Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (9): 2818-2828.DOI: 10.11772/j.issn.1001-9081.2023091304
• Advanced computing • Previous Articles Next Articles
Shanglong LI1,2, Jianhua LIU1,2(), Heming JIA3
Received:
2023-09-19
Revised:
2023-12-19
Accepted:
2023-12-25
Online:
2024-02-20
Published:
2024-09-10
Contact:
Jianhua LIU
About author:
LI Shanglong, born in 2000, M. S. candidate. His research interests include swarm intelligence optimization algorithm.Supported by:
通讯作者:
刘建华
作者简介:
力尚龙(2000—),男,山西朔州人,硕士研究生,主要研究方向:群智能优化算法基金资助:
CLC Number:
Shanglong LI, Jianhua LIU, Heming JIA. Reptile search algorithm based on multi-hunting coordination strategy[J]. Journal of Computer Applications, 2024, 44(9): 2818-2828.
力尚龙, 刘建华, 贾鹤鸣. 融合多狩猎协调策略的爬行动物搜索算法[J]. 《计算机应用》唯一官方网站, 2024, 44(9): 2818-2828.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023091304
算法 | 参数设置 |
---|---|
MHCS-RSA | α=0.1; β=0.005 |
RSA | α=0.1; β=0.1 |
HOA | w=1; phiD=0.2; phiI=0.2 |
ROA | C=0.1 |
RLWOA | Lbd=0.5; sgm=0.5; u=0.6 |
DSCA | a=2; wmin=0.1; wmax=0.9 |
DE | CR=0.2 |
PSO | w=0.9; c1=c2=2 |
MRSA | α=0.1; β=0.005 |
Tab. 1 Parameter setting of each algorithm
算法 | 参数设置 |
---|---|
MHCS-RSA | α=0.1; β=0.005 |
RSA | α=0.1; β=0.1 |
HOA | w=1; phiD=0.2; phiI=0.2 |
ROA | C=0.1 |
RLWOA | Lbd=0.5; sgm=0.5; u=0.6 |
DSCA | a=2; wmin=0.1; wmax=0.9 |
DE | CR=0.2 |
PSO | w=0.9; c1=c2=2 |
MRSA | α=0.1; β=0.005 |
函数 | 指标 | MHCS-RSA | RSA | HOA | ROA | RLWOA | DSCA | DE | PSO | MRSA |
---|---|---|---|---|---|---|---|---|---|---|
F1 | Best | 1.08E+02 | 6.49E+09 | 2.68E+08 | 2.07E+07 | 1.68E+09 | 1.39E+09 | 3.75E+08 | 4.15E+05 | 1.75E+02 |
Mean | 1.91E+03 | 1.27E+10 | 4.00E+08 | 1.67E+09 | 8.36E+09 | 4.52E+09 | 1.43E+09 | 1.64E+08 | 2.59E+03 | |
Std | 1.64E+03 | 4.03E+09 | 1.20E+08 | 1.39E+09 | 3.28E+09 | 2.00E+09 | 6.47E+08 | 4.78E+08 | 2.28E+03 | |
F2 | Best | 1.26E+03 | 2.37E+03 | 2.50E+03 | 1.65E+03 | 2.01E+03 | 1.97E+03 | 1.67E+03 | 1.54E+03 | 1.52E+03 |
Mean | 2.03E+03 | 2.79E+03 | 2.93E+03 | 2.15E+03 | 2.75E+03 | 2.50E+03 | 1.95E+03 | 1.99E+03 | 1.98E+03 | |
Std | 3.40E+02 | 1.67E+02 | 2.19E+02 | 2.48E+02 | 3.13E+02 | 2.19E+02 | 1.62E+02 | 2.40E+02 | 2.81E+02 | |
F3 | Best | 7.22E+02 | 7.85E+02 | 7.60E+02 | 7.38E+02 | 7.65E+02 | 7.89E+02 | 7.41E+02 | 7.33E+02 | 7.24E+02 |
Mean | 7.73E+02 | 8.12E+02 | 7.77E+02 | 7.82E+02 | 8.20E+02 | 8.12E+02 | 7.59E+02 | 7.47E+02 | 7.80E+02 | |
Std | 2.01E+01 | 1.03E+01 | 1.21E+01 | 2.25E+01 | 2.49E+01 | 1.21E+01 | 8.21E+00 | 8.21E+00 | 2.86E+01 | |
F4 | Best | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 |
Mean | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | |
Std | 0.00E+00 | 0.00E+00 | 2.92E+00 | 0.00E+00 | 0.00E+00 | 0.00E+00 | 2.75E-01 | 7.01E-01 | 0.00E+00 | |
F5 | Best | 1.82E+03 | 1.99E+05 | 6.11E+04 | 3.62E+03 | 7.66E+03 | 6.79E+04 | 4.01E+03 | 3.53E+03 | 1.19E+04 |
Mean | 5.30E+03 | 5.22E+05 | 3.13E+05 | 9.71E+04 | 1.32E+06 | 4.44E+05 | 1.00E+04 | 2.49E+04 | 1.78E+05 | |
Std | 4.15E+03 | 1.08E+05 | 2.18E+05 | 1.27E+05 | 1.66E+06 | 2.00E+05 | 4.48E+03 | 2.39E+04 | 1.27E+05 | |
F6 | Best | 1.60E+03 | 1.88E+03 | 1.82E+03 | 1.64E+03 | 1.69E+03 | 1.77E+03 | 1.62E+03 | 1.61E+03 | 1.61E+03 |
Mean | 1.92E+03 | 2.22E+03 | 2.05E+03 | 1.85E+03 | 2.10E+03 | 1.97E+03 | 1.77E+03 | 1.81E+03 | 1.91E+03 | |
Std | 1.68E+02 | 1.78E+02 | 1.24E+02 | 1.45E+02 | 1.73E+02 | 1.21E+02 | 6.47E+01 | 1.40E+02 | 1.45E+02 | |
F7 | Best | 2.30E+03 | 3.75E+04 | 7.69E+03 | 3.04E+03 | 6.77E+03 | 9.42E+03 | 4.03E+03 | 2.85E+03 | 2.13E+03 |
Mean | 8.77E+03 | 1.42E+06 | 2.41E+04 | 1.41E+04 | 5.79E+05 | 4.75E+04 | 4.18E+04 | 1.26E+04 | 1.29E+05 | |
Std | 6.84E+03 | 2.09E+06 | 1.22E+04 | 8.25E+03 | 6.83E+05 | 5.35E+04 | 3.47E+04 | 1.03E+04 | 6.33E+05 | |
F8 | Best | 2.24E+03 | 2.62E+03 | 2.30E+03 | 2.32E+03 | 2.32E+03 | 2.42E+03 | 2.27E+03 | 2.31E+03 | 2.30E+03 |
