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.     

Investigations of Distribution System Planning of Loop Configurations Using a Genetic Algorithm

The distribution system planning is performed with a loop configuration constraint from scratch.With Evolutionary Algorithm,a sample distribution network is designed with a loop configuration,in which the system is operated as radial configuration.It firstly introduces the mathematical model for distribution network planning,then the application of Evolutionary Algorithm for the planning of distribution network.By using the theory mentioned above, Visual C++ is applied to develop the distribution network optimization software.Finally with the optimization process of 17 nodes system being showed,the presented algorithm's utility and validity is verified.     

Learning Causality Diagram Structure Based on Genetic Algorithm

The Causality Diagram theory,which adopted graphical expression of knowledge and direct causality intensity of causality,overcomes some shortages in Belief Network and has evolved into a mixed causality diagram methodology coped with discrete and continuous variable.But it is difficult that the structure of Causality Diagram given by expert.Because the complexity of causality diagram structure goes up exponentially through the number of the vertex's increasing,it is NP-hard problem to find the most possible structure from a set of data.The authors discuss approaches and present Genetic Algorithm,to find the most possible structure from a set of data.Experiment shows the method is effective.     

Improvement and Application of Immune Genetic Algorithm in Structural Damage Identification

In order to solve structural multi-damage identification problem, a two-stage method based on Bayesian theory and immune genetic algorithm (IGA) is presented. Firstly, structural modal strain energy and frequency are considered as two kinds of information sources, and Bayesian theory is utilized to integrate the two information sources and preliminarily detect structural damage locations. Then, immune genetic algorithm is used to identify structural damage extents. Considering the convergence rate of basic IGA is still not very good, some improved strategies, such as culture vaccine, two termination conditions, are presented. It is shown that the two-stage method can precisely identify structural damage locations and extent, and the calculated results of the proposed improved IGA are obviously better than those of both the basic IGA and simple genetic algorithm.     

Application of Improved Multi-population Genetic Algorithm to Damage Identification of Soil-wall System

The method of damage identification in soil-wall system was studied; a new approach based on improved multi-population genetic algorithm (IMGA) was developed. First, the simplified dynamic-detection model of soil-wall system was established, meanwhile, the theoretical analysis of characteristic equations in soil-wall system was conducted when soil in damage status. The objective function based on characteristic equations was established. Then, the improvements of multi-population genetic algorithm, including the adoption of real-valued representation, adaptive cross operator and adaptive mutation operator, were conducted. Finally, the localization and quantification of the soil-wall system damage were performed by IMGA with and without the consideration of noise, respectively. The results indicate that damage location and damage extent can be detected efficiently, and anti-noise performance is better.     

遗传算法优化人工神经网络模型在日光温室湿度预报中的应用

提出一种基于遗传算法优化BP神经网络的方法预测日光温室湿度环境因子。实测日光温室内影响空气湿度的环境因子组成数据样本作为神经网络的输入,采用基于实数编码的遗传算法替代随机设定神经网络的初始权阈值,然后通过改进的BP算法在由遗传算法确定的搜索空间中对网络进行精确训练。模型预报值和实测值基于1：1线的决定系数R2和预测平均相对误差MSE分别为0.9857和3．1％。结果表明,遗传算法优化BP神经网络预报模型收敛速度快、预测精度高。可为日光温室的湿度环境调控制提供理论依据和决策支持。     

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.     

Automatically Assembly Sequence Planning Based On Genetic Algorithms

A modified genetic algorithm for automatically assembly sequence planning is presented in this paper. The assembly direction sets of parts in structure are built to evaluate a assembly sequence without collisions. Five genetic operators are proposed. The fitness of sequence is calculated by its reorientation number, and a fitness scaling scheme is used to overcome premature termination. Example is provided to demonstrate the performance of this algorithm.     

Application of RBF Algorithm Based on Generalized Genetic Optimization in Prediction Model for Molten Iron Desulfuration

Desulfuration process is a very sophisticate reaction which is not only diverse but also non-line. A RBF algorithm based on generalized genetic optimization is proposed after studying the standard genetic and RBF algorithm. The authors also introduce its application in prediction Model for molten Iron Desulfuration. The algorithm perfectly resolve the problem of random selection of RBF cluster center number. Furthermore, it also reduces the time which GA uses. Comparison between the simulation results of RBF and RBF algorithm Based on GGA optimization further proves the efficiency and precision of its application in Prediction Model for Molten Iron Desulfuration. Finally the result of the test shows that after adopting the algorithm, the end-point hitting ratio can reach eighty-five percent. This indicate the algorithm has the engineering practicability.     

Dynamic Route Guidance System Based on Genetic Algorithm

The study on Dynamic Route Guidance System (DRGS) is an important research in the field of Intelligent Transportation System(ITS),which guide the behaviors of travelers by providing them with optimal route based on real-time traffic information. As a result the travel time can be saved and the traffic congestion can be avoided. The route guidance algorithm can compute the best route between the begin point and destination. The globe near best property and real-time property must be considered , the Genetic Algorithm have qualifications for globe optimal and parallel algorithm. Genetic algorithm(GA),for solving the shortest route,is proposed in this thesis?The ordered real code rule,crossover,mutation are given. The efficiency of the GA is proved through an example.     

