Advanced Search
Article Contents
Article Contents

Improved Cuckoo Search algorithm for numerical function optimization

  • * Corresponding author: Changzhi Wu

    * Corresponding author: Changzhi Wu 
This paper is partially supported by the National Natural Science Foundation of China (61473326) and Australian Research Council.
Abstract Full Text(HTML) Figure(2) / Table(4) Related Papers Cited by
  • Cuckoo Search (CS) is a recently proposed metaheuristic algorithm to solve optimization problems. For improving its performance both on the efficiency of searching and the speed of convergence, we proposed an improved Cuckoo Search algorithm based on the teaching-learning strategy (TLCS). For a better balance between intensification and diversification, both a dynamic weight factor and an out-of-bound project strategies are also introduced into TLCS. The results of numerical experiment demonstrate that our improved TLCS performs better than the basic CS and other two improved CS methods appearing in literatures.

    Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.


    \begin{equation} \\ \end{equation}
  • 加载中
  • Figure 1.  Project plot. $x_1, x_2, x_3$ are transformed to $y_1, y_2, y_3$, respectively, by (11) while $x_4$ is first transformed to $y_4$ by (11), then to $y_4'$ by (12).

    Figure 2.  Convergence performance of CS, ASCS, WCS and TLCS, on 8 test functions of 5 dimensions. Horizontal and vertical axes represent the number of iterations and the logarithm of the function values, respectively

    Table 1.  18 benchmark problems of $n(\geq 2)$ dimensions used in experiments. $x = (x_1, x_2, \cdots, x_n)$, C: characteristic, Opt.: optimal function value, U.: unimodal, M.: multimodal, S.: separable, N.: non-separable.

    Function Name C Range Formula Opt.
    Sphere S.U. [-100,100] $f_1(x)= \sum\limits_{i=1}^{n}x_{i}^2$ 0
    Ackley S.M. [-32, 32] $ f_2(x)=20-20 e^{-0.2\sqrt{\frac{1}{n}\sum\limits_{i=1}^n x_i^2}} -e^{\frac{1}{n}\sum\limits_{i=1}^n \cos (2\pi x_i)} +e$ 0
    Sum-Squares S.U. $[-10, 10]$ $f_{3}(x)=\sum\limits_{i=1}^n ix_i^2$ 0
