摘要
根据学生生活习惯、性格爱好等特征允许学生自主选择宿舍和舍友,已成为当前高校宿舍资源合理分配的热点与难点问题。通过预调查获取影响学生宿舍满意度的关键因素,综合运用改进的贪心算法、遗传算法和粒子群优化算法,提出一种基于学生特征属性的宿舍分配算法。该算法在确保高效计算的同时,通过优化特征匹配机制,能够有效缩小宿舍成员间的特征差异,提升分配效率。模拟仿真实验表明,与传统算法相比,该算法在整体分配效果上展现出更高的精确度和更好的用户适应性。
In higher education institutions,enabling students to independently select dormitories and roommates based on lifestyle habits,personality traits,and personal preferences has emerged as both a trending discussion point and a complex challenge in optimizing dormitory resource allocation.Through preliminary surveys identifying key determinants of dormitory satisfaction,this study develops a characteristic-based allocation algorithm that integrates enhanced versions of greedy algorithms,genetic algorithms,and particle swarm optimization.The proposed framework maintains computational efficiency while refining compatibility assessment mechanisms,successfully reducing demographic variance in student traits and consequently improving allocation effectiveness.Computational simulations demonstrate superior performance compared to conventional methods,achieving 23%higher matching precision and 41%better adaptability to user requirements in holistic allocation scenarios.
作者
张晓阳
周玉婷
魏昕恺
秦怡
高溢妍
宋涛
ZHANG Xiaoyang;ZHOU Yuting;WEI Xinkai;QIN Yi;GAO Yiyan;SONG Tao(School of Science,Huzhou University,Huzhou 313000,China;Huzhou Key Laboratory of Data Modeling and Analysis,Huzhou 313000,China)
出处
《湖州师范学院学报》
2025年第4期8-18,共11页
Journal of Huzhou University
基金
浙江省教育厅科研项目(Y202248528)
湖州市科技计划项目(2023YZ28)
浙江省大学生创新创业训练计划项目(S202310347130,S202410347039)。
关键词
高校宿舍分配
个性化需求
多约束优化
贪心算法
遗传算法
粒子群优化
university dormitory allocation
personalized needs
multi-constraint optimization
Greedy Algorithm
Genetic Algorithm
Particle Swarm Optimization
