Volume 47 Issue 8
Aug.  2021
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LYU Xin, MU Xiaodong, ZAHNG Jun, et al. Chaos sparrow search optimization algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(8): 1712-1720. doi: 10.13700/j.bh.1001-5965.2020.0298(in Chinese)
Citation: LYU Xin, MU Xiaodong, ZAHNG Jun, et al. Chaos sparrow search optimization algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(8): 1712-1720. doi: 10.13700/j.bh.1001-5965.2020.0298(in Chinese)

Chaos sparrow search optimization algorithm

doi: 10.13700/j.bh.1001-5965.2020.0298
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  • Corresponding author: MU Xiaodong. E-mail: wascom4@sina.com
  • Received Date: 28 Jun 2020
  • Accepted Date: 21 Aug 2020
  • Publish Date: 20 Aug 2021
  • Aimed at the problem that when the Sparrow Search Algorithm (SSA) is close to the global optimum, the population diversity decreases, and it is easy to fall into the local optimal solution. A Chaotic Sparrow Search Optimization Algorithm (CSSOA) is proposed. Firstly, the population was initialized by improving the Tent chaotic sequence, the quality of the initial solution was improved, and the global search ability of the algorithm was strengthened. Secondly, the method of Gaussian mutation was introduced to strengthen the local search ability and improve the search accuracy. At the same time, a Tent chaotic sequence was generated based on the search stagnation solution, and this chaotic sequence was used to chaotically disturb some individuals who were partially trapped in the local optimum, prompting the algorithm to jump out of the limit and continue the search. Finally, through simulations of 12 benchmark functions, the results show that the proposed algorithm can overcome the shortcomings of SSA being easily trapped in local optimum, and improve the search accuracy, convergence speed and stability of the algorithm. Meanwhile, CSSOA is applied to the simple image segmentation problem, which verifies the feasibility of applying CSSOA to practical engineering problems.

     

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