Chameleon Swarm Algorithm for Segmental Variation Learning of Population and S-type Weight

doi:10.16182/j.issn1004731x.joss.21-0968

Abstract

Abstract:

It is the best choice for intelligent algorithms to be applied to specific fields to explore strong searching ability, good reliability and stability.In this paper, aiming at the defects of chameleon swarm algorithm, such as unstable solution, low convergence accuracy and unbalanced search and development, a chameleon swarm algorithm (RMSCSA) based on population diversity segmental mutation learning and S-type weight is proposed. The refraction mirror learning strategy (RML) is introduced to make the chameleon more consistent with the observation in nature and enhance its diversity. The introduction of segmental variation of population diversity can keep the individuals with poor fitness and guide them to the optimal position. The introduction of S-type decreasing weight makes it further balance the global search and explore ability of the algorithm, and obtains the advantage of S-type decreasing weight factor through convergence analysis. The classical test function and CEC 2017 competition function are used for performance verification, and the results show that the three strategies have better optimization accuracy and efficiency for CSA. In order to compare the performance of different algorithms, the results of 30 independent runs are statistically analyzed by Wilcoxon rank-sum test, Friedman's test and Holm follow-up test. The analysis shows that the three strategies introduced have better optimization ability compared with CSA.

Key words: chameleon swarm algorithm(CSA), refraction mirror learning(RML), diversity variation, S-type decreasing weight, statistical analyzed

CLC Number:

TP301.6

Damin Zhang, Yi Wang, Linna Zhang. Chameleon Swarm Algorithm for Segmental Variation Learning of Population and S-type Weight[J]. Journal of System Simulation, 2023, 35(1): 11-26.

Figures/Tables 17

Table 1

Parameter description of prey search

参数名称	说明
$x i j t$	变色龙 $i$ 在 $j$ 维空间中，第 $t 次迭代$ 的位置
$P i j t$	变色龙 $i$ 在 $j$ 维空间经 $t 次迭代$ 的最佳位置
$G j$	变色龙在 $t 次迭代$ 的全局最优位置
$p 1, p 2$	用于控制算法开发能力的正系数
$r 1, r 2, r 3, r$	取值范围为(0, 1)的随机数
$P p$	变色龙的感知概率
$μ$	随 $t$ 控制的搜索能力参数
$s g n (r a n d - 0.5)$	控制旋转方向

Table 1

Fig. 1

Fig. 2

Fig. 3

Table 2

Main parameter Settings of each algorithm

算法	主要参数
CSA	$P p = 0.1, α = 3.5,$ $p 1 = 0.25,$ $p 2 = 1.5, c 1 = 1.75, c 2 = 1.75$
RCSA	$n 1 = 1.0 ~ 1.5, k = 1, A P = 0.35$
MCSA	$n = 50, N$ 为种群大小
SCSA	$g e n = 51, ω m a x = 0.95, ω m i n = 0.55$
SSA	--
WOA	$a m a x = 2, a m i n = 0$

