인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2026.5
- 수록면
- 737 - 745 (9page)
- DOI
- 10.9717/kmms.2026.29.5.737
이용수
초록· 키워드
With the increasing demand for high-performance and compact electronic devices, spatial optimization techniques for the optimal placement of components within restricted areas have become crucial. This paper proposes a Strategic Backtracking and Pruning algorithm based on Monte Carlo Tree Search (MCTS) to solve the component placement problem characterized by a vast search space. While conventional MCTS suffers from reduced search efficiency when encountering deadlocks, the proposed algorithm overcomes this limitation through non-sequential backtracking based on an elite candidate pool. Furthermore, an integer grid system and GPU kernel acceleration were implemented to minimize computational overhead. Experimental results demonstrate that the proposed method achieves a 95.3% placement rate within an execution time of 22.9 seconds for 100,000 iterations, proving its superior performance in both search speed and success rate compared to existing methods. Compared to the standard MCTS with large-first strategy, the proposed method reduces execution time by approximately 95% (from 759.3 seconds to 22.9 seconds) while improving the placement success rate from 71.3% to 95.3%, corresponding to an increase of 24.0 percentage points.
상세정보 수정요청해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
목차
- ABSTRACT
- 1. 서론
- 2. 이론
- 3. 제안한 방법
- 4. 실험 및 비교
- 5. 결론
- REFERENCE
참고문헌
참고문헌 신청최근 본 자료