    Rastrigin S.M. [-5.12, 5.12] $f_4(x) = 10n + \sum\limits_{i=1}^n \big[x_i^2 -10\cos(2\pi x_i)\big]$ 0
    Rosenbrock N.U. [-10, 10] $f_5(x)= \sum\limits_{i=1}^{n-1}\big[100(x_{i}^2 - x_{i+1})^2 + (x_i -1)^2\big]$ 0
    Griewank N.M. [-600,600] $f_{6}(x) = 1 + \frac{1}{4000} \sum\limits_{i=1}^n x^2_i - \prod\limits_{i=1}^n \cos(\frac{x_i}{\sqrt{i}})$ 0
    Levy N.M. [-10, 10] $f_{7}(x)={\sin(\pi w_1)}^2 +\sum\limits_{i=1}^{n-1}\big \{{(w_i-1)}^2\big[1+10$
    ${\sin(\pi w_i+1)}^2\big]\big\}+{(w_n-1)}^2[1+{\sin(2\pi w_n)]}^2$, 0
    where $w_i=1+\frac{x_i-1}{4}$, for all $i=1, 2, \cdots, n$
    Powell N.M [-4, 5] $f_{8}(x)=\sum\limits_{i=1}^\frac{n}{4} \Big[{(x_{4i-3}+10x_{4i-2})}^2+ 5(x_{4i-1}-x_{4i-2})^2$ 0
    $ + (x_{4i-2}-2x_{4i-1})^4+ 10(x_{4i-3}-x_{4i})^4\Big]$
    Zakharov N.- [-5, 10] $f_9(x)=\sum\limits_{i=1}^{n}x_{i}^2 + \Big(\frac{1}{2}\sum\limits_{i=1}^{n}ix_{i}\Big)^2+\Big( \frac{1}{2}\sum\limits_{i=1}^{n}ix_{i}\Big)^4$ 0
    Weierstrass N.M. $[-0.5, 0.5]$ $f_{10}(x)=\sum\limits_{i=1}^n\big(\sum_{k=0}^{k_{max}}[a^k\cos(2\pi b^k(x_i+0.5))]\big)$ 0
    $~~-n\sum_{k=0}^{k_{max}}[a^k\cos(2\pi b^k\cdot0.5)], $
    $a=0.5, b=3, k_{max}=20$
    Schwefel S.M. [-500,500] $f_{11}(x) = 418.982887 \cdot n-\sum\limits_{i=1}^n \big(x_i \sin(\sqrt{|x_i|}\big)$ 0
    Whitley N.M. [-10.2, 10.2] $f_{12}(x) = \sum\limits_{i=1}^n \sum\limits_{j=1}^n \Big[\frac{(100(x_i^2-x_j)^2 + (1-x_j)^2)^2}{4000} $ 0
    $-\cos\big(100(x_i^2-x_j)^2 + (1-x_j)^2\big)+1\Big]$
    Trid N.- $[-n^2, n^2]$ $f_{13}(x)=\sum\limits_{i=1}^{n}(x_i-1)^2- \sum\limits_{i=1}^{n}x_ix_{i-1}, (n=5)$ $-30$
    $(n=10)$ $-200$
    Styblinski-Tang S.M. $[-5, 5]$ $f_{14}(x)=\frac{1}{n}\sum\limits_{k=1}^n (x_i^4-16x_i^2+5x_i), a=39.16599$ $-an$
    Alpine N.M. $[-10, 10]$ $f_{15}(x)=\sum\limits_{i=1}^n |x_i\cdot \sin(x_i)+0.1\cdot x_i|$ 0
    Easom S.U. $[-100,100]$ $f_{16}(x)=-\cos(x_1)\cos(x_2)e^{-(x_1-pi)^2-(x_2-pi)^2}$ -1
    Treccani S.M. $[-3, 3]$ $f_{17}(x)=(x_1)^4+4(x_1)^3+4(x_1)^2+(x_2)^2$ 0
    Perm$(n, \beta)$ N.U $[-n, n]$ $f_{18}(x)=\sum\limits_{k=1}^n {\Big[\sum\limits_{j=1}^n (j^k+\beta) \left({x_j^ k-\frac{1}{j^k}}\right)\Big]}^2, \beta=0.5$ 0
     | Show Table
    DownLoad: CSV

