期刊
INFORMATION SCIENCES
卷 298, 期 -, 页码 80-97出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2014.11.042
关键词
Cuckoo search algorithm; Global numerical optimization; Self adaptive method; Exploration; Exploitation; Chaotic system
资金
- Opening Fund of Top Key Discipline of Computer Software and Theory in Zhejiang Provincial Colleges at Zhejiang Normal University [ZSDZZZZXK37]
- Fundamental Research Funds for the Central Universities [11CXPY010, 14ZZ2240]
- Guangxi Natural Science Foundation [2013GXNSFBA019263]
- Science and Technology Research Projects of Guangxi Higher Education [2013YB029]
The cuckoo search algorithm (CS) is a simple and effective global optimization algorithm. It has been applied to solve a wide range of real-world optimization problem. In this paper, the proposed method uses two new mutation rules based on the rand and best individuals among the entire population. In order to balance the exploitation and exploration of the algorithm, the new rules are combined through a linear decreasing probability rule. Then, self adaptive parameter setting is introduced as a uniform random value to enhance the diversity of the population based on the relative success number of the proposed two new parameters in the previous period. To verify the performance of SACS, 16 benchmark functions chosen from literature are employed. Experimental results indicate that the proposed method performs better than, or at least comparable to state-of-the-art methods from literature when considering the quality of the solutions obtained. In the last part, experiments have been conducted on Lorenz system and Chen system to estimate the parameters of these two chaotic systems. Simulation results further demonstrate the proposed method is very effective. (C) 2014 Elsevier Inc. All rights reserved.
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