The Optimal Allocation of Finishing Train in Steel Rolling Based on Improved Genetic Algorithm

2013 ◽  
Vol 433-435 ◽  
pp. 720-724
Author(s):  
Hong Xia Liu ◽  
Xin Chen
Keyword(s):  
Genetic Algorithm ◽  
Optimal Allocation ◽  
Empirical Distribution ◽  
Rolling Force ◽  
Optimization Methods ◽  
Improved Genetic Algorithm ◽  
Load Curve ◽  
Distribution Method ◽  
Steel Rolling ◽  
Mutation Operators

The central issue of finishing train is that we should distribute the thickness of each exit with reason and determine the rolling force and relative convexity. The optimization methods currently used are empirical distribution method and the load curve method, but they both have drawbacks. To solve those problems we established a mathematical model of the finishing train and introduced an improved Genetic Algorithm. In this algorithm we used real number encoding, selection operator of a roulette and elitist selection and then improved crossover and mutation operators. The results show that the model and algorithm is feasible and could ensure the optimal effect and convergence speed. The products meet the production requirements.

Neural Network Based On the Improved Genetic Algorithm to Optimize Component Separation Conditions of Small Peptide

2011 ◽  
Vol 284-286 ◽  
pp. 261-264
Author(s):  
Jing Wen Tian ◽  
Feng Jun Wu ◽  
Hui Chen ◽  
Jing Di Ren
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Liquid Chromatography ◽  
Forecast Model ◽  
Optimization Methods ◽  
Reversed Phase ◽  
Improved Genetic Algorithm ◽  
Small Peptide ◽  
Neural Network Theory ◽  
Separation Conditions

Reference to traditional optimization methods, neural network based on improved genetic algorithm is used in optimization of reversed phase chromatography pluralistic isocratic mobile phase separation conditions. With detailing the combination of the improved genetic algorithm and neural network theory, the optimization process for the liquid chromatography conditions is introduced in details. Used this method to small peptide RP chromatography optimization, after searching operation, the establishment of an effective separation of forecast model receives satisfactory predictive value, which can prove that this method can be used in optimization of drug liquid chromatography conditions.

Simulation of the Pressure-Bearing Capacity Test Model in Architectural Decoration Design

2014 ◽  
Vol 716-717 ◽  
pp. 391-394
Author(s):  
Li Mei Guo ◽  
Ai Min Xiao
Keyword(s):  
Genetic Algorithm ◽  
Bearing Capacity ◽  
Test Method ◽  
Test Model ◽  
Improved Genetic Algorithm ◽  
Good Convergence ◽  
Mutation Operators ◽  
Process Pressure ◽  
Calculation Process ◽  
Crossover And Mutation

in architectural decoration process, pressure-bearing capacity test is the foundation of design, and is very important. To this end, a pressure-bearing capacity test method in architectural decoration design is proposed based on improved genetic algorithm. The selection, crossover and mutation operators in genetic algorithm are improved respectively. Using its fast convergence characteristics eliminate the pressure movement in the calculation process. The abnormal area of pressure-bearing existed in buildings which can ensure to be tested is added, to obtain accurate distribution information of the abnormal area of pressure-bearing. Simulation results show that the improved genetic algorithm has good convergence, can accurately test the pressure-bearing capacity in architectural decoration.

Transmission Network Expansion Planning under Improved Genetic Algorithm

2011 ◽  
Vol 347-353 ◽  
pp. 1458-1461
Author(s):  
Hong Fan ◽  
Yi Xiong Jin
Keyword(s):  
Genetic Algorithm ◽  
Linear Optimization ◽  
Large Integer ◽  
Transmission Network ◽  
Improved Genetic Algorithm ◽  
Network Expansion ◽  
Expansion Planning ◽  
Mutation Operators ◽  
Crossover And Mutation ◽  
Search Capability

Improved genetic algorithm for solving the transmission network expansion planning is presented in the paper. The module which considered the investment costs of new transmission facilities. It is a large integer linear optimization problem. In this work we present improved genetic algorithm to find the solution of excellent quality. This method adopts integer parameter encoded style and has nonlinear crossover and mutation operators, owns strong global search capability. Tests are carried out using a Brazilian Southern System and the results show the good performance.

