Strategy Self-adaptive Differential Evolution Algorithm Based on State Estimation Feedback
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摘要: 借鉴闭环控制思想, 提出基于状态估计反馈的策略自适应差分进化(Differential evolution, DE)算法, 通过设计状态评价因子自适应判定种群个体所处于的阶段, 实现变异策略的反馈调节, 达到平衡算法全局探测和局部搜索的目的.首先, 基于抽象凸理论对种群个体建立进化状态估计模型, 提取下界估计信息并结合进化知识设计状态评价因子, 以判定当前种群的进化状态; 其次, 利用状态评价因子的反馈信息, 实现不同进化状态下策略的自适应调整以指导种群进化, 达到提高算法搜索效率的目的.另外, 20个典型测试函数与CEC2013测试集的实验结果表明, 所提算法在计算代价、收敛速度和解的质量方面优于主流改进差分进化算法和非差分进化算法.Abstract: Inspired by the idea of closed-loop control, a strategy self-adaptive differential evolution (DE) algorithm based on state estimation feedback is proposed, the stage of individual can be self-adaptively determined by designing the state judgment factor, and achieve the feedback adjustment of mutation strategies. Consequently, the algorithm can get a trade-off between the exploration and exploitation. Firstly, the estimation model of evolution state is established based on abstract convex theory, from which the underestimation information is extracted combining with the evolutionary information to design the state judgment factor, so that the evolution state of the current population is estimated. Secondly, according to the feedback information of the state judgment factor, the strategy in different evolution state is adaptively selected to guide the evolution of the population. Therefore, the searching efficiency of the algorithm can be improved. Additionally, experimental results of 20 benchmark functions and CEC2013 test set show that the proposed algorithm is superior to the main-stream differential evolution variants and non-differential evolution algorithms mentioned in this paper in terms of computational cost, convergence speed, and solution quality.
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Key words:
- Differential evolution (DE) /
- state estimation /
- feedback /
- global optimization /
- abstract convex
1) 本文责任编委 刘艳军 -
图 8 DELU、SHADE、EPSDE、CoDE、SEFDE的平均收敛速度曲线图
Fig. 8 The curve graph of mean convergence speed on DELU、SHADE、EPSDE、CoDE、SEFDE
表 1 标准测试函数的参数
Table 1 The parameters of benchmark functions
函数名 表达式 维数($N$) 取值范围 全局最小值 Sphere $f_1({\bm x})=\sum\limits_{i=1}^{N}x_i^2$ 30, 50 $(-100, 100)^N$ 0 SumSquares $f_2({\bm x})=\sum\limits_{i=1}^{N}ix_i^2$ 30, 50 $(-10, 10)^N$ 0 Schwefel 2.22 $f_3({\bm x})=\sum\limits_{i=1}^{N}\left|x_i\right|+\prod\limits_{i=1}^{N}\left|x_i\right|$ 30, 50 $(-10, 10)^N$ 0 Exponential $f_4({\bm x})=-\exp(-0.5$$\sum\limits_{i=1}^{N}$$x_i^2)$ 30, 50 $(-1, 1)^N$ $-1$ Tablet $f_5({\bm x})=10^6x_1^2+\sum\limits_{i=2}^{N}x_i^2$ 30, 50 $(-100, 100)^N$ 0 Step $f_6({\bm x})=\sum\limits_{i=1}^{N}\left\lfloor x_i+0.5\right\rfloor^2$ 30, 50 $(-100, 100)^N$ 0 Zakharov $f_7({\bm x})=\sum\limits_{i=1}^{N}x_i^2+\sum\limits_{i=1}^{N}(0.5ix_i)^2+\sum\limits_{i=1}^{N}(0.5ix_i)^4$ 