Research on Ant Colony Algorithm for Continuous Function Optimization

2010 ◽  
Author(s):  
Jianping Sun ◽  
Jing Tian ◽  
Yifang Yuan
Keyword(s):  
Continuous Function ◽  
Ant Colony Algorithm ◽  
Function Optimization ◽  
Ant Colony

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.

scholarly journals An Improved Ant Colony Optimization Algorithm with Crossover Operator

2014 ◽  
Vol 8 (1) ◽  
pp. 96-100
Author(s):  
Junen Guo ◽  
Wenguang Diao
Keyword(s):  
Integrated Circuit ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Network Routing ◽  
Function Optimization ◽  
Ant Colony ◽  
Crossover Operator ◽  
Ant Colony Optimization Algorithm ◽  
Integrated Circuit Design ◽  
Optimization Function

Ant colony algorithm has been widely applied to lots of fields, such as combinatorial optimization, function optimization, system identification, network routing, robot path planning, data mining and large-scale integrated circuit design of integrated wiring, etc. And it achieved good results. But it still has one weak point which is the slowing convergence speed. To aim at the lacks, an improved ACO is presented. This paper studies a kind of improved ant colony algorithm with crossover operator which makes crossover operator among better results at the end of each iteration. The experiment results indicate that the improved ACO is effectual.

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.

Ant Colony Approach to Continuous Function Optimization

2000 ◽  
Vol 39 (10) ◽  
pp. 3814-3822 ◽  
Author(s):  
Mohit Mathur ◽  
Sachin B. Karale ◽  
Sidhartha Priye ◽  
V. K. Jayaraman ◽  
B. D. Kulkarni
Keyword(s):  
Continuous Function ◽  
Function Optimization ◽  
Ant Colony
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