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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

Multiple Load Flow Solutions in Electric Power Systems Based on Genetic Algorithm

Methods for satisfying the power balance requirement and the voltage magnitude costraint are developed and incorporated into the genetic algorithm method to form a constrained genetic algorithm for solving the load flow problem.The robustness of the load flow algorithm is enhanced by the dynamic population.the technique for accelerating the convergence of the optimisation process and the network node sequencing procedure described in the paper.The efficiency and feasibility of the developed CGALF algorithm have been tested using KK 11 node system under light load and heavy load conditions.     

Fast Algorithm About Unit Commitment Based on Revised BP Artificial Neural Network and Dynamic Search

In order to reduce the operation cost and optimize the unit commitment,the fast algorithm about unit commitment based on revised BP ANN(Artificial Neural Network) and dynamic search is discussed.The BP ANN is trained with Levenberg-Marquardt algorithm,which aiming at its drawback of the storage of some matrices that can be quite large for certain problems,and a revised algorithm is presented.The BP ANN is used to generate a pre-schedule according to the input load profile.Then the dynamic search is performed some stages where the commitment states of some of the units are not certain.The experimental results indicate that the proposed algorithm can reduce the execution time and memory space without degrading the quality of the generation schedule.     

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.     

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。     

Analysis on Application in Profile Optimizing Based on Genetic Algorithms

In order to achieving the Automation of profile optimization and offering more reasonable schemes,the author proposes a kind of computer method based on hereditary algorithm,which is using mathematic methods to simplify the profile lines as a list of points and using cut-fill gross as aim's function and using restriced conditions to control the profile lines,the authors optimize the list of points of changing slopes,the results of the method are more accurate than the artificial design and can provide many kinds of schemes within short time.In the actual projects we can properly increase the condition restraints to design more high-quality lines.If GIS platform is added to it,the profile can be optimized more effectively and more intuitionisticly.     

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.     

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.     

Hybrid Intelligent Method for Dynamic Reactive Power Optimization

In dynamic reactive power optimization problem(DRPO), action number constraints of discrete variables should be considered.By integrating immune genetic algorithm(IGA) and nonlinear interior point method(NIPM), a hybrid method for DRPO is proposed.First,the original DRPO problem is converted to a continuous optimization problem by relaxing the discrete variables,and the solution is obtained by NIPM.Then,according to the feature of control variables,the original DRPO problem is decomposed into a continuous optimization sub-problem and a discrete optimization sub-problem.The discrete optimization sub-problem is solved by IGA,and the continuous one is solved by NIPM.By solving the two sub-problems alternately,the optimal solution of the DRPO can be obtained.The proposed hybrid method combines advantages of IGA and NIPM,and finds the approximate optimal solution of DRPO.Numerical simulations on the IEEE 14 bus system illustrate that the proposed hybrid method is effective.