Material Recombined System Based on Genetic Algorithm

A new and better mathematical model is presented, which is based on the in-depth analysis of the material recombined system and the sufficient expenses produced during the material recombined. In order to solve the problem, the authors choose the genetic algorithm, put forward a improved coding method, and get satisfactory answer through experiments. Advantages of efficiency and economic value are given.     

Optimum Analysis of Damper Braces Based on Genetic Algorithm

The aims at optimization of structure with damper braces is studied in this paper. The sum of damping coefficient of all damper braces is considered as a goal function, and the storey displacements are considered as constraint conditions. The program,which introduces the non-proportional damp matrix, based on genetic algorithm and time history is used to analyze the optimization of a damper braced frame. The results are reasonable and show that the genetic algorithm is an efficient method to optimize the damper braced structure.     

Research of Sensitivity Genetic Algorithm for Inverse Computation of Structure

The sensitivity genetic algorithm is developed in order to carry out effectively the inversion analysis of the diagnosis of the structure damage. The genetic algorithm with the technique of sensitivity can not only reduce the operation time of the program     

Hybrid Genetic Algorithm Based on Novel Adaptive Penalty Function

The authors introduce a sort of novel adaptive penalty gene, transform the constrained problem into unconstrained problems. An solution is given for this unconstrained problem with genetic algorithm, and then it is used as initial values for the constrained variable metric method to get precise solution. The numerical experiments illustrate that this hybrid genetic algorithm is more efficient than the genetic algorithm, and at most situations globally optimal solution can be gotten.     

A Genetic Algorithm Based on Repeating Crossover and Optimum Maintaining Strategy

A novel genetic algorithm,simply written as REGA, is proposed with the idea to limit the number of repeating crossover and replacing the worst individuals of the current generation by the best ones of the former generation. The algorithm overcomes the premature phenomenon of the simple genetic algorithm. According to Markov's limitation theorem, we prove its global convergence,explore the properties of the genetic algorithm written as RSGA only based on repeating crossover,and provide a method to calculate the mathematic expectation on the absorption time for the two algorithms. Finally,the simulation shows that the algorithm REGA can solve the optimization problem containing more than one global optimal solutions,on one hand,while eliminating the drawback of local optimum and rapidly enhancing the average fitness. On the other hand, REGA is valuable for function optimization.     

About the Synthesis Method of Planar Multilink Mechanisms Based on Genetic Algorithm

This paper discusses the method of synthesizing planar multilink mechanisms based on GA. The pivotal technology is coding, creating population, account fitness, genetic operation and defining population size. This method does not require initial mechanisms and can search for plural appropriate mechanisms simultaneously. It is efficient about the non-linear problem. As an example, configuration of 4-bar planar mechanisms is decided in a practical application. It can be used the synthesis of 6-bar or more bar planar mechanisms as well.     

基于线粒体Cyt b基因的皖南山区温州光唇鱼种群遗传结构

为探讨皖南山区温州光唇鱼的种群遗传结构,采用线粒体细胞色素b基因(Cyt b)对该区5个野生种群(祁门、黟县、休宁、旌德和宁国)进行群体遗传变异分析。131个样本的Cyt b基因(1141 bp)中共检出38个变异位点(变异率3.33%)、14种单倍型。序列碱基的平均含量分别为A(28.2%)、C(29.7%)、T(27.0%)、G(15.1%),A+T含量(55.2%)明显大于G+C含量(44.8%)。5个地理种群的单倍型多样性(0.0000~0.6799)和核苷酸多样性(0.0000~0.00759)普遍较低。群体分化系数(FST:0.2916-0.9782)和AMOVA分析中高达52.74%的遗传变异来自地理种群间,说明温州光唇鱼地理种群间已产生显著遗传分化;但不同水系的种群间没有显著遗传差异。群体间系统进化树显示:5个群体聚为两大进化枝,祁门和旌德种群为一枝,其余种群为另一枝。温州光唇鱼的这种种群遗传结构与地理隔离及其生态习性相关。     

Wavelet Neural Network Equalizer Using Error Feedback Structure and Algorithm

The inter-symbol interference in the nonlinear time-varying channel is a serious problem in the wireless communication. In order to overcome it, wavelet neural network equalizer using error feedback is employed to cut the auto-correlation of the error signal. Exploiting the decent time-frequency localization of the wavelet analysis, as well as the self-training feature of the neural network, a quicker convergent nalysis and computer simulation confirm the effectiveness of the equalizer and a lower BER are attained. Theoretical aalgorithm. It at The wavelet neural network equalizer based on error feedback advances the communication in the nonlinear time-varying channel.