Application of adaptive quantum particle swarm optimization algorithm for optimal dispatching of cascaded hydropower stations

  • Authors:

  • Guoyong Zhang;Yonggang Wu;Feng Zhang;Xinglong Liu

  • Affiliations:

  • College of Hydroelectricity and Digitalization Engineering, Huazhong University of Science and Technology, Wuhan, China;College of Hydroelectricity and Digitalization Engineering, Huazhong University of Science and Technology, Wuhan, China;Water Resource Management Office of Xionghe Reservoir, Xiangyang, China;College of Hydroelectricity and Digitalization Engineering, Huazhong University of Science and Technology, Wuhan, China

  • Venue:
  • ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
  • Year:
  • 2012

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Abstract

To improve the critical deficiency of traditional QPSO in solving the optimal dispatching of cascade hydropower stations with nonlinear constrained, an adaptive quantum particle swarm optimization algorithm (AQPSO) is proposed. Based on analysis the relative change rate of objective fitness function, this algorithm introduces an adaptive weight parameter to update the probability amplitude of particles, which improves the global searching capability and convergence precision. For the coding of particles, the probability amplitude expression of quantum bit is applied to describe the position of particles, by which one particle can be expressed as the superposition of multi-states. The study assesses the performance of the AQPSO on a series of benchmark problems and an example of the optimal dispatching of cascaded hydropower stations, results show that the superiority of AQPSO as compared to QPSO and PSO.