DocumentCode :
460831
Title :
Multiagent Search Strategy for Combinatorial Optimization Problems in Ant Model
Author :
Hong, Seok Mi ; Lee, SeungGwan
Author_Institution :
Sch. of Comput., Inf. & Commun., Eng. Sangji Univ., Wonju
Volume :
1
fYear :
2006
fDate :
Nov. 2006
Firstpage :
528
Lastpage :
531
Abstract :
Ant colony system (ACS) is a meta heuristic approach based on biology in order to solve combinatorial optimization problem. It is based on the tracing action of real ants that accumulate pheromone on the passed path and uses as communication medium. In order to search the optimal path, it is necessary to make a search for various edges. In existing ACS, the local updating rule assigns the fixed pheromone value to visited edge in all process. In this paper, modified local updating rule gives the pheromone value according to the number of visiting and the edge´s distance between visited nodes. Our approach can have less local optima than existing ACS and can find better solution by taking advantage of more information during searching
Keywords :
knowledge based systems; multi-agent systems; optimisation; search problems; ant colony system; ant model; ant tracing action; combinatorial optimization problem; communication medium; meta heuristic approach; multiagent search strategy; optimal path search; pheromone value; updating rule; Ant colony optimization; Computer science education; Feedback; Genetic algorithms; Heuristic algorithms; Legged locomotion; Simulated annealing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Security, 2006 International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
1-4244-0605-6
Electronic_ISBN :
1-4244-0605-6
Type :
conf
DOI :
10.1109/ICCIAS.2006.294190
Filename :
4072143
Link To Document :
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