Mean | 2.31E+03 | 3.12E+03 | 2.43E+03 | 2.43E+03 | 2.87E+03 | 2.59E+03 | 2.32E+03 | 2.36E+03 | 2.31E+03 | |
Std | 1.37E+01 | 2.86E+02 | 4.03E+02 | 1.36E+02 | 4.22E+02 | 8.68E+01 | 2.53E+01 | 1.60E+02 | 1.94E+01 | |
F9 | Best | 2.50E+03 | 2.61E+03 | 2.55E+03 | 2.54E+03 | 2.78E+03 | 2.57E+03 | 2.53E+03 | 2.58E+03 | 2.50E+03 |
Mean | 2.69E+03 | 2.87E+03 | 2.76E+03 | 2.76E+03 | 2.85E+03 | 2.73E+03 | 2.64E+03 | 2.76E+03 | 2.75E+03 | |
Std | 1.12E+02 | 6.91E+01 | 1.08E+02 | 7.30E+01 | 4.23E+01 | 9.71E+01 | 7.40E+01 | 3.65E+01 | 1.22E+02 | |
F10 | Best | 2.90E+03 | 3.19E+03 | 2.94E+03 | 2.92E+03 | 3.02E+03 | 2.99E+03 | 2.92E+03 | 2.90E+03 | 2.90E+03 |
Mean | 2.93E+03 | 3.40E+03 | 2.97E+03 | 3.00E+03 | 3.33E+03 | 3.14E+03 | 2.99E+03 | 2.95E+03 | 2.94E+03 | |
Std | 2.10E+01 | 1.25E+02 | 1.95E+01 | 8.60E+01 | 1.78E+02 | 8.03E+01 | 3.22E+01 | 3.37E+01 | 2.74E+01 |
Tab. 2 Comparison results of different algorithms on CEC 2020 test functions
函数 | 指标 | MHCS-RSA | RSA | HOA | ROA | RLWOA | DSCA | DE | PSO | MRSA |
---|---|---|---|---|---|---|---|---|---|---|
F1 | Best | 1.08E+02 | 6.49E+09 | 2.68E+08 | 2.07E+07 | 1.68E+09 | 1.39E+09 | 3.75E+08 | 4.15E+05 | 1.75E+02 |
Mean | 1.91E+03 | 1.27E+10 | 4.00E+08 | 1.67E+09 | 8.36E+09 | 4.52E+09 | 1.43E+09 | 1.64E+08 | 2.59E+03 | |
Std | 1.64E+03 | 4.03E+09 | 1.20E+08 | 1.39E+09 | 3.28E+09 | 2.00E+09 | 6.47E+08 | 4.78E+08 | 2.28E+03 | |
F2 | Best | 1.26E+03 | 2.37E+03 | 2.50E+03 | 1.65E+03 | 2.01E+03 | 1.97E+03 | 1.67E+03 | 1.54E+03 | 1.52E+03 |
Mean | 2.03E+03 | 2.79E+03 | 2.93E+03 | 2.15E+03 | 2.75E+03 | 2.50E+03 | 1.95E+03 | 1.99E+03 | 1.98E+03 | |
Std | 3.40E+02 | 1.67E+02 | 2.19E+02 | 2.48E+02 | 3.13E+02 | 2.19E+02 | 1.62E+02 | 2.40E+02 | 2.81E+02 | |
F3 | Best | 7.22E+02 | 7.85E+02 | 7.60E+02 | 7.38E+02 | 7.65E+02 | 7.89E+02 | 7.41E+02 | 7.33E+02 | 7.24E+02 |
Mean | 7.73E+02 | 8.12E+02 | 7.77E+02 | 7.82E+02 | 8.20E+02 | 8.12E+02 | 7.59E+02 | 7.47E+02 | 7.80E+02 | |
Std | 2.01E+01 | 1.03E+01 | 1.21E+01 | 2.25E+01 | 2.49E+01 | 1.21E+01 | 8.21E+00 | 8.21E+00 | 2.86E+01 | |
F4 | Best | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 |
Mean | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | |
Std | 0.00E+00 | 0.00E+00 | 2.92E+00 | 0.00E+00 | 0.00E+00 | 0.00E+00 | 2.75E-01 | 7.01E-01 | 0.00E+00 | |
F5 | Best | 1.82E+03 | 1.99E+05 | 6.11E+04 | 3.62E+03 | 7.66E+03 | 6.79E+04 | 4.01E+03 | 3.53E+03 | 1.19E+04 |
Mean | 5.30E+03 | 5.22E+05 | 3.13E+05 | 9.71E+04 | 1.32E+06 | 4.44E+05 | 1.00E+04 | 2.49E+04 | 1.78E+05 | |
Std | 4.15E+03 | 1.08E+05 | 2.18E+05 | 1.27E+05 | 1.66E+06 | 2.00E+05 | 4.48E+03 | 2.39E+04 | 1.27E+05 | |
F6 | Best | 1.60E+03 | 1.88E+03 | 1.82E+03 | 1.64E+03 | 1.69E+03 | 1.77E+03 | 1.62E+03 | 1.61E+03 | 1.61E+03 |
Mean | 1.92E+03 | 2.22E+03 | 2.05E+03 | 1.85E+03 | 2.10E+03 | 1.97E+03 | 1.77E+03 | 1.81E+03 | 1.91E+03 | |
Std | 1.68E+02 | 1.78E+02 | 1.24E+02 | 1.45E+02 | 1.73E+02 | 1.21E+02 | 6.47E+01 | 1.40E+02 | 1.45E+02 | |
F7 | Best | 2.30E+03 | 3.75E+04 | 7.69E+03 | 3.04E+03 | 6.77E+03 | 9.42E+03 | 4.03E+03 | 2.85E+03 | 2.13E+03 |
Mean | 8.77E+03 | 1.42E+06 | 2.41E+04 | 1.41E+04 | 5.79E+05 | 4.75E+04 | 4.18E+04 | 1.26E+04 | 1.29E+05 | |
Std | 6.84E+03 | 2.09E+06 | 1.22E+04 | 8.25E+03 | 6.83E+05 | 5.35E+04 | 3.47E+04 | 1.03E+04 | 6.33E+05 | |
F8 | Best | 2.24E+03 | 2.62E+03 | 2.30E+03 | 2.32E+03 | 2.32E+03 | 2.42E+03 | 2.27E+03 | 2.31E+03 | 2.30E+03 |
Mean | 2.31E+03 | 3.12E+03 | 2.43E+03 | 2.43E+03 | 2.87E+03 | 2.59E+03 | 2.32E+03 | 2.36E+03 | 2.31E+03 | |
Std | 1.37E+01 | 2.86E+02 | 4.03E+02 | 1.36E+02 | 4.22E+02 | 8.68E+01 | 2.53E+01 | 1.60E+02 | 1.94E+01 | |
F9 | Best | 2.50E+03 | 2.61E+03 | 2.55E+03 | 2.54E+03 | 2.78E+03 | 2.57E+03 | 2.53E+03 | 2.58E+03 | 2.50E+03 |
Mean | 2.69E+03 | 2.87E+03 | 2.76E+03 | 2.76E+03 | 2.85E+03 | 2.73E+03 | 2.64E+03 | 2.76E+03 | 2.75E+03 | |
Std | 1.12E+02 | 6.91E+01 | 1.08E+02 | 7.30E+01 | 4.23E+01 | 9.71E+01 | 7.40E+01 | 3.65E+01 | 1.22E+02 | |
