Improvement of Ant Colony Optimization Algorithm to Solve Traveling Salesman Problem

• 발행기관: 한국산업경영시스템학회
• 저자명: 장주영(Juyoung Jang) ․ 김민제(Minje Kim) 이종환(Jonghwan Lee)
• 간행물 정보: 『산업경영시스템학회지』제42권 제3호, 1~7쪽, 전체 7쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2019.09.30

영문 초록

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.

목차

1. 연구배경 및 방법

2. 순환 판매원 문제

3. 메타 휴리스틱

4. 알고리즘 개발

5. 연구방법

6. 실험 및 결과분석

7. 결론 및 향후 과제

키워드

Ant Colony Optimization Algorithm Traveling Salesman Problem Optimization