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.     

Application of Improved Hierarchic Genetic Algorithm to Damage Detection of the Main Girder for Cable-stayed Bridge

In the process of damage identification for high-order nonlinear structure such as cable-stayed bridges by the standard genetic algorithm, premature convergence would appear. In order to avoid this, an improved hierarchic genetic algorithm was proposed. The cable force change was used to establish the optimization function and threetypes of standard genetic algorithm were combined with variable fine-tuning and hierarchic strategy.To establish a hierarchical genetic algorithm with catastrophe characteristics A single-tower cable-stayed bridge model was used in the numerical simulation and the result showed that the probability of premature convergence was reduced in the improved hierarchic genetic algorithm and and the cable-stayed bridge damage was identified effectively. The anti-noise performance was better.     

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.     

Researches and Advances in Structural Damage Dynamic Identification Technology

This paper describes the theory and research progress of dynamic structural damage identification technique of the last twenty years.The basic theory of dynamic structural damage identification techniques is introduced first.Next,three important aspects of damage identification are discussed: identification mechanisms,dynamic parameters,and identification algorithms.Finally,a review of the state-of-the-art and development trends in this field is presented.     

Distribution Network Structure Planning Based on Multi-population Immune Genetic Algorithm

Distribution Network Structure planning is a complex combinatorial optimization problem, which is difficult to solve properly by using traditional optimization methods. The authors put forward Multiple Population Immune Genetic Algorithm (MPIGA)for optimal planning of distribution network structure, and do optimal search to different aspects of optimization goals. During the genetic evolution process, biologic immune mechanism is introduced to do some immune operator operation on chromosomes of each population, which can interact mutually by the shift of excellent units. By this way, it can effectively prevent population retrogression, promote diversity and the whole optimal searching ability of genetic algorithm. In order to minimize network annual expenditure, a mathematic model is established. The optimal solution is obtained by this algorithm, which has been illustrated effectively by specific examples at the same time.     

Step-by-step Method of Structural Damage Diagnosis

Base on the existing step-by-step structural damage diagnosis method, a three-step structural damage diagnosis method is developed. According to the analysis of the characteristic of genetic algorithm, a new idea of excluding the undamaged element by means of step-by-step evaluation is developed to deal with the damage diagnosis problem. In this method, sensitivity genetic algorithm is used to exclude the undamaged element step by step and residual force method is used to fix the damage district. According to the simulation research of a 2D framed structure, it is shown that the step-by-step structural damage diagnosis method can gain the ideal results when it's difficult for a single damage diagnosis method to get the good results. It is also indicated that sensitivity genetic algorithm is a good way to exclude the undamaged element step by step.     

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     

Improved Genetic Algorithm of Float Encoding and Its Application

As an optimal method, Genetic Algorithm has obvious advantages, which is based on the nature selection and genetic transmission mechanisms such as high collateral,stochastic,self-reliance. but when in practical application, it usually has problems of premature convergence and result swing near optimum value.To solve the problem of premature convergence, the method called Monte-Carlo is adopted to prevent the algorithm from local optimal, and to the problem of result swing, the method changing the hunting zone dynamically is proposed to improve the accuracy of the optimal result. Further more, it devises programs to optimize the test functions of two famous optimal methods. The test results indicate that the improved Genetic Algorithm is valid, which can not only avoid local optimal but also improve the accuracy of the optimal result.     

改进遗传算法在饲料配方设计中的应用

针对现有饲料配方软件的局限性,以白绒山羊饲料配方为例,提出一种基于改进遗传算法的求解方法。与标准遗传算法相比,采用随机联赛选择替代轮盘赌模型,优化了选择策略;采用不同的随机数交叉和高斯变异,改进了交叉算子与变异算子。测试结果表明,该方法具有良好的运算效率,拓宽了搜索空间,提高了对重点区域的搜索能力,降低了成本,为复杂问题的优化提供了一种新的思路。     

白三叶基因工程改良研究进展

白三叶（Trifolium repens L.）作为优良牧草和地被植物具有重要的应用价值。但其耐盐碱、干旱能力差等原因致使应用受到广泛限制,转基因技术的快速发展为白三叶种质创新提供了有效技术手段,为开展白三叶分子育种奠定了基础。本文就近30年来有关白三叶组织培养、转化方法、转基因遗传改良方面的研究进展进行了综述,并对其应用进行了展望。     

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.     

Improved Genetic Algorithm and Its Application in TSP

Crossover operator and mutation operator are basic operators of genetic algorithm , which play important roles in improving the quality of the solution populations. We propose the improved crossover operator , mutation operator and rotation operator on the basis of good point set with the traveling salesman problem , the experimental result shows these operators are very efficient.     

黑龙江省主栽水稻品种耐延迟型冷害能力鉴定

采用人工气候箱模拟低温的方法,对黑龙江省30个主栽水稻品种进行了萌发期和分蘖期耐冷性鉴定,以期筛选出耐延迟型冷害强的品种。结果显示,不同品种在萌发期耐冷性上差异较大,筛选出耐延迟型冷害较强的品种为龙粳16号、龙粳18号、龙粳22号和松粳12号,这些品种在10℃下低温发芽率高且分蘖期低温处理后延迟生育日数少,是可以作为直播栽培的品种。     

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。     

棉花抗逆遗传改良技术与应用

病害、干旱、盐碱等逆境胁迫是棉花生产实现高产稳产的主要影响因素,利用传统育种和现代生物技术对现有棉花育种资源的抗逆性状进行遗传改良是实现高产、优质、多抗育种目标,培育突破性棉花新品种的有效途径。综述了中国农业科学院棉花研究所棉花抗逆遗传改良团队近年来在棉花抗逆育种新材料创制、抗逆育种关键技术研发与新品种培育方面的最新进展,并且提出了未来我国棉花抗逆遗传改良的研究方向。     

An Improved Genetic Algorithm for Optimum Design of Power Transformers

In order to enhance the ability of global searching for genetic algorithm in power transformers optimization design, some interrelated key technique problems such as encoding method, genetic operators, restrict condition, fitness function for the traditional genetic algorithm are further reformed. An Improved Genetic Algorithm (IGA) is developed. The optimal results of a representative mathematical example show that IGA has high efficiency of global searching. At the same time, a multi-objective algorithm based on IGA is studied in this paper. IGA is applied to the single and multi-objective optimum design of S9 power transformers for the first time. All the achievements in the paper are verified a practical S9-1000/10 kV power transformer. All the optimization results are satisfactory and show that IGA has powerful ability of global searching, excellent solution precision and has a bright application prospect in the fields of power transformers design.     

离子束生物技术在小麦遗传改良中的应用

简述了离子束诱变育种的机理,概述了离子束在小麦遗传改良中所取得的成果,并对提高离子注入小麦的诱变育种效率的方法进行了分析。