F10 | Best | 2.90E+03 | 3.19E+03 | 2.94E+03 | 2.92E+03 | 3.02E+03 | 2.99E+03 | 2.92E+03 | 2.90E+03 | 2.90E+03 |
Mean | 2.93E+03 | 3.40E+03 | 2.97E+03 | 3.00E+03 | 3.33E+03 | 3.14E+03 | 2.99E+03 | 2.95E+03 | 2.94E+03 | |
Std | 2.10E+01 | 1.25E+02 | 1.95E+01 | 8.60E+01 | 1.78E+02 | 8.03E+01 | 3.22E+01 | 3.37E+01 | 2.74E+01 |
函数 | RSA | HOA | ROA | RLWOA | DSCA | DE | PSO | MRSA |
---|---|---|---|---|---|---|---|---|
+/=/- | 9/1/0 | 9/1/0 | 8/2/0 | 9/1/0 | 9/1/0 | 6/3/1 | 7/2/1 | 4/6/0 |
F1 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 4.21E-01 |
F2 | 1.22E-04 | 6.10E-05 | 2.29E-01 | 1.83E-04 | 3.05E-04 | 7.62E-01 | 5.24E-01 | 8.04E-01 |
F3 | 6.10E-05 | 5.99E-01 | 6.10E-04 | 6.10E-05 | 1.22E-04 | -4.54E-01 | -3.02E-02 | 1.21E-01 |
F4 | 1.00E+00 | 7.81E-03 | 1.00E+00 | 1.00E+00 | 1.00E+00 | 6.10E-05 | 6.10E-05 | 1.00E+00 |
F5 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 4.13E-02 | 1.25E-02 | 6.10E-05 |
F6 | 1.22E-04 | 2.62E-03 | 4.13E-02 | 3.05E-04 | 3.02E-02 | -4.13E-02 | -9.34E-01 | 1.53E-03 |
F7 | 6.10E-05 | 8.54E-04 | 2.56E-02 | 1.83E-04 | 6.10E-05 | 6.10E-05 | 8.36E-03 | 4.13E-02 |
F8 | 6.10E-05 | 1.22E-04 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 6.71E-03 | 6.10E-04 | 1.51E-02 |
F9 | 6.10E-05 | 3.02E-02 | 4.79E-02 | 6.10E-05 | 3.02E-02 | 2.08E-01 | 1.22E-04 | 8.90E-01 |
F10 | 6.10E-05 | 1.03E-02 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 2.56E-02 | 5.54E-02 |
Tab. 3 Comparison results of different algorithms in Wilcoxon rank-sum test
函数 | RSA | HOA | ROA | RLWOA | DSCA | DE | PSO | MRSA |
---|---|---|---|---|---|---|---|---|
+/=/- | 9/1/0 | 9/1/0 | 8/2/0 | 9/1/0 | 9/1/0 | 6/3/1 | 7/2/1 | 4/6/0 |
F1 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 4.21E-01 |
F2 | 1.22E-04 | 6.10E-05 | 2.29E-01 | 1.83E-04 | 3.05E-04 | 7.62E-01 | 5.24E-01 | 8.04E-01 |
F3 | 6.10E-05 | 5.99E-01 | 6.10E-04 | 6.10E-05 | 1.22E-04 | -4.54E-01 | -3.02E-02 | 1.21E-01 |
F4 | 1.00E+00 | 7.81E-03 | 1.00E+00 | 1.00E+00 | 1.00E+00 | 6.10E-05 | 6.10E-05 | 1.00E+00 |
F5 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 4.13E-02 | 1.25E-02 | 6.10E-05 |
F6 | 1.22E-04 | 2.62E-03 | 4.13E-02 | 3.05E-04 | 3.02E-02 | -4.13E-02 | -9.34E-01 | 1.53E-03 |
F7 | 6.10E-05 | 8.54E-04 | 2.56E-02 | 1.83E-04 | 6.10E-05 | 6.10E-05 | 8.36E-03 | 4.13E-02 |
F8 | 6.10E-05 | 1.22E-04 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 6.71E-03 | 6.10E-04 | 1.51E-02 |
F9 | 6.10E-05 | 3.02E-02 | 4.79E-02 | 6.10E-05 | 3.02E-02 | 2.08E-01 | 1.22E-04 | 8.90E-01 |
F10 | 6.10E-05 | 1.03E-02 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 6.10E-05 | 2.56E-02 | 5.54E-02 |
函数 | MHCS-RSA | RSA | HOA | ROA | RLWOA | DSCA | DE | PSO | MRSA |
---|---|---|---|---|---|---|---|---|---|
F1 | 0.865 547 2 | 0.466 866 2 | 0.680 994 9 | 0.429 403 2 | 0.194 786 9 | 0.187 439 1 | 0.398 512 2 | 0.204 114 7 | 0.615 434 8 |
F2 | 0.596 003 6 | 0.320 4 570 | 0.410 579 7 | 0.302 356 6 | 0.067 340 4 | 0.102 662 5 | 0.229 453 1 | 0.108 317 3 | 0.491 541 7 |
F3 | 0.460 5 690 | 0.320 290 6 | 0.399 804 9 | 0.278 5 350 | 0.066 473 3 | 0.086 961 9 | 0.289 877 2 | 0.119 132 7 | 0.474 818 4 |
F4 | 0.468 607 4 | 0.309 820 6 | 0.400 973 6 | 0.216 281 7 | 0.056 716 1 | 0.093 125 0 | 0.188 858 9 | 0.095 986 9 | 0.410 7 020 |
F5 | 0.572 142 2 | 0.389 536 6 | 0.442 357 3 | 0.245 041 8 | 0.061 646 8 | 0.112 834 2 | 0.207 858 7 | 0.097 707 6 | 0.417 820 9 |
F6 | 0.467 494 5 | 0.307 5 350 | 0.414 101 2 | 0.247 224 2 | 0.060 475 6 | 0.076 890 9 | 0.200 2 390 | 0.094 210 9 | 0.411 226 1 |
F7 | 0.456 926 6 | 0.317 495 8 | 0.363 947 4 | 0.222 376 8 | 0.072 903 9 | 0.077 383 0 | 0.197 891 2 | 0.093 985 2 | 0.445 268 1 |
F8 | 0.567 594 1 | 0.353 148 6 | 0.396 260 5 | 0.309 337 8 | 0.112 694 4 | 0.097 354 2 | 0.219 657 7 | 0.121 633 8 | 0.553 843 9 |
