Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (4): 1088-1092.DOI: 10.11772/j.issn.1001-9081.2017.04.1088

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Cuckoo search algorithm for multi-objective optimization based on chaos cloud model

MA Yiyuan, SONG Weiping, NING Aiping, NIU Haifan   

  1. School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan Shanxi 030024, China
  • Received:2016-08-15 Revised:2016-09-14 Online:2017-04-10 Published:2017-04-19
  • Supported by:
    This work is partially supported by the Doctoral Research Start-up Funds of the Taiyuan University of Science and Technology (20142003), the Graduate Science and Technology Innovation Program of the Taiyuan University of Science and Technology (20145019).


马艺元, 宋卫平, 宁爱平, 牛海帆   

  1. 太原科技大学 电子信息工程学院, 太原 030024
  • 通讯作者: 马艺元
  • 作者简介:马艺元(1991-),女,山西太原人,硕士研究生,主要研究方向:电磁兼容、故障诊断;宋卫平(1960-),男,山西运城人,副教授,博士,主要研究方向:现代控制;宁爱平(1974-),女,山西运城人,讲师,博士,主要研究方向:智能信息处理、语言识别;牛海帆(1991-),女,山西晋中人,硕士研究生,主要研究方向:电磁兼容、故障诊断。
  • 基金资助:

Abstract: Concerning that Cuckoo Search algorithm for Multi-objective Optimization (MOCS) has slow speed in the late iteration and being easy to fall into the local optimum, a new MOCS based on Chaos Cloud Model (CCMMOCS) was proposed. In the evolutionary process, chaos theory was used to optimize the positions of general nests in order to avoid falling into the local optimum; then the cloud model was used to optimize the position of some better nests to improve the accuracy; finally the better value of them was chosen as the best value for optimization. The simulation experiments on five general test functions in error estimated value and diversity index show that CCMMOCS is much better than MOCS, Particle Swarm Optimization algorithm for Multi-objective Optimization (MOPSO) and NSGA-Ⅱ. Its Pareto fronts are closer to the ideal curve than those of other algorithms and the distribution is more uniform.

Key words: Cuckoo Search algorithm for Multi-objective Optimization (MOCS), chaos theory, cloud model, Pareto front, function optimization

摘要: 针对多目标布谷鸟搜索算法(MOCS)迭代后期寻优速度慢,并且容易造成局部最优等缺点,提出一种混沌云模型多目标布谷鸟搜索算法(CCMMOCS)。首先在进化过程中通过混沌理论对一般的布谷鸟巢位置在全局中寻求优化,以防落入局部最优;然后利用云模型对较好的布谷鸟巢位置局部优化来提高精度;最后将两种方法对比得到相对更好的解作为最优值以完成优化。对比误差估计值及多样性指标,由5个常用多目标测试函数仿真结果可知,CCMMOCS比传统多目标布谷鸟搜索算法、多目标粒子群算法(MOPSO)及多目标遗传(NSGA-Ⅱ)算法性能更好,Pareto前沿更接近理想曲线,分布也更均匀。

关键词: 多目标布谷鸟搜索算法, 混沌理论, 云模型, Pareto前沿, 函数优化

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