Cuckoo search with varied scaling factor

摘要

Cuckoo search (CS), inspired by the obligate brood parasitic behavior of some cuckoo species, iteratively uses Levy nights random walk (LFRW) and biased/selective random walk (BSRW) to search for new solutions. In this study, we seek a simple strategy to set the scaling factor in LFRW, which can vary the scaling factor to achieve better performance. However, choosing the best scaling factor for each problem is intractable. Thus, we propose a varied scaling factor (VSF) strategy that samples a value from the range [0,1] uniformly at random for each iteration. In addition, we integrate the VSF strategy into several advanced CS variants. Extensive experiments are conducted on three groups of benchmark functions including 18 common test functions, 25 functions proposed in CEC 2005, and 28 functions introduced in CEC 2013. Experimental results demonstrate the effectiveness of the VSF strategy.