Weighted K-Means Clustering Analysis Based on Improved Genetic Algorithm

2014 ◽  
Vol 511-512 ◽  
pp. 904-908 ◽  
Author(s):  
Tong Jie Zhang ◽  
Yan Cao ◽  
Xiang Wei Mu
Keyword(s):  
Genetic Algorithm ◽  
Clustering Analysis ◽  
Local Optimum ◽  
Speed Of Convergence ◽  
Improved Genetic Algorithm ◽  
Low Speed ◽  
Dynamic Mutation ◽  
Mutation Operators

An algorithm of weighted k-means clustering is improved in this paper, which is based on improved genetic algorithm. The importance of different contributors in the process of manufacture is not the same when clustering, so the weight values of the parameters are considered. Retaining the best individuals and roulette are combined to decide which individuals are chose to crossover or mutation. Dynamic mutation operators are used here to decrease the speed of convergence. Two groups of data are used to make comparisons among the three algorithms, which suggest that the algorithm has overcome the problems of local optimum and low speed of convergence. The results show that it has a better clustering.

The Application of Improved Genetic Algorithm in Distribution Network Reconfiguration

2012 ◽  
Vol 490-495 ◽  
pp. 1689-1693 ◽  
Author(s):  
Wen Hua Zhou ◽  
Xiao Long Chen
Keyword(s):  
Genetic Algorithm ◽  
Distribution Network ◽  
Estimation Method ◽  
Computation Time ◽  
Original Data ◽  
Engineering Practice ◽  
Network Reconfiguration ◽  
Improved Genetic Algorithm ◽  
Load Curve ◽  
Distribution Network Reconfiguration

This paper presents an improved algorithm for distribution network reconfiguration. The objectives is to minimized the power loss and the percentage of over-voltage. Based on the traditional genetic algorithm, the adaptable function selection and the disposal of terminating evolution criteria has been improved, to improve the convergence of the system and the calculation accuracy. At the same time, using a new estimation method to correct the load curve. This approach takes full advantage of existing distribution network's original data, it can significantly reduce the computation time, its accuracy to meet the requirements of engineering practice. Test results have been presented along with the discussion of the algorithm.

scholarly journals Genetic algorithm method for solving the optimal allocation of response resources problem on the example of polish zone of the Baltic Sea / Metoda algorytmów genetycznych do rozwiązywania problemów optymalnej alokacji środków do zwalczania rozlewów olejowych na przykładzie polskiej strefy Morza Bałtyckiego

2016 ◽  
Vol 38 (1) ◽  
pp. 291-310
Author(s):  
Kinga Łazuga ◽  
Lucjan Gucma
Keyword(s):  
Genetic Algorithm ◽  
Optimal Allocation ◽  
Optimization Methods ◽  
Genetic Optimization ◽  
Location Allocation ◽  
Second Stage ◽  
Genetic Algorithm Method ◽  
Logistical Problem ◽  
The Baltic ◽  
Two Stages

Abstract The paper presents research related to optimal allocation of response vessels. Research belong to the logistical problem, location-allocation type (LA). Research is focused on vessels belongs to polish Search and Rescue. For the optimal allocation of resources used two-stages method wherein the first stage, using genetic optimization methods and consist in such allocation of response vessels to minimize costs of the spill at sea. In the second stage uses an accurate simulation model of oil spill combat action to verify the solutions obtained by genetic algorithm method.

scholarly journals Double Genes Improved Genetic Algorithm for Solving Two-Dimensional Rectangular Layout Problem

2021 ◽  
Vol 2138 (1) ◽  
pp. 012007
Author(s):  
Min Cui ◽  
Kun Yang ◽  
Xiangming Deng ◽  
Shuqing Lyu ◽  
Miaomiao Feng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Diversity ◽  
Population Diversity ◽  
Utilization Rate ◽  
Two Dimensional ◽  
Improved Genetic Algorithm ◽  
Layout Problem ◽  
Double Mutation ◽  
Mutation Operators ◽  
Random Crossover

Abstract Two-dimensional rectangular layout is according to the number of rectangular pieces and the size of the area of the rectangular pieces into the plate. Depending on the iteration of population in genetic algorithm, better utilization rate of plate is obtained. However, due to the characteristics of vertical and horizontal rows of rectangular pieces, relying on the sequence of rectangular pieces alone as the gene cannot guarantee the genetic diversity of the population, and leads to premature algorithm. In view of the special characters of rectangular layout, Double Genes improved genetic algorithm is proposed according to the order of rectangular layout and its own placement characteristics. In order to improve population diversity, Angle genes were added on the basis of rectangular sequencing genes. In view of the particularity of double genes, double random crossover operators and double mutation operators are proposed to improve the population diversity and randomness of genetic algorithm. Experimental results show the effectiveness of the improved algorithm.

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