改进的鸡群优化算法

doi:10.13229/j.cnki.jdxbgxb20181268

Abstract

Abstract:

Based on the hierarchy mechanism of the conventional chicken swarm optimization (CSO) algorithm, an improved chicken swarm optimization (ICSO) algorithm is proposed to enhance the solution accuracy and the convergence rate of the conventional CSO algorithm. The ICSO algorithm introduces several improved factors that learned from the grey wolf optimizer (GWO) and the particle swarm optimization (PSO), to extend the searching ability of the algorithm. Moreover, a duplicate remove operator is also introduced to improve the diversity of the population. Experimental results show that the accuracy of the solution and the convergence rate of the proposed algorithm are better than other benchmark algorithms.

Key words: computer applications, chicken swarm optimization(CSO) algorithm, convergence rate, function optimization

CLC Number:

TP301

Bin LI,Guo⁃jun SHEN,Geng SUN,Ting⁃ting ZHENG. Improved chicken swarm optimization algorithm[J].Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1339-1344.

Figures/Tables 5

Fig.1

Table 1

Results of testing benchmark functions"

函数	ICSO算法	FA算法	CSO算法	PSO算法	BBO算法
最优总数	21	11	7	17	9
f₁	5.55e+04 ± 4.75e+04	1.78e+03 ± 1.12e+04	2.91e+06 ± 2.44e+06	2.18e+03 ± 1.93e+04	2.60e+05 ± 2.26e+06
f₂	1.08e+03 ± 2.71e+02	2.96e+02 ± 2.23e+03	1.25e+07 ± 7.15e+07	1.26e+03 ± 4.20e+02	5.02E+03 ± 1.09e+05
f₃	7.71e+02 + 2.86e+02	3.00e+02 ± 3.21e-04	1.45e+03 ± 6.71e+02	3.00e+02 ± 5.75e-03	1.01e+04 ± 3.39e+03
f₄	4.00e+02 ± 1.88e-01	4.35e+02 ± 8.24e+00	4.41e+02 ± 8.99e+00	4.00e+02 ± 1.70e+01	4.35e+02 ±1.16e+01
f₅	5.19e+02 ± 5.97e+00	5.20e+02 ± 2.80e+00	5.20e+02 ± 3.47e+00	5.20e+02 ± 5.48e+00	5.20e+02 ± 7.66e-02
f₆	6.00e+02 ± 1.24e+00	6.00e+02 ± 2.23e-02	6.05e+02 ± 8.97e-01	6.00e+02 ± 9.31e-01	6.02e+02 ± 1.19e+00
f₇	7.00e+02 ± 1.00e-02	7.00e+02 ± 4.89e-02	7.07e+02 ± 2.21e+00	7.00e+02 ± 3.53e-02	7.00e+02 ± 1.14e-01
f₈	8.03e+02 ± 3.77e+00	8.11e+02 ± 3.07e+00	8.12e+02 ± 3.27e+00	8.00e+02 ± 1.61e-14	8.01e+02 ± 1.20e+00
f₉	9.06e+02 ± 4.06e+00	9.07e+02 ± 3.23e+00	9.12e+02 ± 3.43e+00	9.07e+02 ± 1.71e+00	9.14e+02 ± 4.69e+00
f₁₀	1.06e+03 ± 2.36e+02	1.24e+03 ± 1.68e+02	1.26e+03 ± 8.50e+01	1.13e+03 ± 6.20e+01	1.03e+03 ± 1.18e+01
