Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (11): 133-139.DOI: 10.3778/j.issn.1002-8331.1701-0237

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Genetic chicken swarm optimization algorithm for solving high-dimensional optimization problems

YANG Xiaojian, XU Xiaoting, LI Rongyu   

  1. School of Computer Science and Technology, Nanjing University of Technology, Nanjing 211800, China
  • Online:2018-06-01 Published:2018-06-14



  1. 南京工业大学 计算机科学与技术学院,南京 211800

Abstract: Aiming at the problems of slow convergence speed, low optimization precision and easy to fall into local optimum when solving high dimensional complex problems, combined with genetic thought, an improved chicken swarm optimization algorithm is proposed by adding a new concept that roosters and hens mate, mutate to hatch out new chicks, setting the mating cycles and the chicks update cycles, and using crossover operator and mutation operator to improve. Experiments are conducted on the 10 benchmark functions and the results show that the improved chicken swarm optimization algorithm has obviously better performance in optimization precision, the solution accuracy, convergence speed, stability and robustness than chicken swarm optimization algorithm and other two algorithms.

Key words: swarm intelligence, chicken swarm optimization, genetic, crossover operator, mutation operator

摘要: 针对鸡群算法在求解高维复杂优化问题时收敛速度慢、寻优精度不高、容易陷入局部最优等不足,结合遗传思想,增加公鸡和母鸡交配、变异产生新小鸡的概念,并设定交配周期和小鸡淘汰更新周期,利用交叉、变异算子对算法进行改进,得到一种改进的鸡群算法。通过对10组基准函数的实验结果进行分析,相比于标准鸡群算法和其他两种目前比较流行的群体智能优化算法,提出的改进鸡群算法在寻优精度、解的质量、收敛速度、稳定性及鲁棒性等方面优势明显,具有良好的性能。

关键词: 群体智能, 鸡群算法, 遗传, 交叉算子, 变异算子