期刊
NEURAL COMPUTING & APPLICATIONS
卷 28, 期 -, 页码 S421-S438出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-016-2357-x
关键词
Evolutionary computation-based algorithm; Constrained optimization problems; Augmented Lagrangian function; Grey wolf optimization
资金
- National Natural Science Foundation of China [61463009]
- Humanities and Social Sciences Planning Foundation of Ministry of Education of China [12XJA910001]
- Beijing Natural Science Foundation [4122022]
- 125 Special Major Science and Technology of Department of Education of Guizhou Province [[2012]011]
- Science and Technology Foundation of Guizhou Province [[2016]2082]
This paper presents a novel constrained optimization algorithm named MAL-IGWO, which integrates the benefit of the improved grey wolf optimization (IGWO) capability for discovering the global optimum with the modified augmented Lagrangian (MAL) multiplier method to handle constraints. In the proposed MAL-IGWO algorithm, the MAL method effectively converts a constrained problem into an unconstrained problem and the IGWO algorithm is applied to deal with the unconstrained problem. This algorithm is tested on 24 well-known benchmark problems and 3 engineering applications, and compared with other state-of-the-art algorithms. Experimental results demonstrate that the proposed algorithm shows better performance in comparison to other approaches.
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