Improved ant agents system by the dynamic parameter decision

Seung Gwan Lee; Tae Ung Jung; Tae Choong Chung

Improved ant agents system by the dynamic parameter decision

Seung Gwan Lee
, Tae Ung Jung
, Tae Choong Chung

Research output: Contribution to conference › Paper › peer-review

8 Citations (Scopus)

Abstract

Ant Colony System(ACS) Algorithm is a new metaheuristic for hard combinational 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 a new version of the ACS based on Dynamic weighted updating method and Dynamic ant number decision method using curve fitting algorithm. 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 to these problems.

Original language	English
Pages	666-669
Number of pages	4
Publication status	Published - 2001
Event	10th IEEE International Conference on Fuzzy Systems - Melbourne, Australia Duration: 2 Dec 2001 → 5 Dec 2001

Conference

Conference	10th IEEE International Conference on Fuzzy Systems
Country/Territory	Australia
City	Melbourne
Period	2/12/01 → 5/12/01

Cite this

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title = "Improved ant agents system by the dynamic parameter decision",

abstract = "Ant Colony System(ACS) Algorithm is a new metaheuristic for hard combinational 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 a new version of the ACS based on Dynamic weighted updating method and Dynamic ant number decision method using curve fitting algorithm. 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 to these problems.",

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T1 - Improved ant agents system by the dynamic parameter decision

AU - Lee, Seung Gwan

AU - Jung, Tae Ung

AU - Chung, Tae Choong

PY - 2001

Y1 - 2001

N2 - Ant Colony System(ACS) Algorithm is a new metaheuristic for hard combinational 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 a new version of the ACS based on Dynamic weighted updating method and Dynamic ant number decision method using curve fitting algorithm. 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 to these problems.

AB - Ant Colony System(ACS) Algorithm is a new metaheuristic for hard combinational 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 a new version of the ACS based on Dynamic weighted updating method and Dynamic ant number decision method using curve fitting algorithm. 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 to these problems.

UR - https://www.scopus.com/pages/publications/0036343389

M3 - Paper

AN - SCOPUS:0036343389

SP - 666

EP - 669

T2 - 10th IEEE International Conference on Fuzzy Systems

Y2 - 2 December 2001 through 5 December 2001

ER -

Improved ant agents system by the dynamic parameter decision

Abstract

Conference

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