The local attractor point in the quantum-behaved particle swarm optimization algorithm plays an important role
in determining the convergence process of population. Therefore, a quantum-behaved particle swarm optimization algorithm
based on two elitist learning strategys(QPSO-EL) is presented. In this method, the dynamic-approximation search strategy
is exerted on the elitist particles to avoid them running into local optima and provides a good guidance for the population.
While the algorithm is found to be in a dead state according to the premature judgment mechanism, the mutative-scale chaotic
perturbation is used to exhibit a wide range exploration and keep the balance of exploration and exploitation. The experiment
results on classic functions demonstrate the global convergence ability and the search accuracy of the proposed method.