It is one of the known methods to obtain the optimal solution using the Ant Colony Optimization Algorithm for the Traveling Salesman Problem (TSP), which is a combination optimization problem. In this paper, we solve the TSP problem by proposing an improved new ant colony optimization algorithm that combines genetic algorithm mutations in existing ant colony optimization algorithms to solve TSP problems in many cities. The new ant colony optimization algorithm provides the opportunity to move easily fall on the issue of developing local optimum values of the existing ant colony optimization algorithm to global optimum value through a new path through mutation. The new path will update the pheromone through an ant colony optimization algorithm. The renewed new pheromone serves to derive the global optimal value from what could have fallen to the local optimal value. Experimental results show that the existing algorithms and the new algorithms are superior to those of existing algorithms in the search for optimum values of newly improved algorithms.

1. 연구배경 및 방법
2. 순환 판매원 문제
3. 메타 휴리스틱
    3.1 메타 휴리스틱
    3.2 개미집단 최적화
4. 알고리즘 개발
    4.1 기존의 알고리즘
    4.2 새로운 알고리즘
    4.3 알고리즘에서 기호 및 변수 정의
5. 연구방법
    5.1 기존의 알고리즘
    5.2 돌연변이의 적용
    5.3 연구 프로세스
6. 실험 및 결과분석
7. 결론 및 향후 과제
    7.1 결론
    7.2 향후 과제
References

• Juyoung Jang(Department of Industrial Engineering, Kumoh National Institute of Technology) | 장주영
• Minje Kim(Department of Industrial Engineering, Kumoh National Institute of Technology) | 김민제
• Jonghwan Lee(Department of Industrial Engineering, Kumoh National Institute of Technology) | 이종환 Corresponding Author