F9 | 0.550 237 3 | 0.357 430 2 | 0.423 809 6 | 0.348 179 6 | 0.092 146 7 | 0.108 224 1 | 0.235 565 1 | 0.127 872 7 | 0.561 312 4 |
F10 | 0.560 6 440 | 0.343 9 820 | 0.385 282 2 | 0.336 559 7 | 0.081 245 1 | 0.095 593 6 | 0.225 8 590 | 0.121 018 9 | 0.510 322 5 |
Tab. 4 Comparison of computing time of different algorithms on CEC 2020 test functions
函数 | MHCS-RSA | RSA | HOA | ROA | RLWOA | DSCA | DE | PSO | MRSA |
---|---|---|---|---|---|---|---|---|---|
F1 | 0.865 547 2 | 0.466 866 2 | 0.680 994 9 | 0.429 403 2 | 0.194 786 9 | 0.187 439 1 | 0.398 512 2 | 0.204 114 7 | 0.615 434 8 |
F2 | 0.596 003 6 | 0.320 4 570 | 0.410 579 7 | 0.302 356 6 | 0.067 340 4 | 0.102 662 5 | 0.229 453 1 | 0.108 317 3 | 0.491 541 7 |
F3 | 0.460 5 690 | 0.320 290 6 | 0.399 804 9 | 0.278 5 350 | 0.066 473 3 | 0.086 961 9 | 0.289 877 2 | 0.119 132 7 | 0.474 818 4 |
F4 | 0.468 607 4 | 0.309 820 6 | 0.400 973 6 | 0.216 281 7 | 0.056 716 1 | 0.093 125 0 | 0.188 858 9 | 0.095 986 9 | 0.410 7 020 |
F5 | 0.572 142 2 | 0.389 536 6 | 0.442 357 3 | 0.245 041 8 | 0.061 646 8 | 0.112 834 2 | 0.207 858 7 | 0.097 707 6 | 0.417 820 9 |
F6 | 0.467 494 5 | 0.307 5 350 | 0.414 101 2 | 0.247 224 2 | 0.060 475 6 | 0.076 890 9 | 0.200 2 390 | 0.094 210 9 | 0.411 226 1 |
F7 | 0.456 926 6 | 0.317 495 8 | 0.363 947 4 | 0.222 376 8 | 0.072 903 9 | 0.077 383 0 | 0.197 891 2 | 0.093 985 2 | 0.445 268 1 |
F8 | 0.567 594 1 | 0.353 148 6 | 0.396 260 5 | 0.309 337 8 | 0.112 694 4 | 0.097 354 2 | 0.219 657 7 | 0.121 633 8 | 0.553 843 9 |
F9 | 0.550 237 3 | 0.357 430 2 | 0.423 809 6 | 0.348 179 6 | 0.092 146 7 | 0.108 224 1 | 0.235 565 1 | 0.127 872 7 | 0.561 312 4 |
F10 | 0.560 6 440 | 0.343 9 820 | 0.385 282 2 | 0.336 559 7 | 0.081 245 1 | 0.095 593 6 | 0.225 8 590 | 0.121 018 9 | 0.510 322 5 |
函数 | 统计数据 | LOBLRSA | MHCS-RSA | RSA |
---|---|---|---|---|
F1 | Best | 3.34E+09 | 2.50E+02 | |
Mean | 1.22E+10 | 3.14E+03 | ||
Std | 2.85E+09 | 2.54E+03 | ||
F2 | Best | 2.20E+03 | 1.24E+03 | |
Mean | 2.76E+03 | 2.18E+03 | ||
Std | 2.25E+02 | 1.67E+02 | ||
F3 | Best | 7.47E+02 | 7.85E+02 | |
Mean | 8.10E+02 | 7.73E+02 | ||
Std | 1.02E+01 | 1.03E+01 | ||
F4 | Best | |||
Mean | ||||
Std | ||||
F5 | Best | 9.72E+04 | 1.78E+03 | |
Mean | 7.25E+03 | 5.22E+05 | ||
Std | 8.54E+03 | 1.08E+05 | ||
F6 | Best | 1.81E+03 | 1.60E+03 | |
Mean | 2.16E+03 | 1.93E+03 | ||
Std | 1.47E+02 | 1.44E+02 | ||
F7 | Best | 1.29E+04 | 2.27E+03 | |
Mean | 1.02E+04 | 1.42E+06 | ||
Std | 1.82E+06 | 7.29E+03 | ||
F8 | Best | 2.30E+03 | 2.62E+03 | |
Mean | 2.31E+03 | 3.12E+03 | ||
Std | 4.62E+00 | 2.86E+02 | ||
F9 | Best | 2.50E+03 | 2.61E+03 | |
Mean | 2.74E+03 | 2.87E+03 | ||
Std | 8.29E+01 | 6.91E+01 | ||
F10 | Best | 2.90E+03 | ||
Mean | 3.39E+03 | 2.93E+03 | ||
Std | 2.27E+01 | 1.25E+02 |
Tab. 5 Comparison results of single strategies
函数 | 统计数据 | LOBLRSA | MHCS-RSA | RSA |
---|---|---|---|---|
F1 | Best | 3.34E+09 | 2.50E+02 | |
Mean | 1.22E+10 | 3.14E+03 | ||
Std | 2.85E+09 | 2.54E+03 | ||
F2 | Best | 2.20E+03 | 1.24E+03 | |
Mean | 2.76E+03 | 2.18E+03 | ||
Std | 2.25E+02 | 1.67E+02 | ||
F3 | Best | 7.47E+02 | 7.85E+02 | |
Mean | 8.10E+02 | 7.73E+02 | ||
Std | 1.02E+01 | 1.03E+01 | ||
F4 | Best | |||
Mean | ||||
Std | ||||
F5 | Best | 9.72E+04 | 1.78E+03 | |
Mean | 7.25E+03 | 5.22E+05 | ||
Std | 8.54E+03 | 1.08E+05 | ||
F6 | Best | 1.81E+03 | 1.60E+03 | |
Mean | 2.16E+03 | 1.93E+03 | ||
Std | 1.47E+02 | 1.44E+02 | ||
F7 | Best | 1.29E+04 | 2.27E+03 | |
Mean | 1.02E+04 | 1.42E+06 | ||
Std | 1.82E+06 | 7.29E+03 | ||
F8 | Best | 2.30E+03 | 2.62E+03 | |
Mean | 2.31E+03 | 3.12E+03 | ||
Std | 4.62E+00 | 2.86E+02 | ||
F9 | Best | 2.50E+03 | 2.61E+03 | |
Mean | 2.74E+03 | 2.87E+03 | ||
Std | 8.29E+01 | 6.91E+01 | ||
F10 | Best | 2.90E+03 | ||
Mean | 3.39E+03 | 2.93E+03 | ||
Std | 2.27E+01 | 1.25E+02 |
函数 | 指标 | 不同α和β组合下的适应度值 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
