Snap-drift cuckoo search: A novel cuckoo search optimization algorithm

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• 作者：Hojjat Rakhshani ; ^{rakhshani@pgs.usb.ac.ir} ; Amin Rahati ^{a.rahati@cs.usb.ac.ir}
• 关键词：Global numerical optimization ; Cuckoo search ; Lé ; vy flights ; Nonparametric tests ; Parameter sensitivity
• 刊名：Applied Soft Computing
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：52
• 期：Complete
• 页码：771-794
• 全文大小：2958 K
• 卷排序：52

文摘

We propose a novel cuckoo optimization algorithm called snap-drift cuckoo search (SDCS). The proposed SDCS employs reinforcement learning principles and improved search operators to achieve a more rapid and robust algorithm. The improved algorithm compared with cuckoo search (CS) and its several extensions. The improved algorithm compared with state-of-the algorithms and their variants. Statistical comparisons of experimental results show that SDCS is superior to the other algorithms in terms of convergence speed and robustness.