Table 2

Table 3

Information of 14 classical test functions

函数名称	函数公式	特征	取值空间	最优取值
Sphere	$F 1 X = ∑ i = 1 n x i 2$	US	[-100, 100]	0
Schwefel 2.22	$F 2 X = ∑ i = 1 n \| x i \| + ∏ i = 1 n \| x i \|$	US	[-100, 100]	0
Schwefel 1.2	$F 3 X = ∑ i = 1 n (∑ j = 1 n x j) 2$	UN	[-100, 100]	0
Schwefel 2.21	$F 4 X = m a x 1 ≤ i ≤ n {\| x i \|}$	US	[-100, 100]	0
Rosenbrock	$F 5 X = ∑ i = 1 n 100 x i + 1 - x i 2) 2 + (1 - x i 2) 2]$	UN	[-30, 30]	0
Quartic	$F 6 X = ∑ i = 1 n i x i 4 + r a n d o m [0, 1)$	US	[-1.28, 1.28]	0
Rastrigin	$F 7 X = ∑ i = 1 n x i 2 - 10 c o s (2 π x i + 10$	MS	[-5.12, 5.12]	0
Ackley	$F 8 X = - 20 e x p - 0.2 ∑ i = 1 n x i 2 n - e x p ∑ i = 1 n c o s 2 π x i n + 20 + e$	MN	[-32, 32]	0
Griewank	$F 9 X = 1 4 000 ∑ i = 1 n x i 2 - ∏ i = 1 n c o s x i i + 1$	MN	[-600, 600]	0
Penalized 2	$F 10 X = 0.1 {s i n 2 3 π x 1 + ∑ i = 1 n - 1 (x i - 1) 2 [1 + s i n 2 3 π x i + 1]$ $+ (x n - 1) 2 + ∑ i = 1 n u (x i, 5,100, 4}$	MN	[-50, 50]	0
Foxholes	$F 11 X = [1500 + ∑ j = 1 25 1 j + ∑ j = 1 2 (x i - a i j) 6] - 1$	MN	[-65.53, 65.53]	$≈ 1$
Kowalik	$F 12 X = ∑ i = 1 11 [a i - x 1 (b i 2 + b i x 2) b i 2 + b i x 3 x 4] 2$	MN	[-5, 5]	3.075 $× 10 - 4$
Branin	$F 13 X = (x 2 - 5.1 4 π 2 x 12 + 5 π x 1 - 6) 2 + 10 1 - 1 8 π c o s x 1 + 10$	MN	$x 1$ ∈[5, 10] $x 2$ ∈[0, 15]	0.398
Hartman	$F 14 X = - ∑ i = 1 4 c i e x p [- ∑ j = 1 6 a i j (x j - p i j) 2]$	MS	[0,1]	-3.32