    Table 2.  Statistic results obtained by CS [24], ASCS [25], WCS [28] and TLCS on 5-D functions over 30 independent runs, respectively

    Fun. Indictor CS ASCS WCS TLCS
    $f_{1}$ Best 4.5213e-14 2.4536e-18 0.0000e+00 0.0000e+00
    Mean 2.6910e-12 1.7970e-16 0.0000e+00 0.0000e+00
    Std. 4.2657e-12 2.0513e-16 0.0000e+00 0.0000e+00
    $f_{2}$ Best 5.3705e-05 9.3072e-05 8.8818e-16 8.8818e-16
    Mean 3.7553e-04 6.6183e-04 8.8818e-16 8.8818e-16
    Std. 4.3639e-04 6.6652e-04 0.0000e+00 0.0000e+00
    $f_{3}$ Best 9.9825e-16 5.7419e-19 0.0000e+00 0.0000e+00
    Mean 3.5974e-14 1.8282e-17 0.0000e+00 0.0000e+00
    Std. 4.4513e-14 2.9784e-17 0.0000e+00 0.0000e+00
    $f_{4}$ Best 2.1760e-02 6.0844e-02 0.0000e+00 0.0000e+00
    Mean 4.1580e-01 6.0469e-01 0.0000e+00 0.0000e+00
    Std. 4.1771e-01 4.3998e-01 0.0000e+00 0.0000e+00
    $f_{5}$ Best 5.4423e-04 3.8454e-03 9.0786e-05 3.2173e-06
    Mean 4.9548e-02 8.8289e-02 4.9515e-03 9.0026e-05
    Std. 8.2985e-02 1.2769e-01 4.4433e-03 2.4935e-04
    $f_{6}$ Best 2.4318e-02 1.3526e-02 0.0000e+00 0.0000e+00
    Mean 4.0590e-02 4.9268e-02 1.6139e-02 0.0000e+00
    Std. 1.1239e-02 2.1748e-02 2.6449e-02 0.0000e+00
    $f_{7}$ Best 7.4053e-12 4.1549e-11 1.4224e-13 6.0523e-17
    Mean 4.5672e-10 5.7734e-10 4.9219e-11 1.3148e-15
    Std. 1.2514e-09 5.2112e-10 4.1785e-11 2.0139e-15
    $f_{8}$ Best 7.5663e-19 1.9396e-20 0.0000e+00 5.2592e-51
    Mean 3.2565e-17 1.1242e-17 0.0000e+00 1.0280e-44
    Std. 4.6518e-17 1.5804e-17 0.0000e+00 2.1851e-44
    $f_{9}$ Best 2.7592e-13 1.2952e-16 0.0000e+00 0.0000e+00
    Mean 2.6449e-12 1.6090e-15 0.0000e+00 0.0000e+00
    Std. 2.9165e-12 1.4999e-15 0.0000e+00 0.0000e+00
    $f_{10}$ Best 5.5995e-03 6.6042e-03 0.0000e+00 0.0000e+00
    Mean 1.1684e-02 1.0426e-02 0.0000e+00 0.0000e+00
    Std. 6.3211e-03 3.5660e-03 0.0000e+00 0.0000e+00
    $f_{11}$ Best 2.0752e+03 2.0752e+03 2.0752e+03 2.0225e+03
    Mean 2.0752e+03 2.0752e+03 2.0752e+03 2.0616e+03
    Std. 4.7941e-11 1.7728e-10 1.5453e-09 2.2077e+01
    $f_{12}$ Best 1.1195e-03 1.3950e-03 1.1187e-02 9.8810e-13
    Mean 7.2247e-01 5.6108e-01 1.5719e+00 5.6092e+00
    Std. 9.9970e-01 8.9282e-01 1.2832e+00 4.2152e+00
    $f_{13}$ Best -3.0000e+01 -3.0000e+01 -3.0000e+01 -3.0000e+01
    Mean -3.0000e+01 -3.0000e+01 -3.0000e+01 -3.0000e+01
    Std. 2.3597e-13 0.0000e+00 4.2507e-12 6.6486e-14
    $f_{14}$ Best -7.8332e+01 -7.8332e+01 -7.8332e+01 -7.8332e+01
    Mean -7.8332e+01 -7.8332e+01 -7.8332e+01 -7.7201e+01
    Std. 5.0220e-07 8.9447e-07 1.2933e-06 2.3413e+00
    $f_{15}$ Best 1.7330e-03 3.1350e-03 1.1926e-181 0.0000e+00
    Mean 8.1827e-03 1.4765e-02 3.9923e-177 0.0000e+00
    Std. 4.3095e-03 1.2199e-02 0.0000e+00 0.0000e+00
    $f_{16}$ Best -1.0000e+00 -1.0000e+00 -1.0000e+00 -1.0000e+00
    Mean -1.0000e+00 -1.0000e+00 -1.0000e+00 -1.0000e+00
    Std. 1.0624e-13 1.3743e-13 0.0000e+00 0.0000e+00
    $f_{17}$ Best -3.5527e-15 -3.5527e-15 0.0000e+00 0.0000e+00
    Mean -3.3892e-15 -3.1925e-15 0.0000e+00 2.0292e-67
    Std. 6.3290e-16 9.9925e-16 0.0000e+00 7.8473e-67
    $f_{18}$ Best 2.5826e-05 5.0852e-04 3.9689e-07 1.0547e-13
    Mean 6.0659e-02 6.3264e-02 1.7993e-05 4.1372e-12
    Std. 1.2079e-01 1.0323e-01 1.8082e-05 4.4431e-12
     | Show Table
    DownLoad: CSV

    Table 3.  Statistic results obtained by CS [24], ASCS [25], WCS [28] and TLCS on 10-D functions over 30 independent runs, respectively