(0.1,0.005) | (0.1,0.100) | (0.1,0.900) | (0.5,0.005) | (0.5,0.100) | (0.5,0.900) | (0.9,0.005) | (0.9,0.100) | (0.9,0.900) | ||
F1 | Best | 1.08E+02 | 1.76E+02 | 1.12E+02 | 2.85E+02 | 1.25E+02 | 1.13E+02 | 1.08E+02 | 1.23E+02 | 1.30E+02 |
Mean | 1.91E+03 | 4.53E+03 | 2.74E+03 | 3.76E+03 | 2.99E+03 | 2.66E+03 | 2.16E+03 | 2.81E+03 | 3.30E+03 | |
Std | 1.64E+03 | 1.26E+04 | 2.19E+03 | 7.48E+03 | 4.93E+03 | 2.31E+03 | 2.59E+03 | 3.31E+03 | 5.33E+03 | |
F2 | Best | 1.26E+03 | 1.43E+03 | 1.64E+03 | 1.50E+03 | 1.46E+03 | 1.54E+03 | 1.46E+03 | 1.36E+03 | 1.55E+03 |
Mean | 2.03E+03 | 2.09E+03 | 2.20E+03 | 2.14E+03 | 2.14E+03 | 2.18E+03 | 2.16E+03 | 2.13E+03 | 2.16E+03 | |
Std | 3.40E+02 | 2.98E+02 | 3.46E+02 | 3.14E+02 | 3.43E+02 | 3.53E+02 | 3.28E+02 | 3.73E+02 | 4.17E+02 | |
F3 | Best | 7.22E+02 | 7.44E+02 | 7.38E+02 | 7.42E+02 | 7.45E+02 | 7.26E+02 | 7.39E+02 | 7.40E+02 | 7.24E+02 |
Mean | 7.73E+02 | 7.78E+02 | 7.67E+02 | 7.76E+02 | 7.72E+02 | 7.55E+02 | 7.71E+02 | 7.73E+02 | 7.54E+02 | |
Std | 2.01E+01 | 1.75E+01 | 1.77E+01 | 1.85E+01 | 1.67E+01 | 1.46E+01 | 1.92E+01 | 2.09E+01 | 1.69E+01 | |
F4 | Best | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 |
Mean | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | |
Std | 0.00E+00 | 0.00E+00 | 0.00E+00 | 0.00E+00 | 0.00E+00 | 0.00E+00 | 0.00E+00 | 0.00E+00 | 0.00E+00 | |
F5 | Best | 1.82E+03 | 3.40E+03 | 3.18E+03 | 3.59E+03 | 2.67E+03 | 2.36E+03 | 4.23E+03 | 2.45E+03 | 3.64E+03 |
Mean | 5.30E+03 | 9.41E+04 | 4.75E+04 | 1.09E+05 | 1.06E+05 | 6.68E+04 | 9.25E+04 | 9.18E+04 | 9.63E+04 | |
Std | 4.15E+03 | 1.00E+05 | 7.79E+04 | 9.65E+04 | 1.18E+05 | 6.88E+04 | 9.89E+04 | 1.05E+05 | 1.07E+05 | |
F6 | Best | 1.60E+03 | 1.60E+03 | 1.60E+03 | 1.60E+03 | 1.60E+03 | 1.60E+03 | 1.60E+03 | 1.60E+03 | 1.60E+03 |
Mean | 1.92E+03 | 1.75E+03 | 1.78E+03 | 1.80E+03 | 1.76E+03 | 1.72E+03 | 1.81E+03 | 1.74E+03 | 1.70E+03 | |
Std | 1.68E+02 | 1.26E+02 | 1.28E+02 | 1.28E+02 | 1.08E+02 | 9.88E+01 | 1.34E+02 | 1.06E+02 | 9.12E+01 | |
F7 | Best | 2.30E+03 | 2.15E+03 | 2.20E+03 | 2.14E+03 | 2.18E+03 | 2.13E+03 | 2.33E+03 | 2.20E+03 | 2.19E+03 |
Mean | 8.77E+03 | 6.00E+03 | 6.46E+03 | 6.81E+03 | 4.54E+03 | 3.85E+03 | 8.51E+03 | 4.76E+03 | 3.91E+03 | |
Std | 6.84E+03 | 5.66E+03 | 6.66E+03 | 6.46E+03 | 4.09E+03 | 2.53E+03 | 8.23E+03 | 5.29E+03 | 2.40E+03 | |
F8 | Best | 2.24E+03 | 2.30E+03 | 2.23E+03 | 2.25E+03 | 2.24E+03 | 2.30E+03 | 2.30E+03 | 2.24E+03 | 2.27E+03 |
Mean | 2.31E+03 | 2.31E+03 | 2.30E+03 | 2.30E+03 | 2.30E+03 | 2.31E+03 | 2.31E+03 | 2.30E+03 | 2.30E+03 | |
Std | 1.37E+01 | 3.99E+00 | 1.45E+01 | 1.20E+01 | 1.29E+01 | 5.48E+00 | 4.19E+00 | 1.24E+01 | 7.66E+00 | |
F9 | Best | 2.50E+03 | 2.50E+03 | 2.50E+03 | 2.50E+03 | 2.50E+03 | 2.74E+03 | 2.50E+03 | 2.50E+03 | 2.55E+03 |
Mean | 2.69E+03 | 2.70E+03 | 2.72E+03 | 2.72E+03 | 2.71E+03 | 2.75E+03 | 2.72E+03 | 2.70E+03 | 2.74E+03 | |
Std | 1.12E+02 | 1.15E+02 | 9.48E+01 | 8.83E+01 | 9.74E+01 | 1.75E+01 | 9.90E+01 | 1.04E+02 | 3.80E+01 | |
F10 | Best | 2.90E+03 | 2.90E+03 | 2.90E+03 | 2.90E+03 | 2.90E+03 | 2.90E+03 | 2.90E+03 | 2.90E+03 | 2.90E+03 |
Mean | 2.93E+03 | 2.94E+03 | 2.94E+03 | 2.94E+03 | 2.94E+03 | 2.94E+03 | 2.94E+03 | 2.94E+03 | 2.94E+03 | |
Std | 2.10E+01 | 1.91E+01 | 1.73E+01 | 1.66E+01 | 1.71E+01 | 2.31E+01 | 2.17E+01 | 2.11E+01 | 2.01E+01 |
Tab. 6 Sensitivity analysis of parameters
函数 | 指标 | 不同α和β组合下的适应度值 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
(0.1,0.005) | (0.1,0.100) | (0.1,0.900) | (0.5,0.005) | (0.5,0.100) | (0.5,0.900) | (0.9,0.005) | (0.9,0.100) | (0.9,0.900) | ||
F1 | Best | 1.08E+02 | 1.76E+02 | 1.12E+02 | 2.85E+02 | 1.25E+02 | 1.13E+02 | 1.08E+02 | 1.23E+02 | 1.30E+02 |
Mean | 1.91E+03 | 4.53E+03 | 2.74E+03 | 3.76E+03 | 2.99E+03 | 2.66E+03 | 2.16E+03 | 2.81E+03 | 3.30E+03 | |
Std | 1.64E+03 | 1.26E+04 | 2.19E+03 | 7.48E+03 | 4.93E+03 | 2.31E+03 | 2.59E+03 | 3.31E+03 | 5.33E+03 | |