Table 3

Table 4

Comparison of test functions of different policies in different dimensions

函数	算法名	$d = 10$			$d = 30$			$d = 50$
函数	算法名	平均值	标准差	耗时/s	平均值	标准差	耗时/s	平均值	标准差	耗时/s
$F 1$	CSA	5.71×10^-10	1.44×10^-10	1.66	3.37×10^-7	1.31×10^-7	6.60	3.28×10^-6	5.32×10^-7	10.67
	RCSA	2.97×10^-90	1.57×10^-89	1.82	3.71×10^-86	1.45×10^-85	6.89	6.03×10^-89	3.28×10^-88	12.46
	MCSA	2.2×10^-163	6.4×10^-162	1.65	4.3×10^-117	9.9×10^-117	6.71	1.2×10^-116	6.6×10^-116	13.55
	SCSA	4.48×10^-78	2.37×10^-77	1.71	1.31×10^-34	4.56×10^-32	7.01	3.97×10^-21	3.38×10^-20	11.62
	RMSCSA	0	0	1.88	0	0	7.51	0	0	11.65
$F 2$	CSA	1.51×10^-6	6.84×10^-6	1.67	2.47	3.13	6.58	1.31×10²	8.40×10	10.54
	RCSA	4.78×10^-44	2.57×10^-43	1.81	2.75×10^-43	1.49×10^-42	6.61	5.84×10^-45	3.18×10^-44	11.28
	MCSA	5.13×10^-84	2.32×10^-83	2.10	2.37×10^-61	6.22×10^-61	7.56	2.23×10^-65	1.09×10^-64	11.25
	SCSA	2.35×10^-35	6.32×10^-33	1.71	6.19×10^-18	4.48×10^-17	6.84	2.36×10^-13	1.84×10^-12	11.22
	RMSCSA	1.3×10^-¹⁹⁷	3.2×10^-¹⁹⁵	2.15	1.1×10^-188	1.1×10^-187	6.77	2.12×10^-⁹⁹	7.9×10^-⁹⁹	11.32
$F 3$	CSA	3.92×10^-7	9.31×10^-7	1.77	7.88	9.53	7.00	4.39×10³	1.09×10³	11.02
	RCSA	8.64×10^-91	3.11×10^-90	2.03	2.23×10^-83	1.22×10^-82	6.95	2.95×10^-82	1.16×10^-81	10.99
	MCSA	5.7×10^-113	4.5×10^-112	1.88	1.14×10^-75	5.39×10^-75	7.51	4.55×10^-77	2.49×10^-76	11.43
	SCSA	2.03×10^-79	3.72×10^-78	1.81	9.42×10^-31	1.69×10^-29	7.22	3.38×10^-37	2.94×10^-35	11.33
	RMSCSA	0	0	2.02	0	0	7.35	3.0×10^-²⁸¹	9.1×10^-²⁸¹	11.19
$F 4$	CSA	1.14×10^-5	7.20×10^-6	1.67	3.50×10^-3	6.50×10^-3	6.69	2.25×10^-2	2.14×10^-2	10.50
	RCSA	4.75×10^-45	2.54×10^-44	1.83	5.65×10^-43	3.09×10^-42	6.89	5.78×10^-45	2.28×10^-44	10.79
	MCSA	2.41×10^-80	8.67×10^-80	2.16	2.33×10^-37	1.27×10^-36	8.12	5.08×10^-51	5.77×10^-50	10.20
	SCSA	3.79×10^-44	1.39×10^-44	1.72	2.61×10^-17	2.63×10^-18	6.88	7.59×10^-15	3.36×10^-16	9.95
	RMSCSA	4.5×10^-¹⁷⁸	3.6×10^-¹⁷⁸	2.11	6.3×10^-¹⁸²	3.3×10^-¹⁸¹	6.91	5.7×10^-¹⁷⁸	2.9×10^-¹⁷⁶	10.07
$F 5$	CSA	1.95	4.68	1.71	2.46	2.59	6.69	2.51×10²	3.81×10²	10.59
	RCSA	4.06×10^-7	7.73×10^-6	2.01	1.81×10^-5	4.04×10^-5	7.03	5.29×10^-5	1.03×10^-4	11.83
	MCSA	8. 2×10^-4	7.54×10^-3	2.06	2.89×10^-2	1.70×10^-2	7.24	4.0×10^-7	3.31×10^-⁵	11.56
	SCSA	1.11	7.51×10^-1	1.72	2.86	2.14×10^-1	7.89	4.87	4.89	11.21
	RMSCSA	1.40×10^-⁹	1.62×10^-⁹	2.39	1.92×10^-⁹	3.21×10^-⁷	7.21	2.63×10^-⁷	8.61×10^-5	11.31
$F 6$	CSA	3.55	3.02	1.68	1.26×10²	2.21×10	6.62	5.45×10²	5.36×10	10.52
	RCSA	3.26×10^-5	4.31×10^-5	1.96	2.49×10^-5	2.28×10^-5	6.71	2.65×10^-5	3.95×10^-5	12.51
	MCSA	4.17×10^-5	2.27×10^-5	2.21	2.53×10^-5	2.14×10^-⁶	6.55	1.38×10^-7	1.03×10^-6	11.85
	SCSA	1.21×10^-2	4.10×10^-1	1.95	2.98×10^-2	5.58×10^-1	6.55	5.36×10^-2	1.17×10^-2	11.29
	RMSCSA	3.61×10^-⁷	1.25×10^-⁷	2.44	2.62×10^-⁶	1.77×10^-5	6.52	1.37×10^-⁷	1.00×10^-⁶	11.18
$F 7$	CSA	9.44×10^-1	1.15	1.67	1.39×10	2.75×10	6.05	3.11×10²	1.94×10	10.63
	RCSA	0	0	1.96	0	0	6.56	0	0	11.02
	MCSA	0	0	2.12	0	0	6.71	0	0	11.43
	SCSA	3.85×10^-98	2.95×10^-97	1.81	2.55×10^-97	1.78×10^-96	6.96	0	0	11.41
	RMSCSA	0	0	2.09	0	0	6.31	0	0	12.69
$F 8$	CSA	2.92×10^-5	2.32×10^-6	1.71	1.02	1.54	6.03	1.91	1.02×10^-1	10.64
	RCSA	8.88×10^-¹⁶	8.88×10^-¹⁶	2.00	8.88×10^-¹⁶	8.88×10^-¹⁶	6.21	8.88×10^-¹⁶	8.88×10^-¹⁶	12.08
	MCSA	8.88×10^-¹⁶	8.88×10^-¹⁶	2.05	8.88×10^-¹⁶	8.88×10^-¹⁶	6.05	8.88×10^-¹⁶	8.88×10^-¹⁶	10.66
	SCSA	8.88×10^-¹⁶	8.88×10^-¹⁶	1.77	2.04×10^-12	2.47×10^-12	6.22	7.26×10^-13	5.47×10^-12	11.03
	RMSCSA	8.88×10^-¹⁶	8.88×10^-¹⁶	2.07	8.88×10^-¹⁶	8.88×10^-¹⁶	6.55	8.88×10^-¹⁶	8.88×10^-¹⁶	10.68
$F 9$	CSA	5.65×10^-1	4.37×10^-1	1.72	1.33×10^-4	3.12×10^-4	6.15	2.79×10^-3	6.19×10^-3	10.63
	RCSA	0	0	1.89	0	0	6.04	0	0	11.00
	MCSA	0	1.6×10^-311	2.00	4.05×10^-211	3.87×10^-209	6.29	1.4×10^-266	9.5×10^-264	11.52
	SCSA	0	0	1.91	0	0	6.22	0	0	10.99
	RMSCSA	0	0	2.00	0	0	6.49	0	0	10.71
$F 10$	CSA	1.90×10^-11	8.75×10^-11	1.97	9.53×10^-8	3.60×10^-8	6.56	3.70×10^-4	2.01×10^-3	11.26
	RCSA	1.51×10^-8	1.44×10^-7	2.10	1.12×10^-¹⁰	1.74×10^-8	7.06	6.95×10^-8	1.17×10^-8	12.28
	MCSA	4.21×10^-23	2.20×10^-20	2.30	2.99×10^-8	5.83×10^-8	6.89	4.99×10^-8	1.66×10^-9	12.09
	SCSA	3.85×10^-10	7.06×10^-8	2.03	2.56×10^-8	1.31×10^-8	6.66	4.27×10^-5	4.98×10^-4	11.76
	RMSCSA	3.51×10^-²⁶	1.18×10^-²⁴	2.51	1.57×10^-10	1.18×10^-⁸	7.05	7.55×10^-¹³	1.43×10^-¹²	11.81

