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Extended ant colony optimization for non-convex mixed integer nonlinear programming
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DOI：10.1016/j.cor.2008.08.015.png)
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Abstract
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Two novel extensions for the well known ant colony optimization (ACO) framework are introduced here, which allow the solution of mixed integer nonlinear programs (MINLPs). Furthermore, a hybrid implementation (ACOmi) based on this extended ACO framework, specially developed for complex non-convex MINLPs, is presented together with numerical results. These extensions on the ACO framework have been developed to serve the needs of practitioners who face highly non-convex and computationally costly MINLPs. The performance of this new method is evaluated considering several non-convex MINLP benchmark problems and one real-world application. The results obtained by our implementation substantiate the success of this new approach. (C) 2008 Elsevier Ltd. All rights reserved.
Keywords:
Ant colony optimization
MINLP
Global optimization
Hybrid metaheuristics
Constrained optimization
Oracle penalty method
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