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.     

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.     

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。     

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.     

Application Study on Optimal Sensor Placement Technique of Cable-stayed Bridge

Based on the modal assurance criterion, using simulated annealing algorithm, the dynamic characteristic data of cable-stayed bridge are extracted, optimal acceleration sensor layout scheme of main beam is obtained. According to this optimization scheme, we carry out a dynamic characteristics test on one cable-stayed bridge. The results between test data and finite element modal calculation are very closely. Theory technique research and field modal test demonstrated that the simulated annealing algorithm based on modal assurance criterion is effective and viable for optimal acceleration sensors placement of cable-stayed bridge.     

Dynamic Finite Element Model Updating of Prestressed Concrete Continuous Girder Bridge Based on Real-Coded Accelerating Genetic Algorithm

The dynamic finite element model (FEM) of a prestressed concrete continuous girder bridge (PCCGB), named Zhangjiagang river main bridge, was established and updated based on the results of field ambient modal testing using real-coded accelerating genetic algorithm (RAGA), which objective function was defined based on frequency index and correlation coefficient index for evaluating the updated FEM. The dynamic FEM of the bridge was updated based on seven experimental modal parameters. The prediction ability of the updated FEM were evaluated based on three experimental modal parameters. The updated results and prediction ability of updated FEMs indicated that they can reflect adequately the dynamic characteristics of actual PCCGB by using the above objective function and RAGA.     

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.     

Damage Detection for Beam and Bridge under Dead Load

We address damage detection based on dead loads in monitoring the health of beams and bridges. We discuss the damage characteristics of statically indeterminate beams under dead loads and analyze monitoring parameters and sensor placement in monitoring. We used an amended genetic algorithm to solve the optimization problem under constrained parameters. Damage in a beam was detected successfully based on dead loads with limited sensor allocations. We present a way to detect damage based on dead loads in bridge health monitoring.     

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.     

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.     

模式识别的斜拉桥损伤诊断动力指纹与识别算法

为有效并准确诊断出斜拉桥损伤,对基于模式识别的斜拉桥损伤诊断方法进行了研究。选取易于测试出的低阶模态频率和部分关键点竖向振型数据为动力指纹,无需模态扩展或模型缩聚。研究并采用全因子设计进行动力指纹库的创建,可精确评估设定的损伤因子及其交互作用对损伤识别结果的影响。设计并增加了带随机误差的动力指纹库样本集。编制了基于Matlab的模式识别的多种算法,重点研究了精确度高的多层感知器识别算法及其提高该算法预测准确率的装袋集成算法。最后给出一座单塔双跨双索面斜拉桥的多种识别算法的损伤诊断过程和结果,得到一种可包容测试随机误差的高精确度斜拉桥损伤诊断评估模型。     

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     

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.     

Construction Technology for the Main Bridge Pier of Citong Bridge in Quanzhou City

The Citong Bridge is located in the river estuary section with 4 meter tide range and mixed geological structure. In this paper, the technique for construction of the main bridge pier was presented in detail, which consists of the construction process, technological requirements, main construction parameters and amendment on defects. It is of great value for reference to the similar works.     

Optimization Algorithm Research on the Arch Axis of Steel Box-Concrete Composite Arch Bridge

The paper proposed an innovative optimized design philosophy of the optimal arch axis of steel box-concrete arch bridge. Formulas to calculate the arch axis at the arch crown and the arch springing of this type of arch bridge were respectively derived wit     

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。     

基于改进蚁群算法的低碳冷链物流配送模型优化与应用

实现持续低碳化发展是冷链物流技术创新与优化的重要方向。基于成本约束视角,将冷链物流存储、配运、交付诸环节的运营成本和碳排放成本纳入企业总成本目标函数,构建冷链物流配送路径优化模型,使用蚁群算法与遗传算法变异算子结合求解,提高了算法迭代收敛速度。以医药冷链物流配送为例进行算法仿真优化与分析,验证了降低物流成本与节能减排可协同优化,获得良好的经济效益。模型和算法对环境可持续视角下的冷链物流配送路径问题有理论和实践意义。     

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.     

An Improved Learning Algorithm for BP Model and its Application to Load Forecasting in Power Systems

This paper analyses the cause of very slow learning rate during the time of ap-proaching the minim um poi nt in con vertional BP algorithm, by introducing weight factor into themodification of the connection weight,an improved leavning algorithm for BP model is presented,itcan sign ificantly in crease the learning rate and improve con vergent behaviour.As an illustrative ap-plication example,the n umeriCal resu It of the medium-term load forecasting is given.