文章基本信息

标题：A Cuckoo Optimization Algorithm Using Elite Opposition-Based Learning and Chaotic Disturbance
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作者：Juan Li ; Tao Chen ; Ting Zhang 等
期刊名称：Journal of Software Engineering
印刷版ISSN：1819-4311
电子版ISSN：2152-0941
出版年度：2016
卷号：10
期号：1
页码：16-28
DOI：10.3923/jse.2016.16.28
出版社：Academic Journals Inc., USA
摘要：To solve the problem of slow convergence rate before reaching the global optimum in the conventional Cuckoo Search algorithm (CS). This study presents an enhanced CS algorithm called CH-EOBCCS, where elite members are introduced to generate their opposite solutions by elite opposition-based learning. Some excellent members will carry over to the next generations from the current solutions and the opposite solutions. This mechanism is helpful to enhance the ability of global optimum for CS algorithm and to expand the search area. At the same time, in order to expand diversity of the population, CH-EOBCCS algorithm introduces the chaotic disturbance to nest location in the iteration to improve the convergence speed. The experiments are conducted on 8 classic benchmark functions and the results show that the elite opposition-based learning and chaotic disturbance strategy has much better search performance than the generalized opposition-based learning strategy. The novel CH-EOBCCS algorithms improve the ability of CS to jump out of the local optima.