30, 50 $(-5, 10)^N$ 0 Rosenbrock $f_8({\bm x})=\sum\limits_{i=1}^{N-1}[100(x_{i+1}-x_i^2)^2+(x_i-1)^2]$ 30, 50 $(-30, 30)^N$ 0 Griewank $f_9({\bm x})=1+\frac{1}{4000}\sum\limits_{i=1}^{N}x_i^2-\prod\limits_{i=1}^{N}\cos(\frac {x_i}{\sqrt{i}})$ 30, 50 $(-600, 600)^N$ 0 Schaffer 2 $f_{10}({\bm x})=\sum\limits_{i=1}^{N-1}(x_i^2+x_{i+1}^2)^{0.25}(\sin^2(50(x_i^2+x_{i+1}^2)^{0.1})+1)$ 30, 50 $(-100, 100)^N$ 0 Schwefel 2.26 $f_{11}({\bm x})=-\sum\limits_{i=1}^{N}x_i\sin(\sqrt{\left\|x_i\right\|})$ 30, 50 $(-500, 500)^N$ $-12 569.18$ Himmelblau $f_{12}({\bm x})=N^{-1}\sum\limits_{i=1}^{N}(x_i^4-16x_i^2+5x_i)$ 30, 50 $(-5, 5)^N$ -78.3323 Levy and Montalvo 1 $f_{13}({\bm x})=\frac{\pi}{N}(10\sin^2(\pi y_1)+\sum\limits_{i=1}^{N-1}(y_i-1)^{2} [1+\\ 10\sin^2(\pi y_{i+1})]+(y_N-1)^2)$, $y_i=1+\frac{1}{4}(x_i+1)$ 30, 50 $(-10, 10)^N$ 0 Levy and Montalvo 2 $f_{14}({\bm x})=0.1(\sin^2(3\pi x_i)+\sum\limits_{i=1}^{N-1}(x_i-1)^{2} [1+\sin^2(3\pi x_{i+1})]+\\ (x_N-1)^2[1+\sin^2(2\pi x_N)])$ 30, 50 $(-5, 5)^N$ 0 Ackley $f_{15}({\bm x})=-20\exp(-0.02\sqrt{N^{-1}\sum\limits_{i=1}^{N}x_i^2}) -\exp(N^{-1}$ 30, 50 $(-30, 30)^N$ 0 $\sum\limits_{i=1}^{N}\cos(2\pi x_i))+20+e$ Rastrigin $f_{16}({\bm x})=10N+\sum\limits_{i=1}^{N}(x_i^2-10\cos(2\pi x_i))$ 30, 50 $(-5, 5)^N$ 0 Penalized 1 $f_{17}(x)=\frac{\pi}{N}\{\sum\nolimits_{i=1}^{N-1}(y_i-1)^2[1+ \sin(\pi y_{i+1})]+(y_N-1)^2+$ $(10\sin^2(\pi y_1))\}+\sum\nolimits_{i=1}^Nu(x_i, 10, 100, 4)$, $y_i=1+\frac{x_i+1}{4}$ $u(x_i, a, k, m) = \left\{ \begin{array}{ll} k(x_i-a)^m, ~~~~ x_i> a \\ 0, ~~~~-a\leq x_i\leq a \\ k(-x_i-a)^m, ~~~~x_i < -a \end{array} \right.$ 30, 50 $(-50, 50)^N$ 0 Penalized 2 $f_{18}(x)=0.1\{\sin^2(3\pi x_1)+\sum\nolimits_{i=1}^{N-1} (x_i-1)^2[1+\sin^2(3\pi x_{i+1})]+$ $(x_N-1)^2[1+\sin^2(2\pi x_N)]\}+\sum\nolimits_{i=1}^Nu(x_i, 5, 100, 4)$ 30, 50 $-(50, 50)^N$ 0 Neumaier $f_{19}({\bm x})=\sum\limits_{i=1}^{N}(x_i-1)^2- \sum\limits_{i=2}^{N}x_ix_{i-1}+\frac{N(N+4)(N-1)}{6}$ 30, 50 $(-900, 900)^N$ 0 Alpine $f_{20}({\bm x})=\sum\limits_{i=1}^{N}\left|x_i\sin x_i+0.1x_i\right|$ 30, 50 $(-10, 10)^N$ 0 
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表 2 SEFDE中参数$K$设置下的平均函数评价次数和成功率
Table 2 Average numbers of function evaluations and success rates of parameter settings $K$ in SEFDE
Fun $N$ $K=2$ $K=3$ $K=4$ $K=5$ $K=6$ FEs SR FEs SR FEs SR FEs SR FEs SR $f_{1}$ 30 1.51E+04 1.00 1.19E+04 1.00 1.26E+04 1.00 1.21E+04 1.00 1.18E+04 1.00 $f_{2}$ 30 1.17E+04 1.00 1.14E+04 1.00 1.19E+04 1.00 1.06E+04 1.00 1.14E+04 1.00 $f_{3}$ 30 1.85E+04 1.00 1.76E+04 1.00 1.73E+04 1.00 1.75E+04 1.00 1.83E+04 1.00 $f_{4}$ 30 7.88E+03 1.00 7.89E+03 1.00 7.85E+03 1.00 7.83E+03 1.00 7.87E+03 1.00 $f_{5}$ 30 2.94E+04 1.00 2.97E+04 1.00 2.93E+04 1.00 2.91E+04 1.00 2.90E+04 1.00 $f_{6}$ 30 8.94E+03 1.00 8.61E+03 1.00 8.56E+03 1.00 