Extended ant colony optimization for non-convex mixed integer nonlinear programming

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 M...

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Bibliographic Details
Published in:Computers & operations research Vol. 36; no. 7; pp. 2217 - 2229
Main Authors: Schlüter, Martin, Egea, Jose A., Banga, Julio R.
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.07.2009
Elsevier
Pergamon Press Inc
Subjects:
Ant colony optimization
Applied sciences
Benchmarks
Constrained optimization
Exact sciences and technology
Global optimization
Hybrid metaheuristics
Integer programming
Mathematical programming
MINLP
Operational research and scientific management
Operational research. Management science
Optimization
Oracle penalty method
Studies
Oracle penalty method
Ant colony optimization
MINLP
Hybrid metaheuristics
Global optimization
Constrained optimization
Non convex programming
Global optimum
Mixed integer programming
Convex programming
Penalty method
Integer programming
Heuristic method
Swarm intelligence
Oracle
Metamodel
Non convex analysis
ISSN:0305-0548, 1873-765X, 0305-0548
Online Access:Get full text
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Summary: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.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2008.08.015