A Study about Additional Reinforcement in Local Updating and Global Updating for Efficient Path Search in Ant Colony System 


Vol. 10,  No. 3, pp. 237-242, Jun.  2003
10.3745/KIPSTB.2003.10.3.237


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  Abstract

Ant Colony System (ACS) Algorithm is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem (TSP). In this paper, we introduce ACS of new method that adds reinforcement value for each edge that visit to Local/Global updating rule. and the performance results under various conditions are conducted, and the comparision 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 to these problem.

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[IEEE Style]

S. G. Lee and T. C. Chung, "A Study about Additional Reinforcement in Local Updating and Global Updating for Efficient Path Search in Ant Colony System," The KIPS Transactions:PartB , vol. 10, no. 3, pp. 237-242, 2003. DOI: 10.3745/KIPSTB.2003.10.3.237.

[ACM Style]

Seung Gwan Lee and Tae Choong Chung. 2003. A Study about Additional Reinforcement in Local Updating and Global Updating for Efficient Path Search in Ant Colony System. The KIPS Transactions:PartB , 10, 3, (2003), 237-242. DOI: 10.3745/KIPSTB.2003.10.3.237.

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