F2 | Best | 1.26E+03 | 1.43E+03 | 1.64E+03 | 1.50E+03 | 1.46E+03 | 1.54E+03 | 1.46E+03 | 1.36E+03 | 1.55E+03 |
Mean | 2.03E+03 | 2.09E+03 | 2.20E+03 | 2.14E+03 | 2.14E+03 | 2.18E+03 | 2.16E+03 | 2.13E+03 | 2.16E+03 | |
Std | 3.40E+02 | 2.98E+02 | 3.46E+02 | 3.14E+02 | 3.43E+02 | 3.53E+02 | 3.28E+02 | 3.73E+02 | 4.17E+02 | |
F3 | Best | 7.22E+02 | 7.44E+02 | 7.38E+02 | 7.42E+02 | 7.45E+02 | 7.26E+02 | 7.39E+02 | 7.40E+02 | 7.24E+02 |
Mean | 7.73E+02 | 7.78E+02 | 7.67E+02 | 7.76E+02 | 7.72E+02 | 7.55E+02 | 7.71E+02 | 7.73E+02 | 7.54E+02 | |
Std | 2.01E+01 | 1.75E+01 | 1.77E+01 | 1.85E+01 | 1.67E+01 | 1.46E+01 | 1.92E+01 | 2.09E+01 | 1.69E+01 | |
F4 | Best | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 |
Mean | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | 1.90E+03 | |
Std | 0.00E+00 | 0.00E+00 | 0.00E+00 | 0.00E+00 | 0.00E+00 | 0.00E+00 | 0.00E+00 | 0.00E+00 | 0.00E+00 | |
F5 | Best | 1.82E+03 | 3.40E+03 | 3.18E+03 | 3.59E+03 | 2.67E+03 | 2.36E+03 | 4.23E+03 | 2.45E+03 | 3.64E+03 |
Mean | 5.30E+03 | 9.41E+04 | 4.75E+04 | 1.09E+05 | 1.06E+05 | 6.68E+04 | 9.25E+04 | 9.18E+04 | 9.63E+04 | |
Std | 4.15E+03 | 1.00E+05 | 7.79E+04 | 9.65E+04 | 1.18E+05 | 6.88E+04 | 9.89E+04 | 1.05E+05 | 1.07E+05 | |
F6 | Best | 1.60E+03 | 1.60E+03 | 1.60E+03 | 1.60E+03 | 1.60E+03 | 1.60E+03 | 1.60E+03 | 1.60E+03 | 1.60E+03 |
Mean | 1.92E+03 | 1.75E+03 | 1.78E+03 | 1.80E+03 | 1.76E+03 | 1.72E+03 | 1.81E+03 | 1.74E+03 | 1.70E+03 | |
Std | 1.68E+02 | 1.26E+02 | 1.28E+02 | 1.28E+02 | 1.08E+02 | 9.88E+01 | 1.34E+02 | 1.06E+02 | 9.12E+01 | |
F7 | Best | 2.30E+03 | 2.15E+03 | 2.20E+03 | 2.14E+03 | 2.18E+03 | 2.13E+03 | 2.33E+03 | 2.20E+03 | 2.19E+03 |
Mean | 8.77E+03 | 6.00E+03 | 6.46E+03 | 6.81E+03 | 4.54E+03 | 3.85E+03 | 8.51E+03 | 4.76E+03 | 3.91E+03 | |
Std | 6.84E+03 | 5.66E+03 | 6.66E+03 | 6.46E+03 | 4.09E+03 | 2.53E+03 | 8.23E+03 | 5.29E+03 | 2.40E+03 | |
F8 | Best | 2.24E+03 | 2.30E+03 | 2.23E+03 | 2.25E+03 | 2.24E+03 | 2.30E+03 | 2.30E+03 | 2.24E+03 | 2.27E+03 |
Mean | 2.31E+03 | 2.31E+03 | 2.30E+03 | 2.30E+03 | 2.30E+03 | 2.31E+03 | 2.31E+03 | 2.30E+03 | 2.30E+03 | |
Std | 1.37E+01 | 3.99E+00 | 1.45E+01 | 1.20E+01 | 1.29E+01 | 5.48E+00 | 4.19E+00 | 1.24E+01 | 7.66E+00 | |
F9 | Best | 2.50E+03 | 2.50E+03 | 2.50E+03 | 2.50E+03 | 2.50E+03 | 2.74E+03 | 2.50E+03 | 2.50E+03 | 2.55E+03 |
Mean | 2.69E+03 | 2.70E+03 | 2.72E+03 | 2.72E+03 | 2.71E+03 | 2.75E+03 | 2.72E+03 | 2.70E+03 | 2.74E+03 | |
Std | 1.12E+02 | 1.15E+02 | 9.48E+01 | 8.83E+01 | 9.74E+01 | 1.75E+01 | 9.90E+01 | 1.04E+02 | 3.80E+01 | |
F10 | Best | 2.90E+03 | 2.90E+03 | 2.90E+03 | 2.90E+03 | 2.90E+03 | 2.90E+03 | 2.90E+03 | 2.90E+03 | 2.90E+03 |
Mean | 2.93E+03 | 2.94E+03 | 2.94E+03 | 2.94E+03 | 2.94E+03 | 2.94E+03 | 2.94E+03 | 2.94E+03 | 2.94E+03 | |
Std | 2.10E+01 | 1.91E+01 | 1.73E+01 | 1.66E+01 | 1.71E+01 | 2.31E+01 | 2.17E+01 | 2.11E+01 | 2.01E+01 |
算法 | d/in | D/in | N | 质量/lb |
---|---|---|---|---|
OBSCA | 0.052 300 | 0.317 280 | 12.548 540 | 0.012 625 00 |
GA | 0.051 480 | 0.351 661 | 11.632 201 | 0.012 704 78 |
HS | 0.051 154 | 0.349 871 | 12.076 432 | 0.012 670 60 |
PSO | 0.051 728 | 0.357 644 | 11.244 543 | 0.012 674 70 |
ES | 0.051 643 | 0.355 360 | 11.397 926 | 0.012 698 00 |
WOA | 0.051 207 | 0.345 215 | 12.004 032 | 0.012 676 30 |
MVO | 0.052 510 | 0.376 020 | 10.335 130 | 0.012 790 00 |
RSA | 0.057 814 | 0.586 780 | 4.016 700 | 0.011 760 00 |
MHCS-RSA | 0.050 000 | 0.374 430 | 8.546 600 | 0.009 872 50 |
Tab. 7 Results of tension/compression spring design problem
算法 | d/in | D/in | N | 质量/lb |
---|---|---|---|---|
OBSCA | 0.052 300 | 0.317 280 | 12.548 540 | 0.012 625 00 |
GA | 0.051 480 | 0.351 661 | 11.632 201 | 0.012 704 78 |
HS | 0.051 154 | 0.349 871 | 12.076 432 | 0.012 670 60 |
PSO | 0.051 728 | 0.357 644 | 11.244 543 | 0.012 674 70 |
ES | 0.051 643 | 0.355 360 | 11.397 926 | 0.012 698 00 |