    Fun. Indictor CS ASCS WCS TLCS
    $f_{1}$ Best 3.2389e-06 1.1207e-08 0.0000e+00 0.0000e+00
    Mean 8.0057e-06 5.3375e-08 0.0000e+00 0.0000e+00
    Std. 4.6479e-06 2.9549e-08 0.0000e+00 0.0000e+00
    $f_{2}$ Best 3.7125e-02 8.7783e-02 8.8818e-16 8.8818e-16
    Mean 6.0272e-01 4.0848e-01 8.8818e-16 8.8818e-16
    Std. 5.8084e-01 2.2043e-01 0.0000e+00 0.0000e+00
    $f_{3}$ Best 1.8590e-07 5.7623e-10 0.0000e+00 0.0000e+00
    Mean 5.2596e-07 2.7587e-09 0.0000e+00 0.0000e+00
    Std. 2.9285e-07 2.3201e-09 0.0000e+00 0.0000e+00
    $f_{4}$ Best 6.0868e+00 9.9616e+00 0.0000e+00 0.0000e+00
    Mean 1.1568e+01 1.5915e+01 0.0000e+00 0.0000e+00
    Std. 2.4106e+00 4.5746e+00 0.0000e+00 0.0000e+00
    $f_{5}$ Best 5.5106e-01 1.1646e+00 2.7452e+00 2.2219e+00
    Mean 4.3244e+00 3.2423e+00 3.6089e+00 2.7140e+00
    Std. 1.4901e+00 1.5008e+00 3.7702e-01 3.3872e-01
    $f_{6}$ Best 5.4516e-02 4.9131e-02 0.0000e+00 0.0000e+00
    Mean 7.6544e-02 9.0650e-02 0.0000e+00 0.0000e+00
    Std. 1.4500e-02 2.1017e-02 0.0000e+00 0.0000e+00
    $f_{7}$ Best 1.6338e-04 3.3752e-03 4.5958e-06 9.2416e-10
    Mean 1.0474e-02 2.5951e-02 1.2202e-04 1.7004e-08
    Std. 1.0911e-02 1.8264e-02 1.1195e-04 1.4461e-08
    $f_{8}$ Best 6.3186e-09 1.5939e-09 0.0000e+00 2.1348e-36
    Mean 4.4004e-08 9.4253e-09 0.0000e+00 7.5924e-26
    Std. 4.4362e-08 7.1640e-09 0.0000e+00 2.3933e-25
    $f_{9}$ Best 8.7140e-04 3.7275e-05 0.0000e+00 0.0000e+00
    Mean 4.8839e-03 8.7891e-05 0.0000e+00 0.0000e+00
    Std. 3.2252e-03 4.7437e-05 0.0000e+00 0.0000e+00
    $f_{10}$ Best 4.0273e-01 4.6694e-01 0.0000e+00 0.0000e+00
    Mean 7.8045e-01 8.4527e-01 0.0000e+00 0.0000e+00
    Std. 2.4665e-01 2.5778e-01 0.0000e+00 0.0000e+00
    $f_{11}$ Best 4.1504e+03 4.1504e+03 4.1504e+03 4.0458e+03
    Mean 4.1504e+03 4.1504e+03 4.1504e+03 4.1152e+03
    Std. 1.1924e-04 6.4787e-04 2.7421e-04 3.9471e+01
    $f_{12}$ Best 2.7106e+01 3.3064e+01 2.4671e+01 4.0486e+01
    Mean 4.2321e+01 4.4884e+01 3.8730e+01 4.4356e+01
    Std. 6.7034e+00 6.0260e+00 4.9097e+00 1.7008e+00
    $f_{13}$ Best -1.2467e+02 -1.2467e+02 -1.2467e+02 -1.7996e+02
    Mean -1.2467e+02 -1.2467e+02 -1.2467e+02 -1.6700e+02
    Std. 6.0730e-09 5.1284e-10 4.6884e-08 6.7526e+00
    $f_{14}$ Best -7.8281e+01 -7.8225e+01 -7.8051e+01 -7.8332e+01
    Mean -7.7819e+01 -7.6712e+01 -7.6642e+01 -7.6256e+01
    Std. 3.5470e-01 1.4800e+00 1.1564e+00 2.9181e+00
    $f_{15}$ Best 2.2444e-01 5.7034e-01 2.5015e-181 0.0000e+00
    Mean 3.8266e-01 1.1298e+00 4.8905e-179 0.0000e+00
    Std. 1.0949e-01 4.0051e-01 0.0000e+00 0.0000e+00
    $f_{16}$ Best -1.0000e+00 -1.0000e+00 -1.0000e+00 -1.0000e+00
    Mean -1.0000e+00 -1.0000e+00 -1.0000e+00 -1.0000e+00
    Std. 9.4746e-13 6.1618e-13 0.0000e+00 0.0000e+00
    $f_{17}$ Best -3.5527e-15 -3.5527e-15 0.0000e+00 0.0000e+00
    Mean -3.3158e-15 -3.3945e-15 0.0000e+00 2.7979e-35
    Std. 9.1735e-16 5.0210e-16 0.0000e+00 1.0836e-34
    $f_{18}$ Best 1.0000e+10 1.0000e+10 3.2642e+01 2.4003e-05
    Mean 1.0000e+10 1.0000e+10 6.0712e+01 5.3966e-04
    Std. 0.0000e+00 0.0000e+00 2.0484e+01 4.9058e-04
     | Show Table
    DownLoad: CSV

