2015 Seventh International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) (2015)
Dec. 12, 2015 to Dec. 14, 2015
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PAAP.2015.27
The existing algorithm of generating test paper has the problem of low efficiency and slow convergence rate, etc. Improved particle swarm algorithm for test paper auto-generating is proposed on the basic of the particle swarm optimization algorithm and improved genetic algorithm. The algorithm uses greedy algorithm to optimize the initial population. The crossover and mutation operator of genetic algorithm are used to avoid the local convergence of population during the process of iteration. Experimental results show that the improved particle swarm optimization algorithm can applied to auto-generating test paper, which has faster speed and higher success rate.
Parallel architectures, Programming,Genetic Algorithm, Test Paper Auto-generating, Particle Swarm Optimization
Chong Zhang, Jing Zhang, "Research on Test Paper Auto-generating Based on Improved Particle Swarm Optimization", 2015 Seventh International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), vol. 00, no. , pp. 92-96, 2015, doi:10.1109/PAAP.2015.27