WOA | 0.051 207 | 0.345 215 | 12.004 032 | 0.012 676 30 |
MVO | 0.052 510 | 0.376 020 | 10.335 130 | 0.012 790 00 |
RSA | 0.057 814 | 0.586 780 | 4.016 700 | 0.011 760 00 |
MHCS-RSA | 0.050 000 | 0.374 430 | 8.546 600 | 0.009 872 50 |
算法 | x1/cm | x2 | x3 | x4/cm | x5/cm | x6/cm | x7/cm | 质量/kg |
---|---|---|---|---|---|---|---|---|
GA | 3.510 253 | 0.700 0 | 17.000 0 | 8.350 000 | 7.800 000 | 3.362 201 | 5.287 723 | 3 067.561 000 |
HS | 3.520 124 | 0.700 0 | 17.000 0 | 8.370 000 | 7.800 000 | 3.366 970 | 5.288 719 | 3 029.002 000 |
SCA | 3.508 755 | 0.700 0 | 17.000 0 | 7.300 000 | 7.800 000 | 3.461 020 | 5.289 213 | 3 030.563 000 |
CS | 3.501 500 | 0.700 0 | 17.000 0 | 7.605 000 | 7.818 100 | 3.352 000 | 5.287 500 | 3 000.981 000 |
PSO | 3.500 100 | 0.700 0 | 17.000 2 | 7.517 700 | 7.783 200 | 3.350 800 | 5.286 700 | 3 145.922 000 |
FA | 3.507 495 | 0.700 1 | 17.000 0 | 7.719 674 | 8.080 854 | 3.351 512 | 5.287 051 | 3 010.137 492 |
RSA | 3.502 790 | 0.700 0 | 17.000 0 | 7.308 120 | 7.747 150 | 3.350 670 | 5.286 750 | 2 996.515 700 |
MHCS-RSA | 3.497 600 | 0.700 0 | 17.000 0 | 7.300 000 | 7.800 000 | 3.350 060 | 5.285 530 | 2 995.437 400 |
Tab. 8 Results of speed reducer design problem
算法 | x1/cm | x2 | x3 | x4/cm | x5/cm | x6/cm | x7/cm | 质量/kg |
---|---|---|---|---|---|---|---|---|
GA | 3.510 253 | 0.700 0 | 17.000 0 | 8.350 000 | 7.800 000 | 3.362 201 | 5.287 723 | 3 067.561 000 |
HS | 3.520 124 | 0.700 0 | 17.000 0 | 8.370 000 | 7.800 000 | 3.366 970 | 5.288 719 | 3 029.002 000 |
SCA | 3.508 755 | 0.700 0 | 17.000 0 | 7.300 000 | 7.800 000 | 3.461 020 | 5.289 213 | 3 030.563 000 |
CS | 3.501 500 | 0.700 0 | 17.000 0 | 7.605 000 | 7.818 100 | 3.352 000 | 5.287 500 | 3 000.981 000 |
PSO | 3.500 100 | 0.700 0 | 17.000 2 | 7.517 700 | 7.783 200 | 3.350 800 | 5.286 700 | 3 145.922 000 |
FA | 3.507 495 | 0.700 1 | 17.000 0 | 7.719 674 | 8.080 854 | 3.351 512 | 5.287 051 | 3 010.137 492 |
RSA | 3.502 790 | 0.700 0 | 17.000 0 | 7.308 120 | 7.747 150 | 3.350 670 | 5.286 750 | 2 996.515 700 |
MHCS-RSA | 3.497 600 | 0.700 0 | 17.000 0 | 7.300 000 | 7.800 000 | 3.350 060 | 5.285 530 | 2 995.437 400 |
1 | WOLPERT D H, MACREADY W G. No free lunch theorems for optimization [J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 67-82. |
2 | MIRJALILI S. Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm [J]. Knowledge-Based Systems, 2015, 89: 228-249. |
3 | MIRJALILI S, LEWIS A. The whale optimization algorithm [J]. Advances in Engineering Software, 2016, 95: 51-67. |
4 | MIRJALILI S, GANDOMI A H, MIRJALILI S Z, et al. Salp swarm algorithm: a bio-inspired optimizer for engineering design problems[J]. Advances in Engineering Software, 2017, 114: 163-191. |
5 | MIRJALILI S. SCA: a Sine Cosine Algorithm for solving optimization problems [J]. Knowledge-Based Systems, 2016, 96: 120-133. |
6 | MiarNAEIMI F, AZIZYAN G, RASHKI M. Horse herd optimization algorithm: a nature-inspired algorithm for high-dimensional optimization problems [J]. Knowledge-Based Systems, 2021, 213: No.106711. |
7 | JIA H, PENG X, LANG C. Remora optimization algorithm [J]. Expert Systems with Applications, 2021, 185: No.115665. |
8 | LI S, CHEN H, WANG M, et al. Slime mould algorithm: a new method for stochastic optimization [J]. Future Generation Computer Systems, 2020, 111: 300-323. |
9 | ABUALIGAH L, ELAZIZ M A, SUMARI P, et al. Reptile Search Algorithm (RSA): a novel nature-inspired meta-heuristic optimizer[J]. Expert Systems with Applications, 2022, 191: No.116158. |
10 | ELGAMAL Z, SABRI A Q M, TUBISHAT M, et al. Improved reptile search optimization algorithm using chaotic map and simulated annealing for feature selection in medical field [J]. IEEE Access, 2022, 10: 51428-51446. |
11 | 付华,许桐,邵靖宇. 基于水波进化和动态莱维飞行的爬行动物搜索算法 [J]. 控制与决策, 2024, 39(1):59-68. |
