Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (8): 1842-1849.doi: 10.13229/j.cnki.jdxbgxb20210170

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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

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

CLC Number: 

  • TP39

Fig.1

Flowchart of Kin-PSO"

Table 1

100-digit challenge test functions"

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]

Table 2

Scores on 100-digit challenge experiment"

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

Table 3

Detail results of experiment"

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

Fig.2

Top view of dome under initial condition"

Fig.3

Side view of dome under initial condition"

Fig.4

Convergence curves of four algorithms in engineering experiments"

Table 4

Results of engineering example"

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

Table 5

Parameter table of engineering example"

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

Fig.5

Top view of dome under stress"

Fig.6

Side view of dome under stress"

1 陈宝林. 最优化理论与算法[M]. 北京: 清华大学出版社, 2005.
Chen Bao-lin. Theory and Algorithms of Optimization[M]. Beijing: Tsinghua University Press, 2005.
2 Sun S, Cao Z, Zhu H, et al. A survey of optimization methods from a machine learning perspective[J]. IEEE Transactions on Cybernetics, 2020, 50(8): 3668-3681.
3 Ismail M A, Mezhuyev V, Moorthy K, et al. Optimisation of biochemical systems production using hybrid of newton method, differential evolution algorithm and cooperative coevolution algorithm[J]. Indonesian Journal of Electrical Engineering and Computer Science, 2017, 8: 27-35.
4 Dennis J J E, Moré J J. Quasi-newton methods, motivation and theory[J]. Siam Review, 1977, 19(1): 46-89.
5 梁艳春. 群智能优化算法理论与应用[M]. 北京: 科学出版社, 2009.
6 Holland J H. Genetic algorithms[J]. Scholarpedia, 2012, 7(12): 1482.
7 Kennedy J, Eberhart R. Particle swarm optimization[C]∥Proceedings of ICNN'95 International Conference on Neural Networks, Perth, Australia, 1995: 1942-1948.
8 Shi Y, Eberhart R. A modified particle swarm optimizer[C]∥IEEE International Conference on Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, Indianaqolis,USA, 1998: 69-73.
9 Engelbrecht A P. Particle swarm optimization: global best or local best?[C]∥BRICS Congress on Computational Intelligence and 11th Brazilian Congress On Computational Intelligence, Pretoria, South Africa, 2013: 124-135.
10 Sengupta S, Basak S, Peters R A. Particle swarm optimization: a survey of historical and recent developments with hybridization perspectives:1[J]. Machine Learning and Knowledge Extraction, 2019, 1(1): 157-191.
11 Lynn N, Ali M Z, Suganthan P N. Population topologies for particle swarm optimization and differential evolution[J]. Swarm and Evolutionary Computation, 2018, 39: 24-35.
12 Liu L, Wu J, Meng S. Analysis and improvement of neighborhood topology of particle swarm optimization[J]. Journal of Computational Methods in Sciences and Engineering, 2019, 19(4): 955-968.
13 Li X. Niching without niching parameters: particle swarm optimization using a ring topology[J]. IEEE Transactions on Evolutionary Computation, 2009, 14(1): 150-169.
14 Miranda V, Keko H, Junior A J. Stochastic star communication topology in evolutionary particle swarms (EPSO)[J]. International Journal of Computational Intelligence Research, 2008, 4(2): 105-116.
15 West S A, Pen I, Griffin A S. Cooperation and competition between relatives[J]. Science, 2002, 296(5565): 72-75.
16 Nowak M A. Five rules for the evolution of cooperation[J]. Science, 2006, 314(5805): 1560-1563.
17 Wong K C. Evolutionary multimodal optimization: a short survey[J/OL]. [2020-08-04].
18 Liang J J, Qin A K, Suganthan P N, et al. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(3): 281-295.
19 Zhao X, Gao X S, Hu Z C. Evolutionary programming based on non-uniform mutation[J]. Applied Mathematics and Computation, 2007, 192(1): 1-11.
20 Lu J, Zhou X, Ma Y, et al. A novel artificial bee colony algorithm with division of labor for solving CEC 2019 100-Digit challenge benchmark problems[C]∥IEEE Congress on Evolutionary Computation, Nanchang, China, 2019: 387-394.
21 Brest J, MaučEc M S, BošKović B. iL-SHADE: improved L-SHADE algorithm for single objective real-parameter optimization[C]∥IEEE Congress on Evolutionary Computation, Maribor, Slovenia, 2016:1188-1195.
22 Xu P, Luo W, Lin X, et al. Hybrid of PSO and CMA-ES for global optimization[C]∥IEEE Congress on Evolutionary Computation, Hunan, China, 2019: 27-33.
23 Zhang G, Li Y, Ding B, et al. Univariate Gaussian model for multimodal inseparable problems[C]∥International Conference on Intelligent Computing, Liverpool, England, 2017: 612-623.
24 Thompson M K, Thompson J M. Ansys Mechanical Apdl for Finite Element Analysis[M]. Oxford: Butterworth-Heinemann, 2017.
25 张雄, 王天舒. 计算动力学[M]. 北京: 清华大学出版社, 2015.
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