    Table 4.  Wilcoxon rank sum results on best and average function values obtained by TLCS against CS, ASCS and WCS algorithms for 18 benchmark functions

    Algorithm Dim. criteria rank sum $p$ value $h$ value
    TLCS to CS Best 421.5000 0.0051 1
    Mean 411.0000 0.0139 1
    Std 422.0000 0.0048 1
    TLCS to ASCS Best 415.0000 0.0095 1
    5 Mean 406.0000 0.0213 1
    Std 408.5000 0.0165 1
    TLCS to WCS Best 357.0000 0.4363 0
    Mean 356.5000 0.4532 0
    Std 333.0000 1.0000 0
    TLCS to CS Best 410.5000 0.0143 1
    Mean 407.0000 0.0196 1
    Std 405.5000 0.0213 1
    TLCS to ASCS Best 408.5000 0.0170 1
    10 Mean 406.0000 0.0213 1
    Std 407.5000 0.0180 1
    TLCS to WCS Best 353.0000 0.5183 0
    Mean 348.0000 0.6339 0
    Std 324.5000 0.7810 0
     | Show Table
    DownLoad: CSV
  • [1] S. Das and P. Suganthan, Differential evolution, A survey of the state-of-the-art, IEEE Transactions on Evolutionary Computation, 15 (2011), 4-31. 
    [2] M. Dorigo and C. Blum, Ant colony optimization theory, A survey, Theoretical Computer Science, 344 (2005), 243-278.  doi: 10.1016/j.tcs.2005.05.020.
    [3] R. Eberhart and Y. Shi, Particle swarm optimization, Development, applications and resources, Proceedings of the 2001 Congress on Evolutionary Computation, 1 (2001), 81-86.  doi: 10.1109/CEC.2001.934374.
    [4] E. ValianS. Mohanna and S. Tavakoli, Improved cuckoo search algorithm for global optimization, International Journal of Communications and Information Technology, 1 (2011), 31-44. 
    [5] I. FisterX. Yang and D. Fister, Cuckoo search, A brief literature review, Studies in Computational Intelligence, 516 (2014), 49-62.  doi: 10.1007/978-3-319-02141-6_3.
    [6] D. Goldberg, Genetic algorithms and machine learning, Machine Learning, 3 (1988), 95-99. 
    [7] D. Karaboga and B. Basturk, A powerful and efficient algorithm for numerical function optimization, artificial bee colony (ABC) algorithm, Journal of Global Optimization, 39 (2007), 459-471.  doi: 10.1007/s10898-007-9149-x.
    [8] J. LiuS. DongL. ZhangQ. Ma and C. Wu, Estimation of archie parameters by a novel hybrid optimization algorithm, Journal of Petroleum Science and Engineering, 135 (2015), 232-239.  doi: 10.1016/j.petrol.2015.09.003.
    [9] J. LiuK. TeoX. Wang and C. Wu, An exact penalty function-based differential search algorithm for constrained global optimization, Soft Computing, 20 (2015), 1305-1313.  doi: 10.1007/s00500-015-1588-6.
    [10] J. LiuC. WuG. Wu and X. Wang, A novel differential search algorithm and applications for structure design, Applied Mathematics & Computation, 268 (2015), 246-269.  doi: 10.1016/j.amc.2015.06.036.
    [11] J. LiuS. ZhangC. WuJ. LiangX. Wang and K. L. Teo, A hybrid approach to constrained global optimization, Applied Soft Computing, 47 (2016), 281-294.  doi: 10.1016/j.asoc.2016.05.021.
    [12] J. LiuH. ZhuQ. MaL. Zhang and H. Xu, An artificial bee colony algorithm with guide of global & local optimal and asynchronous scaling factors for numerical optimization, Applied Soft Computing, 37 (2015), 608-618. 
