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.

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

• Byeong-Gil Lee(Graduate School of Consulting, Kumoh National Institute of Technology) | 이병길 (금오공과대학교 컨설팅대학원)
• Kyubeom Jeon(School of Industrial Engineering, Kumoh National Institute of Technology) | 전규범 (금오공과대학교 산업공학부)
• Jonghwan Lee(School of Industrial Engineering, Kumoh National Institute of Technology) | 이종환 (금오공과대학교 산업공학부) Corresponding Author