The Application of Quantum-Behaved Particle Swarm Algorithm in Intelligent Test Paper Generation

Abstract:

Aiming at solving the low efficiency and low quality of the existing test paper generation algorithm, this paper proposes an improved particle swarm algorithm, a new algorithm for intelligent test paper generation. Firstly, the paper conducts mathematically modeling based on item response theory. Secondly, in the new algorithm, the inertia weight is expressed as functions of particle evolution velocity and particle aggregation by defining particle evolution velocity and particle aggregation so that the inertia weight has adaptability. At the same time, slowly varying function is introduced to the traditional location updating formula so that the local optimal solution can be effectively overcome. Finally, simulation results show that compared with the quantum-behaved particle swarm algorithm, the proposed algorithm has better performance in success rate and composing efficiency.

• Periodical: Advanced Materials Research (Volumes 694-697)
• Pages: 2378-2382
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.694-697.2378
• Online since: May 2013
• Authors: Xin Ran Li
• Keywords: Adaptability, Inertia Weight, Intelligent Test Paper Generation, Item Response Theory (IRT), Quantum Behaved Particle Swarm Optimization Algorithm (QPSO), Slowly Varying Function

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