Path Optimization of Container Multimodal Transportation Based on Improved Genetic Algorithm

2013 ◽  
Vol 433-435 ◽  
pp. 657-661
Author(s):  
Jing Li ◽  
Yue Fang Yang ◽  
Huan Liu
Keyword(s):  
Genetic Algorithm ◽  
Path Optimization ◽  
Multimodal Optimization ◽  
Intermodal Transport ◽  
Improved Genetic Algorithm ◽  
Multimodal Transportation ◽  
Transportation Time ◽  
Multimodal Transport ◽  
Transport Path ◽  
Crossover And Mutation

This paper mainly studies on the optimization and algorithm of multimodal transport path. The algorithm considered the transportation time, freight, the different transport ways, and the possibility of occurrence of facelift premise between each node. All of this determined the best path and the mixture of intermodal transport, which minimize the total freight fee. Take the complexity of multimodal optimization into account, this paper optimized the genetic algorithm to transport scheme. The certain population of crossover and mutation rules in application will continue to evolve by coding each path, finally achieve the solution of concrete steps.

Research on Multimodal Transport Shortest Path Based on the Genetic Algorithm

2015 ◽  
Vol 744-746 ◽  
pp. 1813-1816
Author(s):  
Shou Wen Ji ◽  
Shi Jin ◽  
Kai Lv
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Shortest Path ◽  
Optimal Solution ◽  
Path Model ◽  
Time Analysis ◽  
Multimodal Transportation ◽  
Network Time ◽  
Multimodal Transport ◽  
Crossover And Mutation

This paper focuses on the research of multimodal transportation optimization model and algorithm, designs an intermodal shortest time path model and gives a solution to algorithm, constructs a multimodal transport network time analysis chart. By using genetic algorithms, the transportation scheme will be optimized. And based on each path’s code, the population will be evolved to obtain the optimal solution by using crossover and mutation rules.

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.

BP Network Optimization Based on Improved Genetic Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1757-1763
Author(s):  
Wei Wei Sun ◽  
Yun Fei Yao ◽  
Chun Sheng Wang ◽  
Ye Gang Hu
Keyword(s):  
Genetic Algorithm ◽  
Network Optimization ◽  
Network Architecture ◽  
Training Method ◽  
Improved Genetic Algorithm ◽  
Bp Network ◽  
Network Training ◽  
Hierarchical Code ◽  
Adaptive Crossover ◽  
Crossover And Mutation

In view of the virtue and shortage of genetic algorithm and BP network, this paper proposes a new BP network training method based on improved genetic algorithm (IGA-BP). This algorithm uses hierarchical code, adaptive crossover and mutation, pruning similar chromosomes, dynamic supply new chromosomes and other operations, so the network structure and weight are optimized at the same time and the "premature" phenomenon is avoided. The simulation results show that the IGA-BP network architecture is simple, the convergence rate is quick, and has good approximation and generalization ability.

A New Binocular Camera Calibration Method Based on an Improved Genetic Algorithm

2011 ◽  
Vol 411 ◽  
pp. 602-608 ◽  
Author(s):  
Xiang Kui Jiang
Keyword(s):  
Genetic Algorithm ◽  
Camera Calibration ◽  
Calibration Method ◽  
Improved Genetic Algorithm ◽  
Linear Calibration ◽  
Calibration Methods ◽  
Calibration Accuracy ◽  
Encoding Method ◽  
The One ◽  
Crossover And Mutation

In this paper,an improved genetic algorithm was proposed,which is applicable to binocular camera calibration. On the one hand, conventional encoding method is improved so that variable search interval can be adjusted adaptively. On the other hand, crossover and mutation probability is varied by using superiority inheritance principle to avoid premature question. Experimental results show that the proposed method has a higher calibration accuracy and better robustness, compared to those of non-linear calibration methods. The proposed method is able to improve the performance of global optimization effectively.

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