Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 84-88.doi: 10.11896/JsJkx.190900148

• Artificial Intelligence • Previous Articles     Next Articles

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).

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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

CLC Number: 

  • TP391
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