计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 217-222.doi: 10.11896/jsjkx.210700032

• 智能计算 • 上一篇    下一篇

基于多种改进策略的改进麻雀搜索算法

李丹丹, 吴宇翔, 朱聪聪, 李仲康   

  1. 郑州轻工业大学建筑环境工程学院 郑州 450001
  • 出版日期:2022-06-10 发布日期:2022-06-08
  • 通讯作者: 李丹丹(lidandan@zzuli.edu.cn)
  • 基金资助:
    国家自然科学基金青年基金(51607157);郑州轻工业大学博士科研基金(2015-BSJJ012)

Improved Sparrow Search Algorithm Based on A Variety of Improved Strategies

LI Dan-dan, WU Yu-xiang, ZHU Cong-cong, LI Zhong-kang   

  1. School of Building Environment Engineering,Zhengzhou University of Light Industry,Zhengzhou 450001,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:LI Dan-dan,born in 1985,Ph.D,lec-turer,postgraduate supervisor.Her mainresearch interests include numerical calculation of electromagnetic field and simulation of magnetic properties of magnetic materials.
  • Supported by:
    National Natural Science Foundation of China(51607157) and Doctor Foundation of Zhengzhou University of Light Industry(2015-BSJJ012).

摘要: 针对麻雀搜索算法收敛速度慢、易陷入局部最优值、寻优精度低等缺陷进行研究,提出一种基于多种改进策略的改进麻雀搜索算法(IM-SSA)。首先,利用Tent混沌序列丰富麻雀搜索算法的初始种群,扩大搜索区域范围,并在发现者中引入自适应交叉变异算子,丰富发现者种群的多样性,平衡全局与局部的搜索能力;其次,按照每次迭代后种群个体的特点选用t-分布扰动或差分变异进行扰动,避免算法后期种群单一,提升算法跳出局部最优值的能力;最后,分别使用IM-SSA算法、灰狼算法、粒子群算法、鲸鱼算法和经典麻雀搜索算法对8个测试函数进行仿真。通过仿真结果对比分析可得,IM-SSA比其他4种算法收敛速度更快,跳出局部最优值能力更强,寻优精度更高。与当前现有改进麻雀搜索算法仿真结果的对比也表明,IM-SSA算法的改进策略更优。

关键词: t-分布扰动, 差分变异, 交叉变异算子, 麻雀搜索算法

Abstract: To solve the shortcomings of sparrow search algorithm,such as slow convergence speed,easy to fall into local optimal value and low optimization precision,an improved sparrow search algorithm(IM-SSA) based on Various improvement strategies is proposed.Firstly,the initial population of the sparrow search algorithm is enriched by Tent chaotic sequence,which expand the search area.Then,the adaptive crossover and mutation operator is introduced into the finders to enrich the diversity of the producers population and balance the global and local search ability of the algorithm.Secondly,the t-distribution perturbation or differential mutation is used to perturbate the population after each iteration according to the individual characteristics,which can avoid the population singularity in the later stage of the algorithm and enhance the ability of jump out of the local optimal value of the algorithm.Finally,the IM-SSA algorithm proposed in this paper,gray Wolf algorithm,particle swarm optimization algorithm,whale algorithm and classical sparrow search algorithm are used to simulate the eight test functions,respectively.Through the comparative analysis of simulation results,it can be concluded that the IM-SSA algorithm has faster convergence speed,stronger ability to get out of local optimal value and higher optimization precision than the other four algorithms.Compared the simulation results of the IM-SSA algorithm with the ones of the existing improved sparrow search algorithm,it is found that the strategy of IM-SSA algorithm proposed in this paper is better.

Key words: Crossover mutation operator, Differential evolution algorithm, Sparrow search algorithm, t-distribution perturbation

中图分类号: 

  • TP301.6
[1] SONG H M,SULAIMAN M H,MOHAMED M R.An Application of Grey Wolf Optimizer for Solving Combined Economic Emission Dispatch Problems[J].International Review on Modelling & Simulations,2014,7(5):838-844.
[2] HACKL A,MAGELE C,RENHART W.Extended firefly algo-rithm for multimodal optimization[C]//International Sympo-sium on Electrical Apparatus & Technologies.IEEE,2016.
[3] JIANG X,LI S.BAS:Beetle Antennae Search Algorithm forOptimization Problems[J].International Journal of Robotics and Control,2017,1(1):1-4.
[4] LJARAH I,FARIS H,MIRJALILI S.Optimizing connectionweights in neural networks using the whale optimization algorithm[J].Soft Computing,2018,22(1):1-15.
[5] XUE J K,SHEN B.A novel swarm intelligence optimization approach:sparrow search algorithm[J].Systems Science and Control Engineering,2020,8(1):22-24.
[6] LV X,MU X D,ZHANG J.Chaos sparrow search optimization algorithm [J/OL].(2020-08-31) [2021-05-22].https://doi.org/10.13700/j.bh.1001-5965.2020.0298.
[7] TANG A D,HAN T,XU D W.Path planning method of unmanned aerial vehicle based on Chaos sparrow search optimization algorithm[J].Journal of Computer Applications,2021,41(7):2128-2136.
[8] MAO Q H,ZHANG Q.Improved Sparrow Algorithm Combining Cauchy Mutation and Opposition-Based[J].Journal of Frontiers of Computer Science and Technology,2021,15(6):1155-1164.
[9] MAO Q H,ZHANG Q,MAO C C.Mixing Sine and Cosine Algorithm with Lévy Flying Chaotic Sparrow Algo-rithm [J/OL].(2021-04-06) [2021-05-22].https://doi.org/10.13451/j.sxu.ns.2020135.l
[10] NING J Q,HE Q.Flower Pollination Algorithm Based on t-distribution Perturbation Strategy and Mutation Strategy[J].Journal of Chinese Computer Systems,2021,42(1):64-70.
[11] IBRAHIM R A,ABD ELAZIZ M,LU S.Chaotic Opposition-Based Grey-Wolf Optimization Algorithm based on Differential Evolution and Disruption Operator for Global Optimization[J].Expert Systems with Applications,2018,108(OCT.):1-27.
[12] DAN L,QIANG H,LI J.Chaotic optimization algorithm based on Tent map[J].Control and Decision,2005,20(2):179-182.
[13] STORN R,PRICE K.Differential Evolution-A Simple and Efficient Heuristic for global Optimization over Continuous Spaces[J].Journal of Global Optimization,1997,11(4):341-359.
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