Multi-Objective Approach for Reconfiguration of Distribution Systems with Distributed Generations

This paper presents a fuzzy multi-objective based heuristic algorithm for network reconfiguration of distribution systems considering distributed generations (DGs). The objectives of reduction of real power loss, branch current carrying capacity limit, maximum and minimum voltage constraints, and feeder load balancing are considered for performance enhancement of the distribution system. Since these objectives are non-commensurable and difficult to solve simultaneously using conventional approaches, they are converted into fuzzy domain and a fuzzy multi-objective function is formulated. A sensitivity analysis based on voltage profile improvement and real power loss reduction is used for obtaining optimal locations of DGs and genetic algorithm is used for optimal sizing of DGs. The proposed reconfiguration algorithm is implemented in two stages, initially in the first stage without incorporating DGs and in the second stage incorporating DGs for obtaining an optimal distribution system network reconfiguration. The advantage of the proposed method is demonstrated through a seventy node four feeders and a sixteen node three feeders distribution systems.     

蚂蚁算法在工件排序问题中的应用

蚂蚁算法是近年来新出现的一类随机型仿生算法。它已被成功的应用于组合优化问题中,如旅行商(TravelSalesmanProblem,TSP)问题等。运用蚂蚁算法研究m台机器目标函数为最小时间表长的同顺序车间作业排序问题(Fm|prmu|Cmax),设计出解决该问题的算法步骤与流程;并将蚂蚁算法与解决该问题的其它启发式算法进行了比较。比较的结果说明,蚂蚁算法能有效地解决此类问题,其最优结果优于或者与其他算法的最优结果相当。     

集装箱码头泊位调度问题的启发式算法研究

为优化集装箱码头泊位的分配,提高泊位的利用率,把码头泊位的调度问题转化为带有约束条件的特殊二维装箱问题。通过建立连续泊位调度的非线性规划模型,提出了一种求解集装箱码头连续泊位分配问题启发式算法。仿真算例结果表明该算法能在实际的集装箱码头泊位调度中有效的提高泊位的利用率。     

一种启发式的用哼唱检索音乐的层次化方法

“用哼唱检索音乐”是一种友好的基于内容的音乐检索方法,它已经引起了广泛的研究兴趣;在对音乐库做了统计分析的基础上,总结了一些启发式规则,帮助对哼唱输入进行基音检测、音符分割,哼唱输入表达为音高轮廓图和节奏,音乐库中的音乐按音乐的节奏类型分为不同的节奏区域,并从每首音乐中抽取旋律轮廓图和节奏信息,用递归神经网络记忆旋律轮廓,音乐库的索引是神经网络的权值矩阵,将哼唱输入与音乐库中的音乐匹配的过程就是计算神经网络的输出过程。实验结果显示了所提方法的有效性。     

基于PB的FPSS系统的设计及其开发

如何在多品种小批量的生产环境下快速经济地得出生产作业调度方案是目前研究的热点。文章围绕一个机械加工公司展开,利用启发式算法来解决生产排序调度问题,并讨论了调度规则库的构建。FPSS系统运行表明:能快速得出生产作业排序调度方案,能根据内外环境的变化快速做出有效的资源调整。     

旅行商问题中巡回路径的数据结构

旅行商问题中巡回路径的数据结构对局部启发式算法的效率起着非常关键的作用。巡回路径的数据结构必须能够查询一条回路中每个城市的相对顺序,并且能够将一条回路中的部分城市逆序。分析了数组表示法、伸展树表示法和两级树表示法表示巡回路径时各种基本操作的实现过程及时间复杂度。数组表示法能够在常数时间内确定一条回路中每个城市的相对顺序,但是最坏情况下完成逆序操作需要Ω（n）时间,不适用于大规模的旅行商问题。伸展树表示法执行查询和更新操作的平摊时间复杂度是O（logn）,适用于极大规模的旅行商问题。而两级树表示法在最坏情况下每一个更新操作的时间复杂度是O（n^0.5）,适用于大规模的旅行商问题。     

Using heuristic algorithms for capacity leasing and task allocation issues in telecommunication networks under fuzzy quality of service constraints

Nowadays, every firm uses telecommunication networks in different amounts and ways in order to complete their daily operations. In this article, we investigate an optimisation problem that a firm faces when acquiring network capacity from a market in which there exist several network providers offering different pricing and quality of service (QoS) schemes. The QoS level guaranteed by network providers and the minimum quality level of service, which is needed for accomplishing the operations are denoted as fuzzy numbers in order to handle the non-deterministic nature of the telecommunication network environment. Interestingly, the mathematical formulation of the aforementioned problem leads to the special case of a well-known two-dimensional bin packing problem, which is famous for its computational complexity. We propose two different heuristic solution procedures that have the capability of solving the resulting nonlinear mixed integer programming model with fuzzy constraints. In conclusion, the efficiency of each algorithm is tested in several test instances to demonstrate the applicability of the methodology.     

On-Line Shape recognition with incremental training using binary synaptic weights algorithm

Recognition of hand drawn shapes is beneficial in drawing packages and automated sketch entry in handheld computers. In this paper, we propose a new approach to on-line geometric shape recognition with incremental training function, which utilizes a heuristic function to reduce noise and a neural network for classification and on-line training. Instead of recognizing segments of a drawing and then performing syntactical analysis to match with a predefined shape, which is weak in terms of generalization and dealing with noise, we examine the shape as a whole. The main concept of the recognition method is derived from the fact that internal angles are very important in the perceived shape of outlines. Our application's aim is to recognize elliptic, rectangular, and triangular shapes in a way similar to human cognition of these shapes. Human beings recognize such basic shapes regardless of the variations in size, noise on the shape border, rotation and in the case of triangles, regardless of the type of the triangle. The key concept is that the neural network learns the relationships between the internal angles of a shape and its classification, therefore only a few training samples which represent the class of the shape are sufficient. Fast meremental training, which is performed on-line, is accomplished by the use of the Binary Synaptic Weights algorithm, a one pass, feedforward neural network training algorithm. Incremental training offers the advantage of adjusting the recognition capability of the system to the user's drawings. the results are very successful, such that the neural network correctly classified shapes that did not have any resemblance to the shapes in the initial training set.     

货郎担问题最优并行启发式算法

本文给出了满足三角不等式的货郎担问题的并行启发式算法，在ＳＩＭＤ ＣＲＥＶ ＰＲＡＭ并行机上该算法使用Ｏ（ｎ＾３／ｌｏｇ＾２ｎ）台处理器需Ｏ熄ｌｏｇ＾２ｎ）时间，这里ｎ是给定城市的个数，因而该并行算法是最优的。     

广义AND／OR图的自底向上的启发式搜索算法BHAO

本文首先根据三角模概念，定义了一类新的更具普遍意义的广义ＡＮＤ／ＯＲ图．根据新定义的启发式函数ｈ（ｎ，ｘ）以及广义ＡＮＤ／ＯＲ图的最佳解树之所有子树亦是最佳子解树的原理，提出了广义ＡＮＤ／ＯＲ图的自底向上的启发式搜索算法ＢＨＡＯ．文中证明了算法ＢＨＡＯ的可采纳性．本文还提出了两类新的启发式函数的单调限制概念，并据此研究了算法ＢＨＡＯ的单调限制性质，研究了两个ＢＨＡＯ算法间的比较性质．