The Advanced Ant Colony Algorithm and its Application

Author

Yonghua, Zhu ; Jin, Xu ; Wentong, Ye ; Yong, Chen

Author_Institution

Dept. of ISEE, Quzhou Coll., Quzhou, China

Volume

3

fYear

2011

fDate

6-7 Jan. 2011

Firstpage

664

Lastpage

667

Abstract

Ant Colony Optimization (ACO) is a novel bionic evolutionary algorithm for solving complex combinatorial optimization problems. This research approach lies at initial stage at present, and a new adaptive ant algorithm is proposed for the traditional ant algorithm easily appears precocious and stagnation behavior phenomenon in this paper. And the traditional parameter of pheromone of ant colony algorithm is self-adaptive adjusted. Selecting a number of typical TSP problems to experiment, the results are indicated that the new adaptive ant colony algorithm has a better ability to search the global optimal solution and have better stability and astringency.

Keywords

evolutionary computation; optimisation; self-adjusting systems; TSP problem; adaptive ant colony algorithm; advanced ant colony; ant colony optimization; bionic evolutionary algorithm; complex combinatorial optimization problem; self-adaptive system; stagnation behavior phenomenon; Acceleration; Algorithm design and analysis; Cities and towns; Convergence; Optimization; Presses; Probability; ACS; pheromone; self-adaptive;

fLanguage

English

Publisher

ieee

Conference_Titel

Measuring Technology and Mechatronics Automation (ICMTMA), 2011 Third International Conference on

Conference_Location

Shangshai

Print_ISBN

978-1-4244-9010-3

Type

conf

DOI

10.1109/ICMTMA.2011.737

Filename

5721574