸缩小带来的SoC(System on Chip,片上系统)芯片可靠性失效的问题,提出了一种基于eFPGA(embedded FPGA,嵌入式FPGA)的在线编程功能实现故障电路逻辑重构的方法。对eFPGA技术优势、JTAG(Joint TestAction Group,联合测试工作组协议)工作原理进行了分析,选取通信基带信号处理的典型算法：FFT(Fast Fourier Transform,快速傅里叶变换)、FIR(Finite Impulse Response,有限脉冲响应)滤波算法为例,模拟通信基带加速器功能失效时,借助JTAG技术配置新的互连关系,利用eFPGA进行逻辑重构,替代通信基带加速器结构实现功能自愈。仿真及验证结果显示eFPGA在面积与功耗方面具备优势,此方案可以实现预期逻辑重构的功能,能有效提高系统可靠性与灵活性。     

Fourth-order runge-kutta schemes for fluid mechanics applications

Multiple high-order time-integration schemes are used to solve stiff test problems related to the Navier-Stokes (NS) equations. The primary objective is to determine whether high-order schemes can displace currently used second-order schemes on stiff NS and Reynolds averaged NS (RANS) problems, for a meaningful portion of the work-precision spectrum. Implicit-Explicit (IMEX) schemes are used on separable problems that naturally partition into stiff and nonstiff components. Non-separable problems are solved with fully implicit schemes, oftentimes the implicit portion of an IMEX scheme. The convection-diffusion-reaction (CDR) equations allow a term by term stiff/nonstiff partition that is often well suited for IMEX methods. Major variables in CDR converge at near design-order rates with all formulations, including the fourth-order IMEX additive Runge-Kutta (ARK₂) schemes that are susceptible to order reduction. The semi-implicit backward differentiation formulae and IMEX ARK₂ schemes are of comparable efficiency. Laminar and turbulent aerodynamic applications require fully implicit schemes, as they are not profitably partitioned. All schemes achieve design-order convergence rates on the laminar problem. The fourth-order explicit singly diagonally implicit Runge-Kutta (ESDIRK4) scheme is more efficient than the popular second-order backward differentiation formulae (BDF2) method. The BDF2 and fourth-order modified extended backward differentiation formulae (MEBDF4) schemes are of comparable efficiency on the turbulent problem. High precision requirements slightly favor the MEBDF4 scheme (greater than three significant digits). Significant order reduction plagues the ESDIRK4 scheme in the turbulent case. The magnitude of the order reduction varies with Reynolds number. Poor performance of the high-order methods can partially be attributed to poor solver performance. Huge time steps allowed by high-order formulations challenge the capabilities of algebraic solver technology.     

基于多变量公钥密码体制的代理重签名方案

代理重签名方案大多是基于大数分解和离散对数难题设计的,而量子计算机能在多项式时间内求解这些难题,从而给方案造成安全隐患。为此,利用多变量公钥密码体制可以抵抗量子攻击的特性,设计一个新的代理重签名方案。理论分析结果表明,该方案满足内部安全性和外部安全性,且运算效率高。     

Integrating Variable Reduction Strategy With Evolutionary Algorithms for Solving Nonlinear Equations Systems

Nonlinear equations systems (NESs) are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots. Evolutionary algorithms (EAs) are one of the methods for solving NESs, given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one run. Currently, the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs. By contrast, problem domain knowledge of NESs is investigated in this study, where we propose the incorporation of a variable reduction strategy (VRS) into EAs to solve NESs. The VRS makes full use of the systems of expressing a NES and uses some variables (i.e., core variable) to represent other variables (i.e., reduced variables) through variable relationships that exist in the equation systems. It enables the reduction of partial variables and equations and shrinks the decision space, thereby reducing the complexity of the problem and improving the search efficiency of the EAs. To test the effectiveness of VRS in dealing with NESs, this paper mainly integrates the VRS into two existing state-of-the-art EA methods (i.e., MONES and DR-JADE) according to the integration framework of the VRS and EA, respectively. Experimental results show that, with the assistance of the VRS, the EA methods can produce better results than the original methods and other compared methods. Furthermore, extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.      

Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints

Optimal reactive power dispatch (ORPD) is well known as a complex mixed integer nonlinear optimization problem where many constraints are required to handle. In the last decades, many artificial intelligence-based optimization methods have been used to solve ORPD problem. But, these optimization methods lack an effective means to handle constraints on state variables. Thus, in this paper, the novel and feasible conditional selection strategies (CSS) are devised to handle constraints efficiently in the proposed improved gravitational search algorithm (GSA-CSS). In addition, considering the weakness of GSA itself, the improved GSA-CSS (IGSA-CSS) is presented which employs the memory property of particle swarm optimization (PSO) to enhance global searching ability and utilizes the concept of opposition-based learning (OBL) for optimizing initial population. The presented GSA-CSS and IGSA-CSS methods are applied to ORPD problem on IEEE14-bus, IEEE30-bus and IEEE57-bus test systems for minimization of power transmission losses (P_loss) and voltage deviation (V_d), respectively. The comparisons of simulation results reveal that IGSA-CSS provides better results and the improvements of algorithm in this work are feasible and effective.     