8.28E+03 1.00 8.45E+03 1.00 $f_{7}$ 30 1.46E+05 1.00 1.45E+05 1.00 1.46E+05 1.00 1.46E+05 1.00 1.44E+05 1.00 $f_{8}$ 30 1.24E+05 1.00 1.20E+05 1.00 1.21E+05 1.00 1.22E+05 1.00 1.21E+05 1.00 $f_{9}$ 30 1.28E+04 1.00 1.26E+04 1.00 1.25E+04 1.00 1.27E+04 1.00 1.24E+04 1.00 $f_{10}$ 30 1.00E+05 1.00 1.09E+05 1.00 1.10E+05 1.00 1.07E+05 1.00 1.09E+05 1.00 $f_{11}$ 30 4.43E+04 1.00 4.34E+04 1.00 4.57E+04 1.00 4.40E+04 1.00 4.48E+04 1.00 $f_{12}$ 30 1.24E+04 1.00 1.71E+04 1.00 1.74E+04 1.00 1.64E+04 1.00 1.70E+04 1.00 $f_{13}$ 30 1.26E+04 1.00 1.24E+04 1.00 1.26E+04 1.00 1.27E+04 1.00 1.26E+04 1.00 $f_{14}$ 30 1.18E+04 1.00 1.19E+04 1.00 1.20E+04 1.00 1.22E+04 1.00 1.19E+04 1.00 $f_{15}$ 30 2.02E+04 1.00 1.95E+04 1.00 2.01E+04 1.00 2.02E+04 1.00 1.98E+04 1.00 $f_{16}$ 30 5.60E+04 1.00 5.44E+04 1.00 5.62E+04 1.00 5.34E+04 1.00 5.66E+04 1.00 $f_{17}$ 30 1.90E+04 1.00 1.96E+04 1.00 1.91E+04 1.00 1.92E+04 1.00 1.98E+04 1.00 $f_{18}$ 30 2.32E+04 1.00 2.29E+04 1.00 2.32E+04 1.00 2.30E+04 1.00 2.29E+04 1.00 $f_{19}$ 30 1.72E+05 1.00 1.70E+05 1.00 1.67E+05 1.00 1.70E+05 1.00 1.68E+05 1.00 $f_{20}$ 30 3.62E+04 1.00 3.75E+04 1.00 3.13E+04 1.00 3.48E+04 1.00 3.47E+04 1.00 AVE 4.41E+04 1.000 4.42E+04 1.000 4.41E+04 1.000 4.40E+04 1.000 4.41E+04 1.000
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表 3 函数评价次数和成功率对比数据
Table 3 Compared data on function evaluations and success rates
Fun $N$ DELU SHADE EPSDE CoDE SEFDE FEs SR FEs SR FEs SR FEs SR FEs SR $f_{1}$ 30 1.22E+04 1.00 2.35E+04 1.00 1.67E+04 1.00 4.02E+04 1.00 ${\bf1.21E+04}$ 1.00 $f_{2}$ 30 1.07E+04 1.00 2.16E+04 1.00 1.51E+04 1.00 3.63E+04 1.00 ${\bf1.06E+04}$ 1.00 $f_{3}$ 30 3.15E+04 1.00 3.42E+04 1.00 2.32E+04 1.00 5.64E+04 1.00 ${\bf1.75E+04}$ 1.00 $f_{4}$ 30 7.98E+03 1.00 1.67E+04 1.00 9.36E+03 1.00 2.24E+04 1.00 ${\bf7.83E+03}$ 1.00 $f_{5}$ 30 2.55E+04 1.00 2.67E+04 1.00 ${\bf1.83E+04}$ 1.00 4.13E+04 1.00 2.91E+04 1.00 $f_{6}$ 30 1.16E+04 1.00 1.19E+04 1.00 ${\bf8.33E+03}$ 1.00 2.01E+04 1.00 ${\bf8.28E+03}$ 1.00 $f_{7}$ 30 7.71E+04 1.00 ${\bf5.98E+04}$ 1.00 1.33E+05 1.00 7.80E+04 1.00 1.46E+05 1.00 $f_{8}$ 30 ${\bf7.09E+04}$ 1.00 1.09E+05 1.00 1.16E+05 0.87 2.37E+05 1.00 1.22E+05 1.00 $f_{9}$ 30 1.54E+04 1.00 2.56E+04 1.00 1.76E+04 0.83 4.39E+04 1.00 ${\bf1.27E+04}$ 1.00 $f_{10}$ 30 ${\bf9.78E+04}$ 1.00 1.08E+05 0.97 1.13E+05 1.00 1.73E+05 1.00 1.07E+05 1.00 $f_{11}$ 30 ${\bf1.34E+04}$ 1.00 9.67E+04 1.00 3.71E+04 1.00 6.00E+04 1.00 4.40E+04 1.00 $f_{12}$ 30 1.98E+04 1.00 3.35E+04 0.97 2.33E+04 1.00 3.56E+04 1.00 ${\bf1.64E+04}$ 1.00 $f_{13}$ 30 ${\bf1.05E+04}$ 1.00 1.67E+04 1.00 1.30E+04 1.00 2.63E+04 1.00 1.27E+04 1.00 $f_{14}$ 30 ${\bf8.71E+03}$ 1.00 1.71E+04 1.00 1.15E+04 1.00 2.70E+04 1.00 1.22E+04 1.00 $f_{15}$ 30 2.65E+04 1.00 3.21E+04 1.00 2.32E+04 1.00 5.68E+04 1.00 ${\bf2.02E+04}$ 1.00 $f_{16}$ 30 5.94E+04 1.00 