Distribution Network Structure Planning Based on Improved Immune Genetic Algorithm

Distribution Network Structure planning is a complex combinatorial optimization problem,which is difficult to solve properly by using traditional optimization methods.In order to solve this problem,Improved Immune Genetic Algorithm is introduced to the distribution network optimal planning. Improved Immune Genetic Algorithm draws into the immune diversity and antibody's density mechanism to maintain the individual's diversity and remains evolution algorithm's global stochastic searching ability,so it can promote diversity and the whole optimal-searching ability of genetic algorithm.The optimal module takes the minimum annual cost as its object,and the capacity and voltage drop of feeder and the radiation of distribution network as its restrictions.According to the require of radiation of distribution network,the spanning tree of the alternative network is taken as the initial solution to speed up the calculation.And the branch-exchange method is used in designing crossover operator and mutation operator to avoid the radiation checking and enhance the optimizing ability.This algorithm has been illustrated effectively by examples,at the same time,the calculation example demonstrates that,the algorithm has higher calculation speed than the traditional immune genetic algorithm.     

OPTIMIZATION OF BIPARTITE JOINS WITH GRAPH THGORY

An optimization approach for multi-relational joins based on the matching concept in graph theory is introduced. The basic idea lies in constructing a join graph from an expression of N-relation query, and seeking for a maximun matching with minimun total weight. The algorithm for bipartition(X, Y) is proposed in this paper.     

A Dynamic Model for Generation Expansion Planning and Algorithm

This paper discusses the problem of long-range generation expansion planning. A dynamic model for long-range expansion planning is set up and an algorithm-a two-step programming is presented, which is used here in a case.     

A New Adaptive Sequential Diagnosis Algorithm in the PMC Model

We present an adaptive algorithm for sequential diagnosis in the PMC model,which can directly identify a faulty processor without first identifying a fault-free processor.     

Real-time Performance of Linux System

The standard Linux is a typical time-sharing system, it has a poor real-time performance. But according to the applications of the Linux OS are expanding on real-time domain, it becomes necessary to enhance the performance of the Linux system. This paper analyses the schedule policies and schedule algorithms of the Linux kernel, and discusses several different kinds of methods about enhancing real-time performance of the Linux system from the total Linux system. They authors summarizes the current research's developments of the Linux system.     

Automatic Fingerprint Classification by GA-Based Neural Network

Fingerprint classification can provide an important indexing mechanism in a fingerprint database. An accurate and consistent classification can greatly reduce fingerprint matching time for large database. In the paper, by combining genetic algorithm and neural network is presented a fingerprint classification algorithm which is able to achieve an accurate classification. By inputting the global feature represented by directional image to three layer neural network trained by genetic algorithm, the fingerprints were classified into six categories: whorl, right loop, left loop, arch, double loop and undiscerning type successfully.     

Avoiding Obstacle Planning for Robots on Molecular Optimization Algorithm

Interest in DNA computing has increased overwhelmingly since Adleman successfully demonstrated its capability to solve Hamiltonian Path Problem. This article introduces the improving method in virtue of the biological thery of DNA technology, a new molecular algorithm is advanced. After a numerical simulation, the result shows that it avoids the prematurely and lower convergent speed of the classic genetic algorithm, and inherits global search capability, the validity and the speed of the genetic algorithm have been increased. The best result can be obtained in few iterative times. It is fit for solving path planning problem.     

基于混沌算法的工程车辆自动变速神经网络控制

为了克服BP神经网络收敛速度慢和易陷入局部极小点的不足，提出了一种基于混沌算法的工程车辆自动变速神经网络控制方法，并利用ZL50型装载机传动系统换挡控制试验的数据进行了验证性仿真试验。仿真结果表明：该方法对于车辆自动换挡智能控制是可行的，可以根据操作工况实现正确的变速挡位决策。     

联合收获机前进速度模糊控制系统多目标遗传优化

针对联合收获机前进速度模糊控制系统参数设计具有主观性,导致作业性能不理想的问题,以切纵流联合收获机样机为研究对象,构建联合收获机前进速度模糊控制系统仿真模型,并建立控制性能和收获性能的优化目标函数来衡量联合收获机的作业性能,利用多目标遗传算法对模糊控制系统的隶属函数和输送槽、割台螺旋输送器和切流滚筒对前进速度的影响因子进行优化。通过对优化前后模型仿真对比和两组田间试验数据分析,表明优化后的控制系统在受外界干扰情况下控制性能得到了较好的保持,收获性能在喂入量变化不大情况下,单位平均损失率分别由1.45%和1.26%降至1.12%和1.14%,联合收获机的总体作业性能得到改善。