Meta-heuristic algorithms have been developed to efficiently solve difficult problems and obtain a global optimal solution. A common feature mimics phenomenon occurring in nature and reliably improves the solution through repetition. And at the same time, the probability is used to deviate from the regional optimal solution and approach the global optimal solution. This study compares the algorithm created based on the above common points with existed SA and HS to show advantages in time and accuracy of results. Existing algorithms have problems of low accuracy, high memory, long runtime, and ignorance. In a two-variable polynomial, the existing algorithms show that the memory increases and the accuracy decrease. In order to improve the accuracy, the new algorithm increases the number of initial inputs and increases the efficiency of the search by introducing a direction using vectors. And, in order to solve the optimization problem, the results of the last experiment were learned to show the learning effect in the next experiment. The new algorithm found a solution in a short time under the experimental conditions of long iteration counts using a two-variable polynomial and showed high accuracy. And, it shows that the learning effect is effective in repeated experiments.

1. 서 론
2. 이론적 배경
    2.1 메타휴리스틱
    2.2 선행 연구
    2.3 기존 알고리즘의 문제점
3. 개발 알고리즘
    3.1 이론
    3.2 알고리즘
4. 연구방법
    4.1 테스트 함수
    4.2 연구 프로세스
5. 연구결과
    5.1 테스트 함수
    5.2 NP-hard 함수
6. 결론 및 향후과제
References

• Sung-Ho Chang(금오공과대학교 산업공학부) | 장성호
• Moonsoo Kwon(금오공과대학교 산업공학부) | 권문수
• Geuntae Kim(금오공과대학교 산업공학부) | 김근태
• Jonghwan Lee(금오공과대학교 산업공학부) | 이종환 Corresponding Author