1.24E+05 1.00 6.40E+04 1.00 1.30E+05 0.97 ${\bf5.34E+04}$ 1.00 $f_{17}$ 30 ${\bf9.17E+03}$ 1.00 1.92E+04 1.00 1.29E+04 1.00 3.03E+04 1.00 1.92E+04 1.00 $f_{18}$ 30 1.52E+04 1.00 ${\bf1.50E+04}$ 1.00 1.59E+04 0.97 3.54E+04 1.00 2.30E+04 1.00 $f_{19}$ 30 1.68E+05 0.97 ${\bf1.09E+05}$ 1.00 NA 0.00 2.69E+05 1.00 1.70E+05 1.00 $f_{20}$ 30 NA 0.00 NA 0.00 1.00E+05 1.00 NA 0.00 ${\bf3.48E+04}$ 1.00 AVE 4.96E+04 0.95 6.00E+04 0.947 5.36E+04 0.934 8.59E+04 0.949 $\textbf{4.40E+04}$ $\textbf{1.000}$
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表 4 DELU、SHADE、EPSDE、CoDE、SEFDE 对30维测试函数的优化结果, 平均值(标准差)
Table 4 Compared data of 30 D optimization results on DELU、SHADE、EPSDE、CoDE、SEFDE, Mean (Std)
Fun $N$ DELU SHADE EPSDE CoDE SEFDE Mean error (Std Dev) Mean error (Std Dev) Mean error (Std Dev) Mean error (Std Dev) Mean error (Std Dev) $f_{1}$ 30 9.25E-19(2.99E-36)$^+$ 2.87E-19(2.31E-19)$^+$ 3.87E-31(9.46E-31)$^+$ 2.16E-10(1.73E-10)$^+$ ${\bf4.01E-38}({\bf5.05E-38})$ $f_{2}$ 30 2.56E-32(4.00E-32)$^+$ 4.40E-20(3.25E-20)$^+$ 5.52E-31(1.94E-30)$^+$ 2.83E-11(2.61E-11)$^+$ ${\bf5.83E-42}({\bf9.48E-42})$ $f_{3}$ 30 1.46E-11(1.23E-11)$^+$ 4.98E-10(2.39E-10)$^+$ 1.29E-16(2.15E-16)$^+$ 3.86E-06(1.32E-06)$^+$ ${\bf2.03E-23}({\bf1.62E-23})$ $f_{4}$ 30 ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ 4.07E-17(5.44E-17)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ 1.30E-14(1.13E-14)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$ $f_{5}$ 30 1.65E-20(4.70E-20)$^-$ 1.97E-18(2.27E-18)$^-$ ${\bf8.85E-30}({\bf1.75E-29})$$^-$ 4.04E-10(3.04E-10)$^+$ 1.06E-15(3.87E-16) $f_{6}$ 30 ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$ $f_{7}$ 30 ${\bf1.82E-04}({\bf3.39E-04})$$^-$ 3.57E-04(1.00E-03)$^-$ 1.42E+01(2.25E+01)$^-$ 5.69E-04(7.53E-04)$^-$ 3.59E+01(1.12E+01) $f_{8}$ 30 ${\bf1.28E-04}({\bf1.36E-04})$$^-$ 1.81E+01(1.11E+00)$^-$ 8.61E+00(2.09E+00)$^-$ 1.97E+01(5.80E-01)$^-$ 2.54E+01(8.26E-01) $f_{9}$ 30 ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ 2.47E-04(1.35E-03)$^+$ 6.57E-04(2.50E-03)$^+$ 2.47E-07(7.34E-07)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$ $f_{10}$ 30 ${\bf2.03E-02}({\bf6.76E-03})$$^-$ 3.56E-01(5.96E-02)$^+$ 2.35E-01(1.31E-01)$^+$ 1.97E+00(4.24E-01)$^+$ 1.84E-01(7.45E-02) $f_{11}$ 30 ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ 1.75E+02(5.85E+01)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ 1.66E-02(3.13E-02)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$ $f_{12}$ 30 ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ 3.14E-05(1.02E-07)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$ $f_{13}$ 30 2.19E-30(2.11E-30)$^+$ 5.53E-21(5.00E-21)$^+$ 2.91E-29(1.14E-28)$^+$ 1.80E-13(3.36E-13)$^+$ ${\bf1.57E-32}({\bf0.00E+00})$ $f_{14}$ 30 1.36E-32(3.13E-34)$^+$ 4.07E-21(6.03E-21)$^+$ 1.75E-32(9.04E-33)$^+$ 