Table 4

Table 5

Optimization comparison in fixed dimensions

函数	算法名称	平均值	标准差	耗时/s	维度
$F 11$	CSA	9.979×10^-1	2.23×10^-4	1.28	2
	RCSA	9.980×10^-1	5.90×10^-11	1.45	2
	MCSA	9.981×10^-1	6.20×10^-2	1.38	2
	SCSA	9.980×10^-1	2.55×10^-8	1.45	2
	RMSCSA	9.982×10^-¹	4.38×10^-¹⁶	1.55	2
$F 12$	CSA	5.110×10^-4	4.99×10^-3	1.27	4
	RCSA	3.117×10^-4	5.53×10^-3	1.36	4
	MCSA	3.083×10^-4	6.58×10^-3	1.92	4
	SCSA	3.100×10^-4	2.87×10^-4	1.37	4
	RMSCSA	3.079×10^-⁴	2.38×10^-⁶	1.81	4
$F 13$	CSA	3.974×10^-1	1.55×10^-2	0.22	2
	RCSA	3.978×10^-¹	1.20	0.21	2
	MCSA	3.986×10^-1	8.21×10^-2	0.29	2
	SCSA	3.978×10^-¹	3.62×10^-5	0.24	2
	RMSCSA	3.978×10^-¹	2.98×10^-6	0.36	2
$F 14$	CSA	-3.322	3.01×10^-2	1.37	6
	RCSA	-3.324	5.80×10^-⁹	1.54	6
	MCSA	-3.320	1.99×10^-1	1.79	6
	SCSA	-3.322	6.75×10^-2	1.97	6
	RMSCSA	-3.320	7.25×10^-8	1.94	6

