A cloud model based DNA genetic algorithm for numerical optimization problems

被引:49
作者
Zang, Wenke [1 ]
Ren, Liyan [1 ]
Zhang, Wenqian [1 ]
Liu, Xiyu [1 ]
机构
[1] Shandong Normal Univ, Sch Management Sci & Engn, Jinan, Shandong, Peoples R China
来源
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2018年 / 81卷
基金
美国国家科学基金会;
关键词
Cloud model; DNA; Genetic algorithm; DNA-GA; Numerical optimization; PARAMETER-ESTIMATION; NETWORKS;
D O I
10.1016/j.future.2017.07.036
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Bio-inspired algorithms for optimization are significant topics in the areas of computational intelligence. Traditional genetic algorithm easily gets stuck at a local optimum, and often has slow convergent speed. To overcome these drawbacks, the Cloud model based genetic algorithm with DNA encoding (CM-DNAGA) is originally proposed in this study. The CM-DNAGA algorithm is based on not only the properties of randomness and stable tendency of the normal cloud model, but also the idea of GA with the bio-inspired coding method, i.e., DNA. In CM-DNAGA, a Y conditional normal cloud generator is used as the genetic crossover operator, and a basic normal cloud generator is used as the mutation operator. The simulation experiments are conducted on 12 numerical optimization functions, which evaluate the performance of the proposed algorithm. The experimental results indicate that the proposed method is a competitive optimizer in comparison with the three state-of-the-art heuristic algorithms, i.e. standard GA, PSO and RNA-GA. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:465 / 477
页数:13
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