An augmented Lagrangian ant colony based method for constrained optimization

Asghar Mahdavi () and Mohammad Shiri

Computational Optimization and Applications, 2015, vol. 60, issue 1, 263-276

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 ( $$\hbox {ACO}_\mathrm{R}$$ ACO 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. Copyright Springer Science+Business Media New York 2015

Keywords: Ant colony; Augmented Lagrangian function (ALF); Constrained optimization problems (COPs) (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s10589-014-9664-x

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