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Abstract

The quantum behavior particle swarm optimization algorithm is analyzed in this paper. The swarm particle search behavior is studied in the algorithm. The local attractive points of the algorithm are analyzed. The different search environments are given for particles in the search process. The algorithm can adaptively learn to optimize the problem environment, and appropriate learning mode is adopt to improve the overall optimization performance of the algorithm. The self-learning quantum particle swarm optimization algorithm is compared with other improved methods by CEC2014 benchmark test function. Finally, the results are analyzed. The simulation results show that the self-learning method can significantly improve the performance of the quantum particle swarm optimization algorithm.

Keywords

Particle swarm optimization (PSO)quantum behavior self-learning local attract point searching model

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Particle swarm optimization research base on quantum self-learning behavior

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