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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2025.4
- 수록면
- 157 - 164 (8page)
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초록· 키워드
오류제보하기
Metamaterials have attracted considerable attention, owing to their unique properties and application prospects across various fields. However, the efficient design of metamaterials remains challenging because of the complex, high-dimensional optimization problems involved. Addressing these complexities via conventional design methodologies is difficult, and leads to extended development times and suboptimal solutions. To overcome these limitations, we propose a hybrid strategy that integrates genetic algorithms with reinforcement learning to achieve autonomous design of metamaterials. This integrated approach exploits the strengths of both techniques and significantly improves the efficiency and effectiveness of the design process. Our method converges to optimal configurations within five generations—a 79% reduction compared to the 24 generations required by conventional genetic-algorithm methods. Moreover, the performance scores for the hybrid and conventional methods are 0.93 and 0.85, respectively, within the same limited iteration span. Thus the hybrid method achieves a performance improvement of approximately 9.4% over the conventional method within just five generations. These results suggest that the proposed approach facilitates the discovery of innovative metamaterial structures and represents a significant advance in the automation and optimization of metamaterial design.
목차
Ⅰ. INTRODUCTION
Ⅱ. METHODS
Ⅲ. RESULTS & DISCUSSION
Ⅳ. CONCLUSION
REFERENCES
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