A Genetic Algorithm for Bus Schedule Synchronization

2006 ◽  
Author(s):  
Fabian Cevallos ◽  
Fang Zhao
Keyword(s):  
Genetic Algorithm ◽  
Schedule Synchronization ◽  
Bus Schedule

A Study of Integrated Optimization Model and Method on Bus Scheduling and Operation Based on TransCAD

2013 ◽  
Vol 475-476 ◽  
pp. 749-753
Author(s):  
Qing Wan Xue ◽  
Li Li Gao ◽  
Zhuo Yang
Keyword(s):  
Genetic Algorithm ◽  
Waiting Time ◽  
Public Transport ◽  
Optimization Model ◽  
Transportation Planning ◽  
Related Factors ◽  
Planning Software ◽  
Bus Scheduling ◽  
Urban Public Transport ◽  
Bus Schedule

Improving the operation of urban public transport scheduling level is an important way to increase the attractiveness of public transport. In this paper, from the interests of bus passengers and bus companies, A model that maximize the benefit of company based on a certain benefit of passengers is attempted to be developed. Passenger waiting time, bus capacity and other related factors as the main variables were considered in this study. An algorithm based on genetic algorithm was developed to solve the problem efficiently. By utilizing the transit module of the transportation planning software TransCAD, a route system was established based on the bus line 183 in Nanjing. Thus, the bus schedule is created and the optimization results could be dynamically displayed.

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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