Improved Ant Colony Algorithm and Application in Sequence Images of Prostate DWI Registration

2014 ◽  
Vol 1049-1050 ◽  
pp. 530-534
Author(s):  
Xiao Ping Zong ◽  
Hai Bin Zhang ◽  
Lei Hao ◽  
Pei Guang Wang
Keyword(s):  
Continuous Function ◽  
Diffusion Weighted Imaging ◽  
Ant Colony Algorithm ◽  
Transition Probabilities ◽  
Function Optimization ◽  
Experimental Results ◽  
Ant Colony ◽  
Optimal Solutions ◽  
Ant System ◽  
Improved Ant Colony Algorithm

Because of the drift which exists in sequence image of prostate DWI (Diffusion Weighted Imaging), the global ant colony algorithm is introduced into the paper for registration optimization. The paper introduces an ant colony algorithm for continuous function optimization, based on max-min ant system (MMAS). This paper controls the transition probabilities and enhances the abilities of ants seeking globally optimal solutions by adding an adjustable factor in the basic ant colony algorithm and updating the local pheromone and global pheromone. Experimental results verify the effectiveness of the algorithm.

Optimization on NoC Mapping Based on Improved Ant Colony Algorithm

2014 ◽  
Vol 539 ◽  
pp. 280-285 ◽  
Author(s):  
Dong Li
Keyword(s):  
Power Consumption ◽  
Ant Colony Algorithm ◽  
Simulation Experiment ◽  
Local Optimization ◽  
Ant Colony ◽  
Optimal Solutions ◽  
Optimization Scheme ◽  
Tabu List ◽  
Mapping Algorithm ◽  
Improved Ant Colony Algorithm

Traditional ant colony mapping algorithm not only has big power consumption, but also is easy to be trapped into local optimization on NoC mapping, for which the paper proposes an optimization scheme based on improved ant colony algorithm. Firstly, the parameters are for initialization operation. Secondly, tabu list is used to solve them, and the solutions are for local optimization of optimal solutions by using 2-opt algorithm. Lastly, pheromone rules are updated. Simulation experiment indicates that compared with traditional ant colony mapping algorithm, NoC mapping optimization scheme based on improved ant colony algorithm not only has better performance on mapping power consumption, but also is not easy to be trapped into local optimization.

A Ships Parameter Identification Arithmetic Based on Improved Ant Colony Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 3450-3455 ◽  
Author(s):  
Ting Wei Liu ◽  
Hong Bo Wang ◽  
Yang Dang ◽  
Shu Ren Yang
Keyword(s):  
System Identification ◽  
Continuous Function ◽  
Nonlinear Model ◽  
Ant Colony Algorithm ◽  
Ship Motion ◽  
Ant Colony ◽  
Identification Accuracy ◽  
Robust Method ◽  
Good Identification ◽  
Improved Ant Colony Algorithm

In this paper, an Improved Ant Colony Algorithm is applied to the identification of the ship motion modelNomotos 1st-order nonlinear model. A robust method based on Ant Colony Algorithm is proposed for the optimization of the continuous function. The transfer criterion of the ants between each layer and global pheromone updating process are described. Some experiments results show that this method has good identification accuracy. The algorithm is feasible and effective, is of enormous significance to the system identification of ship motion.

An Improved Ant-Colony Algorithm Based on Cloud Model Theory

2010 ◽  
Vol 143-144 ◽  
pp. 1204-1206
Author(s):  
Xian Min Wei
Keyword(s):  
Model Theory ◽  
Ant Colony Algorithm ◽  
Cloud Model ◽  
Optimal Solution ◽  
Optimal Performance ◽  
Experimental Results ◽  
Ant Colony ◽  
Local Optimal Solution ◽  
Cloud Model Theory ◽  
Improved Ant Colony Algorithm

This paper studies one method of cloud model to effectively limit the using Ant-colony Algorithm into local optimal solution, and experimental results show that this Ant-colony Algorithm can improve the speed of global search and optimal performance significantly.

An Improved Ant Colony Algorithm Applied in the TSP Problem

2013 ◽  
Vol 380-384 ◽  
pp. 1738-1741
Author(s):  
Meng Lan Wang
Keyword(s):  
Ant Colony Algorithm ◽  
Group Behavior ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Optimal Solutions ◽  
Heuristic Information ◽  
Random Probability ◽  
Positive Feedback Mechanism ◽  
Maximum Minimum ◽  
Improved Ant Colony Algorithm

Ant colony algorithm is a kind of intelligent algorithm imitating the group behavior of ants. The positive feedback mechanism is not only its advantage which makes the ant colony algorithm quickly converge to optimal solutions of a problem, but also its defect which makes it easy to fall into the local optimal solutions. ACS and MMAS are the two typically improved ant algorithms by introducing the pseudo random probability selection rule and maximum-minimum pheromone restriction rule to accelerate the converging speed of this algorithm and avoid falling into local optimal solutions. At present, there is no algorithm put forward to improve the algorithm using the effect of the heuristic information. This paper presents an improved ant colony algorithm based on the heuristic information of direction, and provides a new idea for the study on the improved ant colony algorithm.

Sign in / Sign up

Export Citation Format

Share Document