摘要
针对评价指标关系丰富性和可解释性低,不能满足管控领域本体建模需求的问题,提出基于混沌粒子群优化算法的电力大规模应急物资管控领域本体模型。采用混沌粒子群优化算法,设计映射实体对集合。通过评价适应度和稀疏度,更新粒子位置并判断是否停止迭代。计算得到最优映射结果,并使用全局DEA评估模型是否符合要求。实验结果表明,综合考虑关系丰富性和可解释性指标,研究模型在整体上表现相对较好。
In response to the problem of low richness and interpretability of evaluation index relationships,which cannot meet the ontology modeling requirements of the control field,a chaotic particle swarm optimization algorithm based ontology model for large-scale emergency material control in the power industry is proposed.Using chaotic particle swarm optimization algorithm,design a set of mapped entity pairs.By evaluating fitness and sparsity,update particle positions and determine whether to stop iteration.Calculate the optimal mapping result and use global DEA to evaluate whether the model meets the requirements.The experimental results indicate that,taking into account the richness and interpretability indicators of relationships,the research model performs relatively well overall.
作者
李云龙
徐行
LI Yunlong;XU Hang(State Grid Wenzhou Electric Power Supply Company Wenzhou City,Zhejiang Province,China,325000)
出处
《长江信息通信》
2024年第1期23-25,共3页
Changjiang Information & Communications
关键词
混沌算法
粒子群算法
电力应急物资
物资管控领域
本体模型
本体映射
chaotic algorithm
particle swarm optimization
power emergency materials
material control field
ontology model
ontology mapping
