The KIPS Transactions:PartB (정보처리학회논문지B)

Korea Information Processing Society (한국정보처리학회)
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Multi Colony Ant Model using Positive.Negative Interaction between Colonies

집단간 긍정적.부정적 상호작용을 이용한 다중 집단 개미 모델

  • 이승관 (경희대학교 대학원 전자계산공학과) ;
  • 정태충 (경희대학교 컴퓨터공학과)
  • Published : 2003.12.01
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Abstract

Ant Colony Optimization (ACO) is new meta heuristics method to solve hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was firstly proposed for tackling the well known Traveling Salesman Problem (TSP) . In this paper, we introduce Multi Colony Ant Model that achieve positive interaction and negative interaction through Intensification and Diversification to improve original ACS performance. This algorithm is a method to solve problem through interaction between ACS groups that consist of some agent colonies to solve TSP problem. In this paper, we apply this proposed method to TSP problem and evaluates previous method and comparison for the performance and we wish to certify that qualitative level of problem solution is excellent.

개미 집단 최적화는 최근에 제안된 조합 최적화 문제를 해결하기 위한 메타 휴리스틱 탐색 방법으로, 그리디 탐색뿐만 아니라 긍정적 반응의 탐색을 사용한 모집단에 근거한 접근법으로 순회 판매원 문제를 풀기 위해 처음으로 제안되었다. 본 논문에서는 기존의 개미 집단 시스템의 성능을 향상시키기 위해 강화와 다양화를 통한 집단간 긍정적 상호작용과 부정적 상호작용을 수행하는 다중 집단 개미 모델을 제안한다. 이 알고리즘은 TSP 문제를 해결하기 위해 몇 개의 에이전트 집단으로 이루어진 ACS 집단간의 상호작용을 통해 문제를 해결하는 방법이다. 본 논문에서는 이 제안된 방법을 TSP 문제에 적용해 보고 그 성능에 대해 기존의 ACS 방법과 비교 평가해, 문제 해결의 질적 수준이 우수하다는 것을 실험을 통해 알아보고자 한다.

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References

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