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A modified augmented Lagrangian with improved grey wolf optimization to constrained optimization problems

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Abstract

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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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61463009, the Humanities and Social Sciences Planning Foundation of Ministry of Education of China under Grant No. 12XJA910001, the Beijing Natural Science Foundation under Grant No. 4122022, the 125 Special Major Science and Technology of Department of Education of Guizhou Province under Grant No. [2012]011, and the Science and Technology Foundation of Guizhou Province under Grant No. [2016]2082.

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Authors and Affiliations

  1. Key Laboratory of Economics System Simulation, Guizhou University of Finance and Economics, Guizhou, China

    Wen Long, Shaohong Cai, Jianjun Jiao & Wenzhuan Zhang

  2. School of Science, Beijing University of Civil Engineering and Architecture, Beijing, China

    Ximing Liang

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  1. Wen Long
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  3. Shaohong Cai
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  4. Jianjun Jiao
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Correspondence to Ximing Liang.

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Long, W., Liang, X., Cai, S. et al. A modified augmented Lagrangian with improved grey wolf optimization to constrained optimization problems. Neural Comput & Applic 28 (Suppl 1), 421–438 (2017). https://doi.org/10.1007/s00521-016-2357-x

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