f₁₁	1.46e+03 ± 2.01e+02	1.69e+03 ± 1.68e+02	1.66e+03 ± 1.72e+02	1.48e+03 ± 1.29e+02	2.02e+03 ± 2.10e+02
f₁₂	1.20e+03 ± 3.14e-02	1.20e+03 ± 6.00e-03	1.20e+03 ± 1.17e-01	1.20e+03 ± 5.62e-02	1.20e+03 ± 2.40e-01
f₁₃	1.30e+03 ± 6.97e-02	1.30e+03 ± 1.91e-02	1.30e+03 ± 8.45e-02	1.30e+03 ± 4.53e-02	1.30e+03 ± 9.47e-02
f₁₄	1.40e+03 ± 7.79e-02	1.40e+03 ± 3.86e-02	1.40e+03 ± 1.63e-01	1.40e+03 ± 2.26e-02	1.40e+03 ± 7.76e-02
f₁₅	1.50e+03 ± 1.81e-01	1.50e+03 ± 1.48e-01	1.50e+03 ± 4.48e-01	1.50e+03 ± 2.38e-01	1.50e+03 ± 5.68e-01
f₁₆	1.60e+03 ± 3.18e-01	1.60e+03 ± 4.33e-01	1.60e+03 ± 3.30e-01	1.60e+03 ± 5.77e-01	1.60e+03 ± 4.10e-01
f₁₇	2.16e+03 ± 2.47e+02	2.45e+03 ± 3.62e+02	3.87e+03 ± 2.80e+03	3.89e+03 ± 1.23e+03	1.21e+04 ± 3.07e+05
f₁₈	1.91e+03 ± 6.00e+01	6.54e+03 ± 3.13e+00	8.68e+03 ± 2.71e+03	2.43e+03 ± 3.90e+03	4.02e+03 ± 5.01e+04
f₁₉	1.90e+03 ± 3.48e-01	1.90e+03 ± 6.41e-01	1.90e+03 ± 8.40e-01	1.90e+03 ± 6.95e-01	1.90e+03 ± 4.52e-01
f₂₀	2.10e+03 ± 6.34e+01	2.02e+03 ± 2.25e+01	2.07e+03 ± 1.42e+03	2.01e+03 ± 2.09e+00	2.56e+04 ± 2.78e+04
f₂₁	2.36e+03 ± 6.19e+00	2.55e+03 ± 2.10e+02	2.37e+03 ± 7.96e+02	2.22e+03 ± 5.13e+01	1.20e+04 ± 5.65e+04
f₂₂	2.22e+03 ± 7.12e+01	2.22e+03 ± 5.66e+01	2.23e+03 ± 3.30e+01	2.20e+03 ± 1.00e+01	2.22e+03 ± 4.51e+01
f₂₃	2.40e+03 ± 7.12e+01	2.63e+03 ± 1.26e-07	2.63e+03 ± 1.91e+01	2.63e+03 ± 1.02e-12	2.63e+03 ± 1.28e-01
f₂₄	2.52e+03 ± 4.68e+00	2.52e+03 ± 4.87e+00	2.54e+03 ± 1.24e+01	2.51e+03 ± 4.04e+00	2.53e+03 ± 7.41e+00
f₂₅	2.61e+03 ± 9.46e+00	2.62e+03 ± 1.79e+01	2.68e+03 ± 9.89e+00	2.70e+03 ± 3.72e+01	2.70e+03 ± 9.47e-02
f₂₆	2.70e+03 ± 4.46e-02	2.70e+03 ± 1.62e-02	2.70e+03 ± 4.45e-02	2.70e+03 ± 4.31e-02	2.70e+03 ± 2.60e+01
f₂₇	2.70e+03 ± 6.15e+01	3.00e+03 ± 1.37e+02	3.08e+03 ± 1.86e+02	3.10e+03 ± 1.67e+02	3.11e+03 ± 1.84e+02
f₂₈	3.00e+03 ± 6.35e+01	3.16e+03 ± 1.43e+02	3.19e+03 ± 1.20e+01	3.19e+03 ± 7.26e+01	3.17e+03 ± 2.69e+01
f₂₉	3.14e+03 ± 9.52e+01	3.33e+03 ± 2.41e+05	3.67e+03 ± 2.30e+02	3.23e+03 ±8.25e+05	3.57e+03 ± 5.15e+02
f₃₀	3.47e+03 ± 6.87e+01	3.75e+03 ± 1.59e+02	3.92e+03 ± 3.03e+02	3.63e+03 ± 2.46e+02	3.65e+03 ± 2.59e+02

Table 1

Fig.2

Fig.3

Fig.4

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Improved chicken swarm optimization algorithm

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