FU H, XU T, SHAO J Y. Reptile search algorithm based on water wave evolution and dynamic Levy flight[J]. Control and Decision, 2024, 39(1): 59-68. | |
12 | HUANG L, WANG Y, GUO Y, et al. An improved reptile search algorithm based on Lévy flight and interactive crossover strategy to engineering application [J]. Mathematics, 2022, 10(13): No.2329. |
13 | WU D, WEN C, RAO H, et al. Modified reptile search algorithm with multi-hunting coordination strategy for global optimization problems [J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10090-10134. |
14 | EKINCI S, IZCI D. Enhanced reptile search algorithm with Lévy fight for vehicle cruise control system design [J]. Evolutionary Intelligence, 2023, 16(4):1339-1351. |
15 | 刘银涛,任超,王俊男,等. RSA-BP组合模型在GNSS高程拟合中的应用[J]. 测绘通报, 2023(9): 46-51. |
LIU Y T, REN C, WANG J N, et al. Application of RSA-BP combined model in GNSS height fitting [J]. Bulletin of Surveying and Mapping, 2023(9): 46-51. | |
16 | 李新华,崔东文. 基于WPD-RSA-ELM模型的水文时间序列多步预测[J]. 水利水电技术(中英文), 2022, 53(11): 69-77. |
LI X H, CUI D W. Multi-step prediction of hydrological time series based on WPD-RSA-ELM model [J]. Water Resources and Hydropower Engineering, 2022, 53(11): 69-77. | |
17 | RAO R V, SAVSANI V J, VAKHARIA D P. Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems [J]. Computer-Aided Design, 2011, 43(3): 303-315. |
18 | 李全耀,沈艳霞. 一种基于教与学的混合灰狼优化算法[J]. 控制与决策, 2022, 37(12): 3190-3196. |
LI Q Y, SHEN Y X. A hybrid gray wolf optimization algorithm based on the teaching-learning optimization [J]. Control and Decision, 2022, 37(12): 3190-3196. | |
19 | JIANG X, LI S. BAS: beetle antennae search algorithm for optimization problems [EB/OL]. (2017-10-30) [2022-12-02].. |
20 | 姚信威,王佐响,姚远,等. 融合改进天牛须和正余弦的双重搜索优化算法 [J]. 小型微型计算机系统, 2022, 43(8): 1644-1652. |
YAO X W, WANG Z X, YAO Y, et al. Dual search optimization algorithm based on improved beetle antennae search and sine cosine algorithm [J]. Journal of Chinese Computer Systems, 2022, 43(8): 1644-1652. | |
21 | 龙文,伍铁斌,唐明珠,等. 基于透镜成像学习策略的灰狼优化算法 [J]. 自动化学报, 2020, 46(10): 2148-2164. |
LONG W, WU T B, TANG M Z, et al. Grey wolf optimizer algorithm based on lens imaging learning strategy[J]. Acta Automatica Sinica, 2020, 46(10): 2148-2164. | |
22 | 廖列法,欧阳宗英. 基于二次插值的天牛须搜索算法 [J]. 计算机应用研究, 2021, 38(3):745-750. |
LIAO L F, OUYANG Z Y. Beetle antennae search based on quadratic interpolation [J]. Application Research of Computers, 2021, 38(3):745-750. | |
23 | SEYYEDABBASI A, ALIYEV R, KIANI F, et al. Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems [J]. Knowledge-Based Systems, 2021, 223: No.107044. |
24 | LI Y, ZHAO Y, LIU J. Dynamic sine cosine algorithm for large-scale global optimization problems [J]. Expert Systems with Applications, 2021, 177: No.114950. |
25 | 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. |
26 | KENNEDY J, EBERHART R. Particle swarm optimization [C]// Proceedings of the 1995 International Conference on Neural Networks — Volume 4. Piscataway: IEEE, 1995: 1942-1948. |
[1] | Heming JIA, Shanglong LI, Lizhen CHEN, Qingxin LIU, Di WU, Rong ZHENG. Remora optimization algorithm based on chaotic host switching mechanism [J]. Journal of Computer Applications, 2023, 43(6): 1759-1767. |
[2] | Hao GAO, Qingke ZHANG, Xianglong BU, Junqing LI, Huaxiang ZHANG. Teaching-learning-based optimization algorithm based on cooperative mutation and Lévy flight strategy and its application [J]. Journal of Computer Applications, 2023, 43(5): 1355-1364. |
[3] | TONG Nan, FU Qiang, ZHONG Caiming. Improved teaching-learning-based optimization algorithm based on self-learning mechanism [J]. Journal of Computer Applications, 2018, 38(2): 443-447. |
[4] | HUANG Xiangdong, XIA Shixiong, NIU Qiang, ZHAO Zhijun. Improved teaching-learning-based optimization algorithm based on K-means [J]. Journal of Computer Applications, 2015, 35(11): 3126-3129. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||