1.31E-13(1.51E-13)$^+$ ${\bf1.35E-32}({\bf0.00E+00})$ $f_{15}$ 30 2.42E-10(1.35E-10)$^+$ 1.19E-10(6.88E-11)$^+$ 6.04E-15(1.66E-15)$^+$ 3.75E-06(1.88E-06)$^+$ ${\bf4.00E-15}({\bf1.02E-15})$ $f_{16}$ 30 1.99E-06(2.50E-06)$^+$ 2.30E+01(2.38E+00)$^+$ 5.48E-02(1.39E-01)$^+$ 3.30E+01(5.81E+00)$^+$ ${\bf5.18E-07}({\bf1.12E-06})$ $f_{17}$ 30 4.57E-26(7.30E-26)$^+$ 2.85E-20(4.26E-20)$^+$ ${\bf6.02E-32}({\bf1.48E-31})$$^-$ 1.44E-12(1.26E-12)$^+$ 2.09E-26(1.95E-26) $f_{18}$ 30 3.80E-21(4.03E-21)$^+$ 3.51E-19(3.01E-19)$^+$ 3.66E-04(2.01E-03)$^+$ 2.45E-11(2.36E-11)$^+$ ${\bf2.48E-21}({\bf4.41E-21})$ $f_{19}$ 30 ${\bf3.67E+02}({\bf4.24E+02})$$^-$ 6.53E+02(6.50E+02)$^+$ 9.64E+02(4.81E+02)$^+$ 6.92E+02(8.00E+02)$^+$ 6.23E+02(5.05E+02) $f_{20}$ 30 2.20E-01(1.67E-05)$^+$ 1.60E-02(1.92E-03)$^+$ 1.38E-03(1.06E-03)$^+$ 1.75E+00(1.47E+00)$^+$ ${\bf2.07E-13}({\bf2.86E-13})$ $+/\approx/-$ 10/5/3 16/1/3 12/4/4 16/2/2 -/-/-
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表 5 DELU、SHADE、EPSDE、CoDE、SEFDE对50维测试函数的优化结果, 平均值(标准差)
Table 5 Compared data of 50 D optimization results on DELU、SHADE、EPSDE、CoDE、SEFDE, Mean (Std)
Fun $N$ DELU SHADE EPSDE CoDE SEFDE Mean error (Std Dev) Mean error (Std Dev) Mean error (Std Dev) Mean error (Std Dev) Mean error (Std Dev) $f_{1}$ 50 4.24E-42(4.13E-42)$^+$ 2.05E-53(3.34E-53)$^+$ 2.52E-50(7.95E-50)$^+$ 4.94E-21(5.74E-21)$^+$ ${\bf2.30E-57}({\bf2.91E-57})$ $f_{2}$ 50 1.92E-32(4.01E-32)$^+$ ${\bf1.35E-53}({\bf3.43E-53})$$^-$ 1.80E-51(3.75E-51)$^-$ 4.46E-22(6.24E-22)$^+$ ${\bf1.59E-42}({\bf2.13E-42})$ $f_{3}$ 50 7.87E-29(9.66E-29)$^+$ 2.22E-27(1.67E-27)$^+$ 5.63E-29(1.68E-28)$^+$ 6.05E-12(2.77E-12)$^+$ ${\bf5.52E-29}({\bf4.63E-29})$ $f_{4}$ 50 ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ 9.99E-17(3.51E-17)$^+$ 1.33E-16(4.68E-17)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$ $f_{5}$ 50 7.64E-21(8.52E-21)$^+$ ${\bf1.03E-52}({\bf1.07E-52})$$^-$ 3.04E-50(7.79E-50)$^-$ 1.01E-20(1.21E-20)$^+$ 1.50E-27(1.66E-27) $f_{6}$ 50 ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ 5.00E-01(7.07E-01)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$ $f_{7}$ 50 1.77E-04(9.11E-05)$^-$ ${\bf8.22E-07}({\bf1.80E-06})$$^-$ 2.22E+02(6.11E+01)$^+$ 2.80E-04(3.97E-04)$^-$ 9.43E+01(9.34E+00) $f_{8}$ 50 ${\bf2.59E-06}({\bf1.29E-06})$$^-$ 1.34E+01(1.83E+00)$^-$ 2.72E+01(1.81E+01)$^-$ 5.16E+01(2.64E+01)$^+$ 3.73E+01(3.74E+00) $f_{9}$ 50 ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ 2.46E-03(5.69E-03)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$ $f_{10}$ 50 2.28E-02(1.14E-02)$^-$ 5.45E-02(3.68E-02)$^-$ ${\bf3.12E-03}({\bf4.16E-03})$$^-$ 1.11E-01(7.80E-02)$^-$ 2.19E-01(2.67E-01) $f_{11}$ 50 ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ 1.19E+01(3.79E+01)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$ $f_{12}$ 50 ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ 3.39E-01(3.95E-01)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$ $f_{13}$ 50 9.42E-33(1.44E-48)$^+$ 9.42E-33(1.44E-48)$^+$ 9.46E-33(6.47E-35)$^+$ 4.76E-25(4.46E-25)$^+$ ${\bf1.44E-33}({\bf1.11E-33})$ $f_{14}$ 50 1.35E-32(2.88E-48)$^+$ 1.35E-32(2.88E-48)$^+$ 1.40E-32(8.62E-34)$^+$ 1.06E-24(8.10E-25)$^+$ ${\bf1.33E-32}({\bf0.00E+00})$ $f_{15}$ 50 6.13E-15(1.95E-15)$^+$ 7.11E-15(0.00E+00)$^+$ 1.14E-14(4.97E-15)$^+$ 8.59E-12(4.34E-12)$^+$ ${\bf1.04E-15}({\bf3.89E-15})$ $f_{16}$ 50 2.45E-07(1.58E-07)$^+$ 1.84E-01(5.31E-02)$^+$ 4.42E+00(1.09E+01)$^+$ 4.27E+01(7.39E+00)$^+$ ${\bf1.96E-07}({\bf1.10E-07})$ $f_{17}$ 50 3.25E-30(1.48E-30)$^-$ ${\bf9.42E-33}({\bf1.44E-48})$$^-$ 1.00E-32(1.96E-33)$^-$ 9.53E-24(4.52E-25)$^+$ 1.68E-26(3.74E-26) $f_{18}$ 50 3.08E-32(5.23E-33)$^-$ ${\bf1.36E-32}({\bf3.90E-34})$$^-$ 1.10E-03(3.47E-03)$^+$ 1.29E-22(1.22E-22)$^+$ 1.71E-24(3.73E-24) $f_{19}$ 50 8.90E+03(6.05E+03)$^-$ ${\bf1.04E+03}({\bf6.24E+02})$$^-$ 1.26E+04(1.65E+03)$^-$ 1.09E+04(1.44E+03)$^-$ 1.66E+04(2.24E+03) $f_{20}$ 50 2.70E-01(4.67E-02)$^+$ 2.60E-01(5.16E-02)$^+$ 2.80E-01(4.22E-02)$^+$ 3.20E-01(4.22E-02)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$ $+/\approx/-$ 9/5/6 10/2/7 12/2/6 12/5/3 -/-/-
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表 6 CLPSO、CMA-ES、GL-25和SEFDE的优化结果性能对比数据, 平均值(标准差)
Table 6 Compared data of optimization results on CLPSO、CMA-ES、GL-25 and SEFDE, Mean (Std)
Fun $N$ CLPSO CMA-ES GL-25 SEFDE Mean error (Std Dev) Mean error (Std Dev) Mean error (Std Dev) Mean error (Std Dev) $f_{1}$ 30 9.13E-14(3.33E-14)$^+$ 1.97E-29(2.07E-30)$^+$ 4.78E-87(1.69E-86)$^+$ ${\bf1.26E-103}({\bf2.39E-103})$ $f_{2}$ 30 9.79E-15(5.44E-15)$^+$ 3.75E-28(4.61E-29)$^+$ 2.66E-78(1.25E-77)$^+$ ${\bf2.40E-112}({\bf3.36E-112})$ $f_{3}$ 30 4.48E-09(1.48E-09)$^+$ 2.03E-14(9.76E-16)$^+$ 2.98E-28(1.12E-27)$^+$ ${\bf3.68E-60}({\bf3.36E-112})$ $f_{4}$ 30 3.37E-16(7.98E-17)$^+$ 4.81E-17(5.60E-17)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$ $f_{5}$ 30 9.33E-14(6.40E-14)$^+$ 1.50E-24(2.15E-25)$^+$ ${\bf4.38E-85}({\bf2.40E-84})$$^-$ 2.42E-45(2.29E-45) $f_{6}$ 30 ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$ $f_{7}$ 30 4.73E+00(1.20E+00)$^+$ ${\bf2.95E-27}({\bf4.82E-28})$$^-$ 3.14E-02(8.77E-02)$^-$ 2.66E-01(2.28E-01) $f_{8}$ 30 1.95E+01(2.62E+00)$^-$ ${\bf2.66E-01}({\bf1.01E+00})$$^-$ 2.21E+01(6.19E-01)$^+$ 2.19E+01(5.02E-01) $f_{9}$ 30 2.40E-09(3.67E-09)$^+$ 1.40E-03(3.70E-03)$^+$ 1.61E-15(4.55E-15)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$ $f_{10}$ 30 1.54E-01(2.09E-02)$^-$ 2.50E+02(1.23E+01)$^+$ 2.95E+00(1.00E+00)$^+$ ${\bf1.30E-01}({\bf6.42E-02})$ $f_{11}$ 30 ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ 5.19E+03(6.07E+02)$^+$ 4.46E+03(1.36E+03)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$ $f_{12}$ 