Author/Authors :
A. Afshar، A. Afshar نويسنده دانشكده مهندسي دانشگاه علم و صنعت ايران A. Afshar, A. Afshar , S. Madadgar، S. Madadgar نويسنده Department of Civil and Environmental Engineering, Portland State University S. Madadgar, S. Madadgar , M.R. Jalali، M.R. Jalali نويسنده Department of Civil Engineering, Tafresh Branch, Islamic Azad University, and Payeshsad Consulting Engineering Co., Iran M.R. Jalali, M.R. Jalali , F. Sharifi، F. Sharifi نويسنده MBACandidate 2011, Rotman School ofManagement, University of Toronto, Canada F. Sharifi, F. Sharifi
Abstract :
Ant colony optimization algorithms (ACOs) have been basically introduced to discrete variable
problems and applied to different research domains in several engineering fields. Meanwhile,
abundant studies have been already involved to adapt different ant models to continuous search
spaces. Assessments indicate competitive performance of ACOs on discrete or continuous
domains. Therefore, as potent optimization algorithms, it is encouraging to involve ant models to
mixed-variable domains which simultaneously tackle discrete and continuous variables. This paper
introduces four ant-based methods to solve mixed-variable problems. Each method is based upon
superlative ant algorithms in discrete and/or continuous domains. Proposed methods’ performances
are then tested on a set of three mathematical functions and also a water main design problem in
engineering field, which are elaborately subject to linear and non-linear constraints. All proposed
methods perform rather satisfactorily on considered problems and it is suggested to further extend
the application of methods to other engineering studies.
