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
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.13 No.3
- 발행연도
- 2024.6
- 수록면
- 263 - 272 (10page)
- DOI
- 10.5573/IEIESPC.2024.13.3.263
이용수
초록· 키워드
오류제보하기
The optimal allocation of rural land use plays a unique role in developing rural industries. Therefore, realizing the effective use of land resources is the key to sustainable development. This research attempts to explore the modeling of rural land use in combination with an ant colony algorithm and multi-objective optimization problem under the background of the current digital economy and to explore land use and space allocation after optimal allocation. The experimental results showed that the multi-objective optimization model of land use proposed in this study could optimize the relevant objective functions so the entire optimization system can reach the optimal solution. The iterations of different objective functions under the three optimization models were compared. The four objective functions of carbon emission, minimum planning cost, adaptability value, and spatial agglomeration all iterated approximately 45 times. They began to converge under the premise of taking the land adaptability value and spatial agglomeration as optimization goals. The convergence rate was faster under this optimization model. In addition, the iteration and running time of the traditional ant colony algorithm, the genetic algorithm, and the improved ant colony algorithm under two different objective functions were compared.
목차
Abstract
1. Introduction
2. Related Works
3. Construction of a Multi-objective Layout Optimization Model for Rural Land based on an Improved Ant Colony Algorithm
4. Analysis of the Detection Results of Rural Land Multi-objective Optimization Model based on the Improved Ant Colony Algorithm
5. Conclusion
Reference
참고문헌 (21)
참고문헌 신청
M. Cramer, T. Koegst, J. Traenckner, “Multi-criterial evaluation of P-removal optimization in rural wastewater treatment plants for a sub-catchment of the Baltic Sea,” Ambio, vol. 47, pp. 93-102, 2018.
X Ning, J Qi, C Wu, et al., “Reducing noise pollution by planning construction site layout via a multi-objective optimization model,” Journal of Cleaner Production, vol. 222, pp. 218-230, 2019.
J. Liu, H. Zhang, K. He, et al., “Multi-objective particle swarm optimization algorithm based on objective space division for the unequal-area facility layout problem,” Expert Systems with Applications, vol. 102, pp. 179-192, 2018.
B. Kanso, A. Kansou, A. Yassine, “Open Capacitated ARC routing problem by Hybridized Ant Colony Algorithm,” RAIRO - Operations Research, vol. 55, pp. 639-652, 2021.
X. Ning, J. Qi, C. Wu, et al., “A tri-objective ant colony optimization based model for planning safe construction site layout,” Automation in Construction, vol. 89, pp. 1-12, 2018.
X Ning, J Qi, C Wu, et al., “Reducing noise pollution by planning construction site layout via a multi-objective optimization model,” Journal of Cleaner Production, vol. 222, pp. 218-230, 2019.
J. Liu, H. Zhang, K. He, et al., “Multi-objective particle swarm optimization algorithm based on objective space division for the unequal-area facility layout problem,” Expert Systems with Applications, vol. 102, pp. 179-192, 2018.
B. Kanso, A. Kansou, A. Yassine, “Open Capacitated ARC routing problem by Hybridized Ant Colony Algorithm,” RAIRO - Operations Research, vol. 55, pp. 639-652, 2021.
X. Ning, J. Qi, C. Wu, et al., “A tri-objective ant colony optimization based model for planning safe construction site layout,” Automation in Construction, vol. 89, pp. 1-12, 2018.
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