    [13] Q. Long and C. Wu, A hybrid method combining genetic algorithm and Hooke-Jeeves method for constrained global optimization, Journal of Industrial & Management Optimization, 10 (2014), 1279-1296.  doi: 10.3934/jimo.2014.10.1279.
    [14] M. PlatelL. DefoinS. Schliebs and N. Kasabov, Quantum-inspired evolutionary algorithm, a multimodel EDA, IEEE Transactions on Evolutionary Computation, 13 (2009), 1218-1232.  doi: 10.1109/TEVC.2008.2003010.
    [15] A. QinV. Huang and P. Suganthan, Differential evolution algorithm with strategy adaptation for global numerical optimization, IEEE Transactions on Evolutionary Computation, 13 (2009), 398-417.  doi: 10.1109/TEVC.2008.927706.
    [16] R. RaoV. Savsani and D. Vakharia, Teaching-learning-based optimization, A novel method for constrained mechanical design optimization problems, Computer-aided Design, 43 (2011), 303-315.  doi: 10.1016/j.cad.2010.12.015.
    [17] R. RaoV. Savsani and D. Vakharia, Teaching-learning-based optimization, An optimization method for continuous non-linear large scale problems, Information Sciences, 183 (2012), 1-15.  doi: 10.1016/j.ins.2011.08.006.
    [18] S. Tao, C. Wu, Z. Sheng and X. Wang, Space-time repetitive project scheduling considering location and congestion, Journal of Computing in Civil Engineering, 32 (2018), Article ID, 04018017. doi: 10.1061/(ASCE)CP.1943-5487.0000745.
    [19] S. TaoC. WuZ. Sheng and X. Wang, Stochastic project scheduling with hierarchical alternatives, Applied Mathematical Modelling, 58 (2018), 181-202.  doi: 10.1016/j.apm.2017.09.015.
    [20] M. Tavazoei and M. Haeri, Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms, Applied Mathematics and Computation, 187 (2007), 1076-1085.  doi: 10.1016/j.amc.2006.09.087.
    [21] Z. WangD. Gao and J. Liu, Multi-objective sidetracking horizontal well trajectory optimization in cluster wells based on DS algorithm, Journal of Petroleum Science & Engineering, 147 (2016), 771-778.  doi: 10.1016/j.petrol.2016.09.046.
    [22] X. Yang and S. Deb, Cuckoo search, Recent advances and applications, Neural Computing and Applications, 24 (2014), 169-174.  doi: 10.1007/s00521-013-1367-1.
    [23] X. S. Yang, Nature-Inspired Metaheuristic Algorithms, Luniver Press, 2010.
    [24] X. S. Yang and S. Deb, Cuckoo search via lévy flights, In World Congress on Nature & Biologically Inspired Computing, (2009), 210-214.
    [25] Y. ZhangL. Wang and Q. Wu, Modified adaptive cuckoo search (MACS) algorithm and formal description for global optimisation, International Journal of Computer Applications in Technology, 44 (2012), 73-79.  doi: 10.1504/IJCAT.2012.048675.
    [26] C. ZhaoC. WuJ. ChaiX. WangX. YangJ. Lee and M. Kim, Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty, Applied Soft Computing, 55 (2011), 549-564.  doi: 10.1016/j.asoc.2017.02.009.
    [27] H. Zheng and Y. Zhou, A novel cuckoo search optimization algorithm base on Gauss distribution, Journal of Computational Information Systems, 8 (2012), 4193-4200. 
    [28] H. Zheng and Y. Zhou, Self-adaptive step cuckoo search algorithm, Computer Engineering & Applications, 49 (2013), 68-71. 
  • 加载中




Article Metrics

HTML views(2619) PDF downloads(380) Cited by(0)

Access History

Other Articles By Authors



    DownLoad:  Full-Size Img  PowerPoint