LIU Jingsen, LIU Li, LI Yu. A Differential Evolution Flower Pollination Algorithm with Dynamic Switch Probability[J]. Chinese Journal of Electronics, 2019, 28(4): 737-747. doi: 10.1049/cje.2019.04.008
Citation: LIU Jingsen, LIU Li, LI Yu. A Differential Evolution Flower Pollination Algorithm with Dynamic Switch Probability[J]. Chinese Journal of Electronics, 2019, 28(4): 737-747. doi: 10.1049/cje.2019.04.008

A Differential Evolution Flower Pollination Algorithm with Dynamic Switch Probability

doi: 10.1049/cje.2019.04.008
Funds:  This work is supported by the Science & Technology Program of Henan Province, China (No.182102310886, No.162102110109).
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  • Corresponding author: LI Yu (corresponding author) was born in 1969. She received the Ph.D. degree in management science and engineering from University of Shanghai for Science and Technology, China. She is a professor of Institute of Management Science and Engineering, Henan University. Her research interests include intelligence algorithms and electronic commerce, etc. (Email:lyhenu@163.com)
  • Received Date: 2018-06-27
  • Rev Recd Date: 2019-01-27
  • Publish Date: 2019-07-10
  • For the shortcomings of the basic flower pollination algorithm, this paper proposes a differential evolution flower pollination algorithm with dynamic switch probability based on the Weibull distribution. This new algorithm improved the convergence rate and precision. The switch probability is improved by Weibull distribution function combined with the number of iterations. It can balance the relationship between the global pollination and the local pollination to improve the overall optimization performance of the algorithm. Random mutation operator is merged into the global pollination process to increase diversity of the population, enhance the ability of the algorithm's global search and avoid premature convergence. In the process of local pollination, directed mutation and crossover operation of the differential evolution are incorporated, it makes the individual flower position update with the memory function, which can choose the direction of variation reasonably. The use of cross-operation can avoid new solutions crossing the boundary. Convergence rate is improved and the algorithm can approach the global optimal solution continuously. Theoretical analysis proved the convergence and time complexity of the improved algorithm. The simulation results based on the function optimization problem show that the improved algorithm has better performance of optimization, faster convergence speed and higher convergence accuracy.
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