An augmented Lagrangian ant colony based method for constrained optimization

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 (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\hbox {ACO}_\mathrm{R}$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <msub> <mtext>ACO</mtext> <mi mathvariant="normal">R</mi> </msub> </math> </EquationSource> </InlineEquation>) 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