Chaos-Ant Colony Algorithm and its application in continuous space optimization

2008 ◽  
Author(s):  
Yibao Chen ◽  
Hongmei Xu ◽  
Tiezhu Ma
Keyword(s):  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Continuous Space

scholarly journals Ant Colony Optimization Algorithm Model Based on the Continuous Space

2016 ◽  
Vol 12 (12) ◽  
pp. 27 ◽  
Author(s):  
Xuepeng Huang
Keyword(s):  
Ant Colony Algorithm ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Optimization Strategy ◽  
Ant Colony Optimization Algorithm ◽  
Continuous Space ◽  
Distributed Parallel Computing ◽  
Branching Factor ◽  
Positive Feedback Mechanism ◽  
Distribution Algorithms

Ant colony algorithm is a heuristic algorithm which is　fit for solving complicated combination optimization.It showed great advantage on solving combinatorial optimization problem since it was proposed. The algorithm uses distributed parallel computing and positive feedback mechanism, and is easy to combine with other algorithms.This ant colony algorithm has already been widespread used in the field of discrete space optimization, however, is has been rarely used for continuous space optimization question.On the basis of basic ant colony algorithm principles and mathematical model, this paper proposes an ant colony algorithm for solving continuous space optimization question.Comparing with the ant colony algorithm, the new algorithm improves the algorithm in aspects of ant colony initialization, information density function, distribution algorithms, direction of ant colonymotion, and so on. The new algorithm uses multiple optimization strategy, such as polynomial time reduction and branching factor, and improves the ant colony algorithm effectively.

A Mixed Ant Colony Algorithm for Problem of Function Optimization

2011 ◽  
Vol 101-102 ◽  
pp. 216-219
Author(s):  
Yan Jun Luo ◽  
Zhao Yu Bei
Keyword(s):  
Ant Colony Algorithm ◽  
Local Optimization ◽  
Function Optimization ◽  
Ant Colony ◽  
Continuous Space ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Ant colony algorithm has disadvantages such as long researching time and easily relapsing into local optimization. Artificial fish-swarm algorithm is presented to conquer the disadvantages. The combination of the two algorithms is applied in function optimization to overcome the limitation that the ant colony algorithm does not fit to solve continuous space optimization. The tested function shows the effect of the method.

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