Adaptive cooperation evolutionary bat algorithm

被引：0
作者：
Liu Z. [1 ]
Lu H.-J. [1 ]
Liu W.-B. [1 ]
机构：
[1] College of Coastal Defense Force, Naval Aeronautical University, Yantai
来源：
Kongzhi yu Juece/Control and Decision | 2019年 / 34卷 / 08期
关键词：
Adaptive; Bat algorithm; Convergence; Cooperation evolutionary; Search framework;
D O I：
10.13195/j.kzyjc.2018.0029
中图分类号：
学科分类号：
摘要：
The bat algorithm is a novel meta-heuristic nature-inspired algorithm, and also easy to trap into local optimum inevitably, therefore, the paper proposes an adaptive cooperation evolutionary bat algorithm (ACEBA). In order to ensure the proper framework for the algorithm, the evolutionary framework can be switched between the centralized and distributed framework according to the diversity judgment criteria in order to ensure the favorable evolutionary framework for the algorithm. In order to ensure the exploration ability of the main population and the exploitation ability of the sub-population, the position and velocity for the bat are updated, and the update way in main population is different from the sub-population. The compensation for Doppler effect in echoes is considered and the former fixed constant can change adaptively. Finally, the convergence of the algorithm is also deduced and verified by simulation results show the effectiveness and correctness of the proposed algorithm. © 2019, Editorial Office of Control and Decision. All right reserved.
引用
收藏
页码：1626 / 1634
页数：8
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[21]  
Tanweer M.R., Suresh S., Sundararajan N., Self-regulating particle swarm optimization algorithm, Information Sciences, 294, pp. 182-202, (2015)
[22]  
Rana F., Alireza M., Richard J., Et al., Enriched ant colony optimization and its application in feature selection, Neurocomputing, 142, 10, pp. 354-371, (2014)
[23]  
Phuc N.H., Chang W.A., Fast articial bee colony and its application to stereo correspondence, Expert Systems with Applications, 45, 1, pp. 460-470, (2016)
[24]  
Jensi R., Wiselin G., An improved krill herd algorithm with global exploration capability for solving numerical function optimization problems and its application to data clustering, Applied Soft Computing, 46, 4, pp. 230-245, (2016)
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