A hybrid intelligent optimization algorithm for an unconstrained single level lot-sizing problem

被引：0

作者：

Han, Yi ^{[1
,2
,3
]}

Dezhi, Wang ^{[1
]}

Mu, Lifeng ^{[4
]}

Cai, Jianhu ^{[1
]}

Kaku, Ikou ^{[5
]}

Zhao, Liping ^{[6
]}

机构：

[1] Shanghai University, Shanghai, China

[2] Zhejiang University of Technology, Hangzhou, China

[3] Huazhong University of Science and Technology, Wuhan, China

[4] Research Center for Technology Innovation and Enterprise Internationalization, Hangzhou, China

[5] Tokyo City University, Yokohama, Japan

[6] College of Information and Engineering, Jinhua Polytechnic, Jinhua, China

来源：

Open Cybernetics and Systemics Journal | 2014年 / 8卷

关键词：

Continuous optimization problems - Engineering optimization problems - Intelligent optimization algorithm - Lot sizing - Scatter search - Shuffled frog leaping - Shuffled frog leaping algorithm (SFLA) - Unconstrained;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Scatter search algorithm (SSA) and shuffled frog leaping algorithm (SFLA) are two intelligent optimization algorithms. SSA was introduced to solve discrete optimization problems in 1977 and SFLA in 2003 was created for solving continuous optimization problems. Currently, These two algorithms had already been wildly applied to solving many engineering optimization problems. Within this paper, a hybrid algorithm, which combines SSA and SFLA, is presented in the hope that the hybrid algorithm can contribute a great deal to the advancement of intelligence optimization research. A test is done on an unconstrained single-level lot-sizing (SLLS) problem to further demonstrate the effectiveness and efficiency of this hybrid algorithm. © Han et al.

引用

页码：484 / 488

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