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.

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.

An Improved Genetic Algorithm for Digital Facility Layout Optimization Oriented Multi-Species and Variable-Batch Production Mode

2013 ◽  
Vol 694-697 ◽  
pp. 3632-3635
Author(s):  
Dao Guo Li ◽  
Zhao Xia Chen
Keyword(s):  
Genetic Algorithm ◽  
Facility Layout ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Improved Genetic Algorithm ◽  
Layout Problem ◽  
Batch Production ◽  
Production Mode ◽  
Slow Convergence ◽  
Weak Points

When solving facility layout problem for the digital workshop to optimize the production, the traditional genetic algorithm has its flaws with slow convergence speed and that the accuracy of the optimal solution is not ideal. This paper analyzes those weak points and proposed an improved genetic algorithm according to the characteristics of multi-species and variable-batch production mode. The proposed approach improved the convergence speed and the accuracy of the optimal solution. The presented model of GA also has been tested and verified by simulation.

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.

SVM Classification Model Parameters Optimized by Improved Genetic Algorithm

2014 ◽  
Vol 889-890 ◽  
pp. 617-621 ◽  
Author(s):  
Qing Hua Mao ◽  
Hong Wei Ma ◽  
Xu Hui Zhang
Keyword(s):  
Genetic Algorithm ◽  
Classification Model ◽  
Function Parameter ◽  
Model Parameters ◽  
Improved Genetic Algorithm ◽  
Data Set ◽  
Svm Classification ◽  
Mutation Operators ◽  
Factor C ◽  
Defects Classification

SVM classification model has been widely applied to mechanical equipment fault diagnosis and material defects classification. It is difficult to choose the optimal value of penalty factor C and kernel function parameter for SVM model. Therefore, an improved genetic algorithm to optimize SVM parameters is put forward, which improves crossover and mutation operators and enhances convergence properties by using the best individual retention strategy. UCI data set is used to verify the algorithm. The testing results show that the algorithm can quickly and effectively select optimal SVM parameters and improve SVM classification accuracy.

scholarly journals Dynamic Route Guidance Using Improved Genetic Algorithms

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Zhanke Yu ◽  
Mingfang Ni ◽  
Zeyan Wang ◽  
Yanhua Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Steady State ◽  
Fitness Function ◽  
Population Diversity ◽  
Route Guidance ◽  
Improved Genetic Algorithm ◽  
Simulation Results ◽  
Guidance Algorithm ◽  
Dynamic Route

This paper presents an improved genetic algorithm (IGA) for dynamic route guidance algorithm. The proposed IGA design a vicinity crossover technique and a greedy backward mutation technique to increase the population diversity and strengthen local search ability. The steady-state reproduction is introduced to protect the optimized genetic individuals. Furthermore the junction delay is introduced to the fitness function. The simulation results show the effectiveness of the proposed algorithm.

Sign in / Sign up

Export Citation Format

Share Document