计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 84-88.doi: 10.11896/JsJkx.190900148

• 人工智能 • 上一篇    下一篇

一种基于量子耗散粒子群的评估模型构建方法

张素梅1, 张波涛2   

  1. 1 浙江经贸职业技术学院公共教学部 杭州 310018;
    2 杭州电子科技大学自动化学院 杭州 310018
  • 发布日期:2020-07-07
  • 作者简介:waveact@163.com
  • 基金资助:
    国家自然科学基金(61611530709);浙江省高等教育课堂教学改革项目(KG20160782)

Evaluation Model Construction Method Based on Quantum Dissipative Particle Swarm Optimization

ZHANG Su-mei1 and ZHANG Bo-tao2   

  1. 1 Division of Public Teaching,ZheJiang Institute of Economics and Trade,Hangzhou 310018,China
    2 School of Automation,Hangzhou Dianzi University,Hangzhou 310018,China
  • Published:2020-07-07
  • About author:ZHANG Su-mei, born in 1982, master, lecturer.Her main research interests include language data mining, corpus analysis and so on.
    ZHANG Bo-tao, born in 1982, Ph.D, associate professor.His main research interests include theoretical method of computational intelligence and machine learning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61611530709) and ZheJiang Higher Education Classroom Tea-ching Reform ProJect(kg20160782).

摘要: 提出了一种量子耗散粒子群算法,每个粒子信息位采用双本征态叠加表达,量子信息载体用于粒子群的种群差异化;并设计了惯性权重的自适应调整策略。针对4个经典测试函数进行了测试,结果表明所提算法相比标准粒子群、指数耗散粒子群和惯性递减耗散粒子群等算法具有明显的优势。将该算法用于一种教学评估模型的构建中,用于克服主观意识对客观评价的干扰,结果表明所建模型可以与现实数据高度拟合,取得了比人工经验模型更高的评估精度。

关键词: 关联规则, 耗散粒子群, 量子表达, 数据挖掘

Abstract: In this paper,a quantum dissipative particle swarm optimization (QD-PSO) algorithm is proposed.Each particle information bit is represented by double eigenstate superposition.Quantum information carrier is applied to population differentiation of particle swarm,and the adaptive adfustment stratgey of inertia weight is designed.Four classical benchmark functions are tes-ted.The results show that the proposed algorithm has obvious advantages over standard particle swarm optimization (PSO),expo-nential dissipative particle swarm optimization (APSO) and inertia decline dissipative particle swarm optimization (W-G-PSO).The algorithm is applied to the construction of a teaching evaluation model to overcome the interference of subJective consciousness on obJective evaluation.The results show that the model can be highly matched with empirical data.It has higher eva-luation accuracy than the artificial experience model.

Key words: Association rule, Data mining, Dissipative particle swarm, Quantum expression

中图分类号: 

  • TP391
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