Table 5

Fig. 4

Table 6

Table 7

Performance comparison of various algorithms under CEC 2017 test

函数名	评价指标	RMSCSA	CSA	RCSA	MCSA	SCSA	SSA	WOA
CEC04	平均值	4.705 2×10²	2.120 8×10³	7.353 7×10²	4.724 4×10²	7.489 6×10²	6.425 5×10²	6.890 7×10²
	标准差	7.298×10^-4	1.628×10	1.587×10^-4	1.698×10	1.093×10²	2.055×10	6.773×10
	耗时/s	4.08	6.32	2.69	3.13	2.01	1.25	0.83
CEC05	平均值	5.209 7×10²	1.006 5×10	6.885 6×10²	7.511 1×10²	5.385 6×10²	6.970 5×10²	8.454 6×10²
	标准差	1.625×10^-⁵	2.013×10	1.526×10	2.296×10	2.160×10	3.852×10	5.344×10
	耗时/s	3.88	6.44	2.67	3.14	2.01	1.61	0.75
CEC08	平均值	9.235 5×10²	1.237 0×10³	1.237 0×10³	1.005 9×10³	1.037 9×10³	9.958 0×10²	1.129 4×10³
	标准差	2.684×10^-⁵	6.200×10^-4	1.954×10^-3	2.192×10	1.573×10	4.910 2×10	6.337×10
	耗时/s	3.91	2.91	2.71	4.19	2.26	1.53	0.88
CEC16	平均值	3.258 6×10³	6.706 5×10³	3.749 8×10³	4.503 9×10³	3.450 6×10³	1.855 9×10⁴	4.921 2×10³
	标准差	3.105×10²	2.176×10³	2.112×10³	3.051×10²	3.220×10²	3.771 5×10³	4.472×10²
	耗时/s	3.96	3.73	2.66	4.26	2.84	1.32	0.77
CEC17	平均值	2.356 9×10³	2.499 6×10⁴	4.446 5×10³	3.004 0×10³	2.282 3×10³	2.966 2×10³	2.986 8×10³
	标准差	3.144×10	3.334×10⁴	2.781×10²	8.944×10	1.395×10²	2.836×10²	2.991×10²
	耗时/s	4.11	3.96	2.78	4.37	2.93	1.55	0.56
CEC20	平均值	2.630 2×10³	3.646 7×10³	3.410 6×10³	3.012 0×10³	2.758 8×10³	2.795 5×10³	3.127 9×10³
	标准差	1.361×10	1.592×10²	9.995×10	7.638×10	2.135×10²	5.541×10³	1.823×10²
	耗时/s	4.14	3.85	2.81	4.40	2.90	1.97	0.57
CEC21	平均值	2.610 0×10³	2.926 4×10³	2.849 0×10³	2.604 0×10³	2.526 2×10³	2.917 2×10³	2.831 9×10³
	标准差	6.886	5.267×10	3.944×10	2.114×10	1.941×10	5.541 0×10²	3.544×10
	耗时/s	4.26	3.82	3.23	4.38	3.07	1.97	0.42
CEC23	平均值	3.211 7×10³	4.012 8×10³	3.781 0×10³	3.257 7×10³	3.893 0×10³	3.698 8×10³	3.987 5×10³
	标准差	4.617×10	2.371×10²	1.298×10²	4.601×10	2.389×10²	4.629×10³	1.256×10²
	耗时/s	2.95	3.76	3.16	3.45	3.36	1.45	0.95
CEC27	平均值	3.060 0×10³	5.167 4×10³	3.508 0×10³	5.611 0×10³	3.302 8×10³	4.305 7×10³	4.558 1×10³
	标准差	2.015×10	3.219×10³	1.143×10²	5.111×10²	3.083×10²	2.609×10	8.075×10
	耗时/s	4.06	4.13	4.46	3.17	4.35	1.30	0.56
CEC29	平均值	3.652 9×10³	2.614 1×10⁴	8.485 1×10³	8.485 8×10³	4.419 8×10³	4.202 8×10³	5.034 0×10³
	标准差	3.505	2.067×10⁴	5.081×10	5.081×10³	2.094×10²	2.024×10²	5.363×10²
	耗时/s	5.70	4.09	3.07	3.07	4.12	1.28	0.42

Table 7

Table 8

Average comparison with other intelligent algorithms

算法	$F 1$	$F 2$	$F 3$	$F 4$	$F 5$	$F 6$	$F 7$	$F 8$	$F 9$	$F 10$
RMSCSA	0	2.21×10^-179	0	2.77×10^-¹⁸¹	1.92×10^-⁵	2.39×10^-⁶	0	8.88×10^-16	0	5.52×10^-9
MPEA^[20]	2.70×10^-11	2.77×10^-91	1.52×10^-20	1.04×10^-68	6.74×10^-12	2.35×10^-1	6.38×10^-6	2.84×10^-13	4.3×10^-7	2.27×10^-5
SSA^[21]	0	2.12×10^-259	0	5.72×10^-177	9.24×10^-4	1.34×10^-4	0	8.88×10^-16	0	7.17×10^-12
HCMOIWO^[22]	3.17×10^-55	3.66×10^-51	5.41×10^-10	9.34×10^-13	5.28×10^-1	8.41×10^-3	4.62×10^-1	1.86×10^-11	3.29×10^-2	1.35×10^-3
HCUGOA^[23]	0	0	0	3.23×10^-176	1.78×10^-3	4.96×10^-5	0	8.88×10^-16	0
GOBL-RNADE^[24]	1.63×10^-240	1.80×10^-126	7.15×10^-276	6.26×10^-115	5.94×10^-2	7.70×10^-2	0	0	0	2.28×10^-¹²
WOAEP^[25]	1.59×10^-96	1.27×10^-48	3.97×10^-96	1.46×10^-49	2.89×10	2.67×10^-4	0	8.88×10^-16	0	2.99
FO-FPA^[26]	1.51×10^-184	5.04×10^-93	1.23×10^-183	9.97×10^-93	2.89×10	1.13×10^-4	0	8.88×10^-16	0	9.65×10^-6
SHDNMRA^[18]	1.80×10^-60	1.80×10^-25	5.70×10^-61	8.20×10^-24	8.00×10^-2	6.40×10^-4	8.20×10^-10	8.20×10^-10	4.60×10^-17	3.02×10^-7
SCCSA^[27]	9.22×10^-69	8.24×10^-41	4.31×10^-13	2.15×10^-17	5.90	1.33×10^-3	5.46	8.88×10^-16	1.34×10^-2	3.00