30 ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ 1.30E+01(2.50E+00)$^+$ 3.14E-05(3.97E-14)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$ $f_{13}$ 30 1.13E-17(6.31E-18)$^+$ 9.13E-01(1.06E+00)$^+$ 8.82E-31(4.10E-30)$^+$ ${\bf1.57E-32}({\bf0.00E+00})$ $f_{14}$ 30 5.94E-17(3.31E-17)$^+$ 1.10E-03(3.35E-03)$^+$ 1.28E-30(5.05E-30)$^+$ ${\bf1.35E-32}({\bf0.00E+00})$ $f_{15}$ 30 9.76E-08(2.38E-08)$^+$ 1.93E+01(1.97E-01)$^+$ 1.09E-13(1.91E-13)$^+$ ${\bf7.55E-15}({\bf0.00E+00})$ $f_{16}$ 30 9.41E-07(6.48E-07)$^+$ 2.20E+02(5.64E+01)$^+$ 3.07E+01(2.71E+01)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$ $f_{17}$ 30 3.49E-16(1.59E-16)$^+$ 1.73E-02(3.93E-02)$^+$ 1.26E+02(1.09E+01)$^+$ ${\bf1.57E-32}({\bf0.00E+00})$ $f_{18}$ 30 2.52E-14(1.22E-14)$^+$ 2.20E-03(4.47E-03)$^+$ 1.97E+03(1.93E+02)$^+$ ${\bf1.35E-32}({\bf0.00E+00})$ $f_{19}$ 30 6.26E+03(9.93E+02)$^+$ ${\bf5.59E-10}({\bf7.36E-11})$$^-$ 2.49E+03(4.07E+02)$^+$ 5.60E+02(1.36E+02) $f_{20}$ 30 2.33E-04(9.49E-05)$^+$ 8.96E-02(1.46E-01)$^+$ 3.55E-04(9.33E-04)$^+$ ${\bf1.23E-10}({\bf2.47E-10})$ $+/\approx/-$ 15/3/2 16/1/3 16/2/2 -/-/-
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表 7 SHADE、IDE、ZPEDE、SinDE、SEFDE的优化结果性能对比数据, 平均值(标准差)
Table 7 Compared data of optimization results on SHADE、IDE、ZEPDE、SinDE、SEFDE, Mean (Std)
Fun N SHADE IDE ZEPDE SinDE SEFDE Mean error (Std Dev) Mean error (Std Dev) Mean error (Std Dev) Mean error (Std Dev) Mean error (Std Dev) $F_{1}$ 30 ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ 2.27E-13(1.53E-28)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$ $F_{2}$ 30 ${\bf2.66E+04}({\bf1.13E+04})$$^-$ 1.68E+06(4.23E+05)$^-$ 1.97E+05(7.53E+04)$^-$ 2.66E+06(8.33E+05)$^-$ 2.85E+07(4.18E+06) $F_{3}$ 30 8.80E+05(1.96E+06)$^+$ 1.38E+05(1.85E+05)$^+$ 1.50E+06(2.07E+06)$^+$ 1.01E+05(3.77E+05)$^+$ ${\bf5.80E+04}({\bf3.74E+04})$ $F_{4}$ 30 ${\bf1.61E-03}(1.41E-03)$$^-$ 6.85E+03(1.10E+03)$^+$ 7.66E-01(4.78E-01)$^-$ 8.28E+03(1.54E+03)$^+$ 3.13E+03(1.12E+03) $F_{5}$ 30 ${\bf0.00E+00}(0.00E+00)$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ 1.14E-13(7.65E-29)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$ $F_{6}$ 30 4.28E+01(5.52E+00)$^+$ 4.34E+01(2.62E-04)$^+$ 4.34E+01(3.18E-13)$^+$ 4.34E+01(1.44E-14)$^+$ ${\bf1.52E+01}({\bf1.95E-01})$ $F_{7}$ 30 2.33E+01(9.32E+00)$^-$ 3.18E+00(1.55E+00)$^-$ 1.37E+01(4.88E+00)$^-$ ${\bf6.10E-01}(5.97E-01)$$^-$ 2.80E+01(7.96E+00) $F_{8}$ 30 2.09E+01(1.68E-01)$^+$ 2.11E+01(2.44E-02)$^+$ 2.11E+01(1.17E-01)$^+$ 2.11E+01(3.59E-02)$^+$ ${\bf2.09E+01}({\bf2.78E-02})$ $F_{9}$ 30 5.54E+01(1.98E+00)$^+$ 3.56E+01(5.54E+00)$^+$ 3.74E+01(5.85E+00)$^+$ 3.48E+01(4.34E+00)$^+$ ${\bf3.04E+01}({\bf6.01E-01})$ $F_{10}$ 30 7.37E-02(3.67E-02)$^+$ 4.38E-02(2.17E-02)$^+$ 1.37E-01(6.96E-02)$^+$ 7.93E-02(3.57E-02)$^+$ ${\bf2.71E-02}({\bf1.69E-03})$ $F_{11}$ 