Abstract

An advanced Quantum-Particle algorithm is presented to improve the problem of the curse of dimensionality. The Quantum-Particle algorithm which has high-dimensional problem uses multiple swarms to optimize different components of the solution vector by learning method and help the algorithm break away from the curse of dimensionality to find the global optimal solution. Furthermore, the mutation mechanism is introduced into LQPSO to increase the diversity of the swarm to find the global-optimal solution. The experimental results of classic functions show that the improved hybrid method keeps the balance between the global search and the convergence of QPSO.

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