Abstract：PSO algorithm is poor in search accuracy and prone to fall into the local extremum when solving complex multimodal function optimization problem. So, we propose an improved quantum behaved particle swarm optimization algorithm. This paper studies the fundamentals and basic procedure of that algorithm, An orthogonal test for parameter selection is designed to select a set of reasonable control parameters. We use a suite of 28 test functions from CEC’13 as test set. NM-QPSO is compared with both of traditional PSO and QPSO by using the Wilcoxon Signed Ranks Test respectively. Tests show that the NM-QPSO algorithm has better performance than the traditional PSO and QPSO algorithms in statistical sense, and it has obvious advantages in the high-dimensional function optimization.
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