Development of Fuzzy Logic Ant Colony Optimization Algorithm for Multivariate Traveling Salesman Problem

서지정보

ㆍ발행기관 : 한국산업경영시스템학회 ㆍ수록지정보 : 산업경영시스템학회지 / 46권 / 1호
ㆍ저자명 : Byeong-Gil Lee, Kyubeom Jeon, Jonghwan Lee

목차

1. 연구배경 및 방법
2. 순환 판매원 문제
3. 메타 휴리스틱
3.1 개미집단 최적화 알고리즘
3.2 퍼지 로직
4. 알고리즘 개발
4.1 기존의 알고리즘
4.2 개선 알고리즘
5. 실 험
5.1 실험 설정
5.2 실험 및 결과
6. 결론 및 향후 과제
6.1 결론
6.2 향후 과제
Acknowledgement
References

영어 초록

An Ant Colony Optimization Algorithm(ACO) is one of the frequently used algorithms to solve the Traveling Salesman Problem(TSP). Since the ACO searches for the optimal value by updating the pheromone, it is difficult to consider the distance between the nodes and other variables other than the amount of the pheromone. In this study, fuzzy logic is added to ACO, which can help in making decision with multiple variables. The improved algorithm improves computation complexity and increases computation time when other variables besides distance and pheromone are added. Therefore, using the algorithm improved by the fuzzy logic, it is possible to solve TSP with many variables accurately and quickly. Existing ACO have been applied only to pheromone as a criterion for decision making, and other variables are excluded. However, when applying the fuzzy logic, it is possible to apply the algorithm to various situations because it is easy to judge which way is safe and fast by not only searching for the road but also adding other variables such as accident risk and road congestion. Adding a variable to an existing algorithm, it takes a long time to calculate each corresponding variable. However, when the improved algorithm is used, the result of calculating the fuzzy logic reduces the computation time to obtain the optimum value.

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