Differential Cloud Particles Evolution Algorithm Based on Data-Driven Mechanism for Applications of ANN

被引:0
|
作者
Li, Wei [1 ]
机构
[1] Xian Univ Technol, Sch Comp Sci & Engn, Xian 710048, Peoples R China
关键词
GLOBAL OPTIMIZATION; SWARM OPTIMIZATION; GENETIC ALGORITHM; SELF-ADAPTATION; CONVERGENCE; STABILITY; STRATEGY; OPERATOR; GA;
D O I
10.1155/2017/8469103
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Computational scientists have designed many useful algorithms by exploring a biological process or imitating natural evolution. These algorithms can be used to solve engineering optimization problems. Inspired by the change of matter state, we proposed a novel optimization algorithm called differential cloud particles evolution algorithm based on data-driven mechanism (CPDD). In the proposed algorithm, the optimization process is divided into two stages, namely, fluid stage and solid stage. The algorithm carries out the strategy of integrating global exploration with local exploitation in fluid stage. Furthermore, local exploitation is carried out mainly in solid stage. The quality of the solution and the efficiency of the search are influenced greatly by the control parameters. Therefore, the data-driven mechanism is designed for obtaining better control parameters to ensure good performance on numerical benchmark problems. In order to verify the effectiveness of CPDD, numerical experiments are carried out on all the CEC2014 contest benchmark functions. Finally, two application problems of artificial neural network are examined. The experimental results show that CPDDis competitive with respect to other eight state-of-the-art intelligent optimization algorithms.
引用
收藏
页数:23
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