Table 8

Table 9

Rank sum test values of classical test functions

函数	$p 1$	$p 2$	$p 3$	$p 4$
$F 1$	3.00×10^-11	3.01×10^-11	3.00×10^-11	3.00×10^-11
$F 2$	3.02×10^-11	3.02×10^-11	3.02×10^-11	3.02×10^-11
$F 3$	2.99×10^-11	2.99×10^-11	2.99×10^-11	2.99×10^-11
$F 4$	3.02×10^-11	3.00×10^-11	3.02×10^-11	3.00×10^-11
$F 5$	2.14×10^-9	4.04×10^-11	2.06×10^-10	1.16×10^-10
$F 6$	0.282	0.365	5.66×10^-11	3.02×10^-11
$F 7$	NA	NA	NA	1.21×10^-12
$F 8$	5.28×10^-5	3.99×10^-3	NA	1.21×10^-12
$F 9$	NA	3.68×10^-3	NA	1.21×10^-12
$F 10$	6.84×10^-7	3.02×10^-11	1.17×10^-9	1.21×10^-12
$F 11$	0.4247	3.02×10^-11	1.17×10^-9	1.21×10^-12
$F 12$	3.02×10^-11	1.49×10^-4	8.15×10^-11	8.48×10^-9
$F 13$	0.595	1.23×10^-9	2.78×10^-11	1.09×10^-12
$F 14$	7.70×10^-8	5.57×10^-10	3.02×10^-11	3.02×10^-11
+/-/=	9/3/2	12/1/1	11/0/3	14/0/0

Table 9

Table 10

Average ranking of algorithms under classical test functions

算法	$R 1$	$R 2$	$R 3$	$R ¯$	排名
RMSCSA	2.35	2.2	2.05	2.2	1
RCSA	4.7	2.2	2.45	3.116 666 7	3
MCSA	2.35	2.5	2.25	2.366 666 7	2
SCSA	2.85	3.3	3.35	3.166 666 7	4
CSA	2.75	4.8	4.9	4.15	5

Table 10

Table 11

Average ranking of CEC 2017 functions

算法	$R$	排名
RMSCSA	1.8	1
CSA	6.75	7
RCSA	4.55	5
MCSA	3.9	4
SCSA	2.5	2
SSA	3.7	3
WOA	4.8	6

Table 11

Table 12

Subsequent verification results of classical function Holm

$i$	RMSCSA vs.	$z ¯$ 值	$p ¯$ 值	$α / i$	是否拒绝
4	CSA	3.677 0	2.36×10^-4	0.0125	是
3	SCSA	2.877 5	4.01×10^-3	0.016 666 7	是
2	RCSA	2.388 7	1.69×10^-2	0.025	是
1	MCSA	0.893 1	0.3725	0.05	否

Table 12

Table 13

CEC 2017 Holm subsequent verification results

$i$	RMSCSA vs.	$z$ 值	$p$ 值	$α / i$	是否拒绝
6	CSA	5.124	2.99×10^-7	0.008 333	是
5	WOA	3.105	1.903×10^-3	0.01	是
4	RCSA	2.847	4.413×10^-3	0.025	是
3	MCSA	2.617 4	8.86×10^-3	0.016 67	是
2	SSA	2.359 4	1.83×10^-2	0.025	是
1	SCSA	0.725	0.468 452	0.05	否

Table 13

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