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An augmented Lagrangian ant colony based method for constrained optimization

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Abstract

One of the most efficient penalty based methods to solve constrained optimization problems is the augmented Lagrangian algorithm. This paper presents a constrained optimization algorithm to solve continuous constrained global optimization problems. The proposed algorithm integrates the benefit of the continuous ant colony (\(\hbox {ACO}_\mathrm{R}\)) capability for discovering the global optimum with the effective behavior of the Lagrangian multiplier method to handle constraints. This method is tested on 13 well-known benchmark functions and compared with four other state-of-the-art algorithms.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran

    Asghar Mahdavi & Mohammad Ebrahim Shiri

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  1. Asghar Mahdavi
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  2. Mohammad Ebrahim Shiri
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Correspondence to Asghar Mahdavi.

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Mahdavi, A., Shiri, M.E. An augmented Lagrangian ant colony based method for constrained optimization. Comput Optim Appl 60, 263–276 (2015). https://doi.org/10.1007/s10589-014-9664-x

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