北京航空航天大学学报 ›› 2021, Vol. 47 ›› Issue (8): 1712-1720.doi: 10.13700/j.bh.1001-5965.2020.0298

• 论文 • 上一篇    

混沌麻雀搜索优化算法

吕鑫1,2, 慕晓冬1, 张钧2, 王震3   

  1. 1. 火箭军工程大学 作战保障学院, 西安 710025;
    2. 北京遥感设备研究所, 北京 100854;
    3. 火箭军工程大学 导弹工程学院, 西安 710025
  • 收稿日期:2020-06-28 发布日期:2021-09-06
  • 通讯作者: 慕晓冬 E-mail:wascom4@sina.com

Chaos sparrow search optimization algorithm

LYU Xin1,2, MU Xiaodong1, ZAHNG Jun2, WANG Zhen3   

  1. 1. Operational Support Academy, Rocket Force University of Engineering, Xi'an 710025, China;
    2. Beijing Institute of Remote Sensing Equipment, Beijing 100854, China;
    3. Missile Engineering College, Rocket Force University of Engineering, Xi'an 710025, China
  • Received:2020-06-28 Published:2021-09-06

摘要: 针对麻雀搜索算法(SSA)在接近全局最优时,种群多样性减少,易陷入局部最优解等问题,提出了一种混沌麻雀搜索优化算法(CSSOA)。首先,通过改进Tent混沌序列初始化种群,提高初始解的质量,增强算法的全局搜索能力。其次,引入高斯变异的方法,加强局部搜索能力,提高搜索精度;同时以搜索停滞的解为基础产生Tent混沌序列,用此混沌序列对部分陷入局部最优的个体进行混沌扰动,促使算法跳出限制继续搜索。最后,对12个基准函数进行仿真实验。结果表明:所提算法能够克服SSA易陷入局部最优的缺点,提高算法的搜索精度、收敛速度和稳定性。同时,将CSSOA应用到简单图像分割问题,验证了CSSOA应用于实际工程问题的可行性。

关键词: 麻雀搜索算法(SSA), Tent混沌, 高斯变异, 局部最优, 基准函数, 图像分割

Abstract: 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.

Key words: Sparrow Search Algorithm (SSA), Tent chaos, Gaussian mutation, local optimum, benchmark function, image segmentation

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