文章摘要
王晓红,杨礼彬,任展翔,莫海宁,庞斌,陈利,黄泽铭.求解包装生产中复杂问题的布谷鸟算法改进[J].包装工程,2021,42(5):240-246.
WANG Xiao-hong,YANG Li-bin,REN Zhan-xiang,MO Hai-ning,PANG Bin,CHEN Li,HUANG Ze-ming.Improvement of Cuckoo Algorithm for Solving Complex Problems in Packaging Production[J].Packaging Engineering,2021,42(5):240-246.
求解包装生产中复杂问题的布谷鸟算法改进
Improvement of Cuckoo Algorithm for Solving Complex Problems in Packaging Production
投稿时间:2020-07-26  
DOI:10.19554/j.cnki.1001-3563.2021.05.031
中文关键词: 高维函数  优化  布谷鸟搜索算法  变步长  莱维飞行  发现概率
英文关键词: high-dimensional function  optimization  cuckoo search algorithm  variable step size  Levi flight  discovery probability
基金项目:
作者单位
王晓红 上海理工大学上海 200093 
杨礼彬 上海理工大学上海 200093 
任展翔 上海理工大学上海 200093 
莫海宁 上海理工大学上海 200093 
庞斌 上海理工大学上海 200093 
陈利 上海理工大学上海 200093 
黄泽铭 上海理工大学上海 200093 
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中文摘要:
      目的 为了解决在求解复杂的高维函数优化问题时存在的求解精度不够高和易陷入局部最优等问题,提出一种基于莱维飞行发现概率的变步长布谷鸟搜索算法(LFCS)。方法 在相同环境下,选取6个不同难度、不同类型的测试函数,将LFCS算法与IPSO,IDE,IABC,CS算法比较,分析算法的收敛速度和收敛精度。结果 相比其他4种算法,LFCS算法迭代次数更少,收敛速度更快,收敛精度更高。结论 无论是低维函数还是高维函数,LFCS算法在收敛速度和收敛精度方面都有所提高,尤其是针对复杂的高维函数优化问题,在取值范围较大的情况下,LFCS算法能够更快、更准地找到最优解。
英文摘要:
      The work aims to propose a variable-step cuckoo search algorithm (denoted as LFCS) based on Levi's flight detection probability to solve the problems in solving complex high-dimensional function optimization, such as insufficient accuracy and easy to fall into local optimization. Under the same environment, six test functions of different difficulty and different types were selected, and the LFCS algorithm was compared with the IPSO, IDE, IABC, and CS algorithms to analyze the convergence speed and convergence accuracy of the algorithm. Compared with the other four algorithms, the LFCS algorithm had fewer iterations, faster convergence speed, and higher convergence accuracy. No matter whether it is a low-dimensional function or a high-dimensional function, the LFCS algorithm has improved convergence speed and convergence accuracy. Especially for complex high-dimensional function optimization problems, the LFCS algorithm can find the optimal solution faster and more accurately when the value range is large.
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