A hybrid CMA-ES and HDE optimisation algorithm with application to solar energy potential

This paper describes the results of initial experiments to apply computational algorithms to explore a large parameter space containing many variables in the search for an optimal solution for the sustainable design of an urban development using a potentially complicated fitness function. This initial work concentrates on varying the placement of buildings to optimise solar irradiation availability. For this we propose a hybrid of the covariance matrix adaptation evolution strategy (CMA-ES) and hybrid differential evolution (HDE) algorithms coupled with an efficient backwards ray tracing technique. In this paper we concentrate on the formulation of the new hybrid algorithm and its testing using standard benchmarks as well as a solar optimisation problem. The new algorithm outperforms both the standalone CMA-ES and HDE algorithms in benchmark tests and an alternative multi-objective optimisation tool in the case of the solar optimisation problem.     

Embedding linear programming in multi objective genetic algorithms for reducing the size of the search space with application to leakage minimization in water distribution networks

This paper shows how embedding a local search algorithm, such as the iterated linear programming (LP), in the multi-objective genetic algorithms (MOGAs) can lead to a reduction in the search space and then to the improvement of the computational efficiency of the MOGAs. In fact, when the optimization problem features both continuous real variables and discrete integer variables, the search space can be subdivided into two sub-spaces, related to the two kinds of variables respectively. The problem can then be structured in such a way that MOGAs can be used for the search within the sub-space of the discrete integer variables. For each solution proposed by the MOGAs, the iterated LP can be used for the search within the sub-space of the continuous real variables. An example of this hybrid algorithm is provided herein as far as water distribution networks are concerned. In particular, the problem of the optimal location of control valves for leakage attenuation is considered. In this framework, the MOGA NSGAII is used to search for the optimal valve locations and for the identification of the isolation valves which have to be closed in the network in order to improve the effectiveness of the control valves whereas the iterated linear programming is used to search for the optimal settings of the control valves. The application to two case studies clearly proves the reduction in the MOGA search space size to render the hybrid algorithm more efficient than the MOGA without iterated linear programming embedded.     

求解Web集群文档分布的混沌搜索算法

提出Web集群文档分布方案,用M/G/1/K PS排队模型对服务器进行建模,将文档分布问题转化为0-1整数规划问题,然后求解该规划问题。针对该类0-1整数规划问题,给出一种基于混沌搜索的求解算法,该算法让多个独立的混沌变量在其各自的轨道中搜索,使得对应生成的0-1矩阵能遍历任意一种可能的分布,从而能搜索到全局最优解。设计一种基于贪婪思想的文档分布算法。测试表明,混沌搜索算法能找到全局最优解,优于传统的贪婪算法。     

Solving electrical power system problems by harmony search: a review

Today, electric power plays a highly significant role in the development of various sectors of the countries. Most often, power system optimization problems have non-linear and non-convex objective functions with intense equality and inequality constraints along with various types of decision variables (continuous, discrete and integer). As modern electrical power systems become more complex, planning, operation and control of such systems using traditional methods face increasing difficulties. Owing to the ability of escaping local optima, meta-heuristic optimization algorithms can be efficient alternatives to solve power system optimization problems. Inspired by the improvisation process of music, harmony search (HS) algorithm is a meta-heuristic search method which has received a considerable attention to solve different power system optimization problems. HS has simple concept, is easy to implement, converges rapidly to the solution and has high efficiency. In this paper, technical literature about HS applied to power system optimization problems is reviewed. This review will enable the researchers to open the mind to explore possible applications in this field as well as beyond this area.     

一种高效保形迭代函数的构造及其应用

管海明提出的保形迭代函数构造方法(武汉大学学报: 理学版, 2008年第5期)在实际应用中不能顺利求得迭代大整数次的结果。针对该问题,结合离散对数问题的应用方法,提出2个解决策略,利用特殊构造法寻找一个结构简单的多项式,从而构造一种可行的高效保形迭代函数,分析证明其具有较高的迭代效率,并将该函数用于推广的ElGamal加密方案与Schnorr签名方案。     

A systematic approach to design the reconfiguration scheme of fault-tolerant systems

A general method is derived to design the reconfiguration scheme of fault-tolerant digital systems. Two tables are established during the design procedure. One is an Error Table which lists all the possible errors which can occur in the system and the other is the Truth Table which is used to assist the design of reconfiguration schemes.     

约束规划方法在原油混输调度中的应用

应用约束规划方法建立炼厂原油混输模型,并通过搜索求解获得可操作的调度方案。约束规划允许以逻辑约束表达混输过程中的操作规则,使模型简洁直观,而对整型变量及非线性约束不敏感,则降低了模型的求解难度。选取了一个时间跨度为300小时实际调度问题作为案例,模型中包含约8000个变量和14000条约束,在普通PC机上计算求解,能够在30秒内获得调度方案,方案具有可操作性且优于人工方案,表明以约束规划方法解决原油混输调度问题是可行的。     

求解0-1整数规划问题的混沌遗传算法*

针对一类特殊的0-1整数规划求解问题提出一种混沌遗传算法。该算法采用幂函数载波技术提高混沌搜索的充分性与遍历性,以混沌搜索算法得出的优化个体作为遗传算法的新群体进行交叉、变异等操作,提高种群质量,同时增加种群多样性,改善遗传算法的早熟问题。该算法被用于解决片上网络映射A3MAP(architecture-aware analytic mapping) 0-1整数规划问题。实验仿真证明,该算法的收敛速度和解的精度均优于A3MAP-GA。