Quantum-behaved particle swarm optimization algorithm based on elitist learning
Zhang Guo-yon · Kongzhi yu juece · 2013
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.