An effective dynamic weighted rule for ant colony system optimization

Author

Lee, SeungGwan ; Jung, TaeUng ; Chung, TaeChoong

Author_Institution

Dept. of Comput. Eng., Kyung Hee Univ., Seoul, South Korea

Volume

2

fYear

2001

fDate

2001

Firstpage

1393

Abstract

The ant colony system (ACS) algorithm is new metaheuristic for hard combinational optimization problems. It is a population-based approach that exploits positive feedback as well as greedy search. It was first proposed for tackling the well known traveling salesman problem (TSP). We introduce a new version of the ACS based on a dynamic weighted updating rule. Implementation to solve TSP and the performance results under various conditions are conducted, and the comparison between the original ACS and the proposed method is shown. It turns out that our proposed method can compete with the original ACS in terms of solution quality and computation speed for these problem

Keywords

algorithm theory; evolutionary computation; feedback; search problems; travelling salesman problems; ant colony system optimization; computation speed; dynamic weighted rule; dynamic weighted updating rule; greedy search; hard combinational optimization problems; population-based approach; positive feedback; solution quality; traveling salesman problem; Ant colony optimization; Cities and towns; Genetics; Legged locomotion; Neural networks; Simulated annealing; Traveling salesman problems;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2001. Proceedings of the 2001 Congress on

Conference_Location

Seoul

Print_ISBN

0-7803-6657-3

Type

conf

DOI

10.1109/CEC.2001.934354

Filename

934354