The KIPS Transactions:PartB (정보처리학회논문지B)

Korea Information Processing Society (한국정보처리학회)
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A Study about Additional Reinforcement in Local Updating and Global Updating for Efficient Path Search in Ant Colony System

Ant Colony System에서 효율적 경로 탐색을 위한 지역갱신과 전역갱신에서의 추가 강화에 관한 연구

  • 이승관 (경희대학교 대학원 전자계산공학과) ;
  • 정태충 (경희대학교 컴퓨터공학과)
  • Published : 2003.06.01
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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 tile original ACS in terms of solution quality and computation speed to these problem.

Ant Colony System(ACS) 알고리즘은 조합 최적화 문제를 해결하기 위한 메타 휴리스틱 탐색 방법이다. 이것은 greedy search뿐만 아니라 exploitation of positive feedback을 사용한 모집단에 근거한 접근법으로 Traveling Salesman Problem(TSP)를 풀기 위해 제안되었다. 본 논문에서는 전통적 전역갱신과 지역갱신 방법에 개미들이 방문한 각 간선에 대한 방문 횟수를 강화값으로 추가한 새로운 방법의 ACS를 제안한다. 그리고 여러 조건 하에서 TCS 문제를 풀어보고 그 성능에 대해 기존의 ACS 방법과 제안된 ACS 방법을 비교 평가해, 최적해에 더 빨리 수렴함을 실험을 통해 알 수 있었다.

Keywords

References

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