30 ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ ${\bf0.00E+00}({\bf0.00E+00})$$^\approx$ 3.65E-01(6.12E-01)$^+$ 5.92E+00(2.86E+00)$^+$ ${\bf0.00E+00}({\bf0.00E+00})$ $F_{12}$ 30 5.86E+01(1.11E+01)$^-$ 6.89E+01(8.82E+00)$^-$ 6.04E+01(1.76E+01)$^-$ ${\bf5.61E+01}(1.41E+01)$$^-$ 1.21E+02(9.62E+00) $F_{13}$ 30 1.45E+02(1.95E+01)$^+$ 1.34E+02(2.28E+01)$^+$ 1.32E+02(3.62E+01)$^+$ 1.39E+02(3.41E+01)$^+$ ${\bf1.24E+02}({\bf1.29E+00})$ $F_{14}$ 30 ${\bf3.45E-02}({\bf1.93E-02})$$^-$ 1.17E+02(8.38E+01)$^+$ 4.83E+00(2.70E+00)$^-$ 2.34E+02(9.23E+01)$^-$ 5.85E+00(2.05E+00) $F_{15}$ 30 6.82E+03(4.41E+02)$^+$ 6.54E+03(5.91E+02)$^+$ 6.59E+03(9.36E+03)$^+$ 6.80E+03(1.00E+03)$^+$ ${\bf5.95E+03}({\bf2.37E+02})$ $F_{16}$ 30 1.28E+00(2.07E-01)$^-$ 1.59E+00(2.36E-01)$^-$ ${\bf7.82E-01}({\bf6.74E-01})$$^-$ 2.08E+00(3.66E-01)$^-$ 1.02E+02(1.66E-01) $F_{17}$ 30 5.08E+01(4.27E-14)$^+$ 5.92E+01(1.41E+00)$^+$ 5.11E+01(1.60E-01)$^+$ 6.52E+01(3.47E+00)$^+$ ${\bf4.21E+01}({\bf6.06E+01})$ $F_{18}$ 30 1.37E+02(1.29E+01)$^-$ 1.68E+02(1.27E+01)$^-$ ${\bf1.03E+02}({\bf1.19E+01})$$^-$ 1.41E+02(2.27E+01)$^-$ 3.01E+02(9.05E+00) $F_{19}$ 30 2.64E+00(2.83E-01)$^-$ ${\bf2.24E+00}({\bf3.66E-01})$$^-$ 3.71E+00(7.55E-01)$^-$ 4.85E+00(8.82E-01)$^-$ 1.03E+02(1.58E-01) $F_{20}$ 30 1.93E+01(7.70E-01)$^-$ 1.93E+01(4.47E-01)$^-$ 1.97E+01(7.88E-01)$^-$ ${\bf1.92E+01}(7.52E-01)$$^-$ 1.12E+02(9.46E-02) $F_{21}$ 30 8.45E+02(3.63E+02)$^+$ 7.32E+02(3.82E+02)$^+$ 6.33E+02(4.48E+02)$^+$ 5.84E+02(4.22E+02)$^+$ ${\bf5.03E+02}({\bf4.03E+01})$ $F_{22}$ 30 ${\bf1.33E+01}({\bf7.12E+00})$$^-$ 6.88E+01(2.03E+01)$^-$ 4.23E+02(5.75E+02)$^+$ 3.51E+02(2.72E+02)$^+$ 2.41E+02(1.24E+01) $F_{23}$ 30 7.63E+03(6.58E+02)$^+$ 7.32E+03(6.92E+02)$^+$ 7.02E+03(8.73E+02)$^+$ 6.59E+03(8.47E+02)$^+$ ${\bf6.38E+03}({\bf2.25E+02})$ $F_{24}$ 30 2.34E+02(1.01E+01)$^+$ 2.02E+02(1.14E+00)$^-$ 2.35E+02(1.09E+01)$^+$ ${\bf2.00E+02}(1.34E-01)$$^-$ 2.32E+02(1.18E+00) $F_{25}$ 30 3.40E+02(3.09E+01)$^+$ 3.03E+02(1.09E+01)$^+$ 3.23E+02(1.31E+01)$^+$ 2.97E+02(1.33E+01)$^+$ ${\bf2.58E+02}({\bf1.05E+01})$ $F_{26}$ 30 2.58E+02(8.08E+01)$^-$ ${\bf2.23E+02}({\bf4.46E+01})$$^-$ 2.27E+02(6.20E+01)$^-$ 2.76E+02(5.96E+01)$^-$ 3.02E+02(1.90E-01) $F_{27}$ 30 9.36E+02(3.07E+02)$^+$ ${\bf3.58E+02}({\bf3.30E+01})$$^-$ 9.38E+02(1.40E+02)$^+$ 4.75E+02(1.55E+02)$^-$ 6.11E+02(1.62E+02) $F_{28}$ 30 4.58E+02(4.13E+02)$^+$ ${\bf4.00E+02}({\bf0.00E+00})$$^\approx$ ${\bf4.00E+02}({\bf0.00E+00})$$^\approx$ ${\bf4.00E+02}(0.00E+00)$$^\approx$ ${\bf4.00E+02}({\bf0.00E+00})$ $+/\approx/-$ 14/3/11 13/4/11 15/3/10 16/1/11 -/-/-
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Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization, Technical Report 201212, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, 2013. -
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