LP-based Genetic Algorithm for the Minimum Graph Bisection Problem

Author(s):  
Michael Armbruster ◽  
Marzena Fügenschuh ◽  
Christoph Helmberg ◽  
Nikolay Jetchev ◽  
Alexander Martin
Author(s):  
Michael Armbruster ◽  
Marzena Fügenschuh ◽  
Christoph Helmberg ◽  
Nikolay Jetchev ◽  
Alexander Martin

Author(s):  
RONG-LONG WANG ◽  
KOZO OKAZAKI

The graph bisection problem is an important problem in printed circuit board layout and communication networks. Since it is known to be NP-complete, approximation algorithm have been considered. In this paper, we propose a so-called two-state ant colony algorithm for efficiently solving the problem. In the proposed algorithm two kinds of pheromone and two kinds of heuristic information are introduced to reinforce the search ability. The proposed algorithm is tested on a large number of instances and is compared with a heuristic algorithm and a genetic algorithm. The experimental results show that the proposed approach is superior to its competitors.


1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan

Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi

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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