吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (8): 1842-1849.doi: 10.13229/j.cnki.jdxbgxb20210170

• 计算机科学与技术 • 上一篇    

基于亲缘关系选择的粒子群优化算法

管仁初1(),贺宝润1,梁艳春1,2,时小虎1()   

  1. 1.吉林大学 计算机科学与技术学院,长春 130012
    2.吉林大学珠海学院 计算机科学与技术系,广东 珠海 519041
  • 收稿日期:2021-03-05 出版日期:2022-08-01 发布日期:2022-08-12
  • 通讯作者: 时小虎 E-mail:guanrenchu@jlu.edu.cn;shixh@jlu.edu.cn
  • 作者简介:管仁初(1981-),男,教授,博士生导师. 研究方向:机器学习,智能优化. E-mail: guanrenchu@jlu.edu.cn
  • 基金资助:
    国家自然科学基金面上项目(61972174);吉林省科技厅技术公关项目(20190302107GX)

Particle swarm optimization algorithm based on kinship selection

Ren-chu GUAN1(),Bao-run HE1,Yan-chun LIANG1,2,Xiao-hu SHI1()   

  1. 1.College of Computer Science and Technology,Jilin University,Changchun 130012,China
    2.Computer Science and Technology Department,Zhuhai College of Jilin University,Zhuhai 519041,China
  • Received:2021-03-05 Online:2022-08-01 Published:2022-08-12
  • Contact: Xiao-hu SHI E-mail:guanrenchu@jlu.edu.cn;shixh@jlu.edu.cn

摘要:

针对传统粒子群优化算法在解决最优化问题中存在早熟收敛和无法寻找到全局最优解问题,本文提出了一种基于亲缘关系选择的粒子群优化算法,提高了算法的全局搜索能力。此外,引入了多个种群的交流机制与各子种群之间的淘汰机制,有效避免了个体在寻优过程中陷入局部最优点。实验部分首先在单目标优化函数集上与传统的粒子群优化算法以及一些有竞争力的算法结果进行对比分析,发现算法在相同种群规模与评价次数的条件下,在准确性与搜索能力上有着明显的优势;然后,将新算法应用到桁架穹顶优化问题上,并与传统的粒子群优化算法进行了比较,求得了这一实际问题的一个可行解。

关键词: 最优化问题, 群智能算法, 粒子群算法, 亲缘关系选择, 穹顶优化

Abstract:

Aiming at the problem that the traditional particle swarm optimization(PSO) algorithm has premature convergence and unable to find the global optimal solution in solving the optimization problem, a particle swarm optimization algorithm based on kinship selection is proposed, which improves the global search ability of the algorithm. In addition, the communication mechanism of multiple populations and the elimination mechanism between each subpopulation are introduced, which effectively avoids individuals falling into the local optimum in the process of optimization. In the experiment part, the single objective optimization function set is compared with the traditional particle swarm optimization algorithm and the results of some competitive algorithms. obvious advantages; then, the new algorithm is applied to the optimization problem of truss dome, and compared with the traditional particle swarm optimization algorithm, a feasible solution to this practical problem is obtained.

Key words: optimization problem, swarm intelligence, particle swarm optimization, kinship selection, dome optimization

中图分类号: 

  • TP39

图1

Kin-PSO 算法流程图"

表1

100位挑战测试函数"

序号待求解函数FD可行解范围
1切比雪夫多项式拟合问题19[-8192, 8192]
2逆希尔伯特矩阵问题116[-16384,16384]
3伦纳德-琼斯最小能量簇问题118[-4,4]
4拉斯特金函数110[-100,100]
5格里旺克函数110[-100,100]
6魏尔斯特拉斯函数110[-100,100]
7修正施韦费尔函数110[-100,100]
8扩展的 Schaffer's F6 函数110[-100,100]
9Happy Cat 函数110[-100,100]
10阿克利函数110[-100,100]

表2

100位挑战实验得分"

函数算法
PSOclPSONUMPSOABCDFSABCiL-SHADECMA-ESUNIVARKin-PSO
总分1.1720E+011.6410E+011.7540E+019.6400E+004.0560E+014.2040E+013.9360E+014.0200E+015.0260E+01
10.0000E+000.0000E+000.0000E+000.0000E+000.0000E+001.0000E+011.0000E+010.0000E+001.0000E+01
20.0000E+000.0000E+000.0000E+000.0000E+000.0000E+001.0000E+011.0000E+010.0000E+001.0000E+01
31.9200E+003.4000E+001.0000E+001.3600E+001.2000E+001.0000E+011.3600E+000.0000E+009.1000E+00
40.0000E+000.0000E+000.0000E+000.0000E+009.5200E+000.0000E+000.0000E+001.0000E+010.0000E+00
52.7200E+002.8100E+004.5800E+005.1600E+007.0000E+001.0000E+016.7200E+001.0000E+013.3000E+00
63.9200E+004.7600E+005.7600E+001.0000E+001.0000E+010.0000E+001.0000E+011.0000E+015.7000E+00
70.0000E+004.0000E-020.0000E+000.0000E+004.0000E-020.0000E+000.0000E+002.0000E+000.0000E+00
80.0000E+000.0000E+000.0000E+008.0000E-028.0000E-010.0000E+000.0000E+001.0000E+006.0000E-01
92.0000E+002.0000E+002.0000E+002.0000E+002.0000E+002.0400E+001.2800E+002.0000E+002.1000E+00
101.1600E+003.4000E+004.2000E+004.0000E-021.0000E+010.0000E+000.0000E+005.2000E+001.0000E+01

表3

实验结果的细节"

函数最佳最差方差得分
总分45
11.00E+001.05E+001.98E-3410
21.00E+001.00E+002.27E-0810
31.00E+001.41E+002.68E-029.1
42.99E+001.19E+011.13E+010
51.00E+001.02E+003.03E-043.3
61.00E+001.00E+003.78E-095.7
72.46E+025.94E+026.05E+040
81.29E+002.99E+001.45E-000.06
91.01E+001.05E+009.52E-042.1
101.00E+001.00E+001.80E-1910

图2

穹顶初始条件下的俯视图"

图3

穹顶初始条件下的侧视图"

图4

四种算法在工程实例上的收敛曲线"

表4

工程实例结果"

算法适应度最大形变/mm总体积/m3
PSO768.904.3303.99
clPSO756.914.0204.18
NUMPSO790.164.8703.93
Kin-PSO847.685.6042.65

表5

工程实例结果参数表"

连杆数目体积/m3连杆数目体积/m3
11920.243213960.0815
21920.235514960.0898
31920.227315960.0870
41920.178416490.0450
51920.168617480.0332
61920.198318480.0332
71920.187419480.0319
81920.179220480.0326
91920.112921480.0150
10960.108122480.0151
11960.103023480.0150
12960.0820242560.1470

图5

穹顶受力条件下的俯视图"

图6

穹顶受力条件下的侧视图"

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