Description: A quantum behaved particle swarm optimization (QPSO) method for solving multimodal function optimization problems. For this reason, the introduction of a species in QPSO form a strategy, the method is based on the similarity of the groups of particles divided into sub-groups in parallel. Each sub-group is established around the seeds of a group. The QPSO algorithm optimal search for each sub-group. In order to ensure that each peak has the same opportunity to find, this method has good local optimization features. Will compare the results of multimodal optimization problems based on QPSO algorithm and particle swarm optimization (PSO) species formed. Simulation results prove that several important test function, based species formed QPSO algorithm can be as much as possible to find the peak point, the peak convergence outperforms PS
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一种求解多峰函数优化问题的量子行为粒子群算法.pdf
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