期刊
INFORMATION SCIENCES
卷 473, 期 -, 页码 142-165出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2018.09.034
关键词
Artificial bee colony; Scale-free network; Optimization; Exploration; Exploitation
Many optimization algorithms have adopted scale-free networks to improve the search ability. However, most methods have merely changed their population topologies into those of scale-free networks; their experimental results cannot verify that these algorithm: have superior performance. In this paper, we propose a scale-free artificial bee colony algorithm (SFABC) in which the search is guided by a scale-free network. The mechanise enables the SFABC search to follow two rules. First, the bad food sources can learn more information from the good sources of their neighbors. Second, the information exchanged among good food sources is relatively rare. To verify the effectiveness of SFABC, the algorithm is compared with the original artificial bee colony algorithm (ABC), several advance ABC variants, and other metaheuristic algorithms on a wide range of benchmark functions Experimental results and statistical analyses indicate that SFABC obtains a better balance between exploration and exploitation during the optimization process and that, in most cases, it can provide a competitive performance of the benchmark functions. We also verify that scale-free networks can not only improve the optimization performance of ABC but also enhance the search ability of other metaheuristic algorithms, such as differentia evolution (DE) and the flower pollination algorithm (FPA). (C) 2018 Elsevier Inc. All rights reserved
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据