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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| Vydáno v: | Computers & operations research Ročník 36; číslo 7; s. 2217 - 2229 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Kidlington
Elsevier Ltd
01.07.2009
Elsevier Pergamon Press Inc |
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| ISSN: | 0305-0548, 1873-765X, 0305-0548 |
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| Abstract | 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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| AbstractList | 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. 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. [PUBLICATION ABSTRACT] 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. |
| Author | Schlüter, Martin Banga, Julio R. Egea, Jose A. |
| Author_xml | – sequence: 1 givenname: Martin surname: Schlüter fullname: Schlüter, Martin email: martin.schlueter@uni-bayreuth.de organization: Department of Computer Science, University of Bayreuth, 95440 Bayreuth, Germany – sequence: 2 givenname: Jose A. surname: Egea fullname: Egea, Jose A. email: jegea@iim.csic.es organization: Process Engineering Group, Instituto de Investigaciones Marinas (IIM-CSIC), 36208 Vigo, Spain – sequence: 3 givenname: Julio R. surname: Banga fullname: Banga, Julio R. email: julio@iim.csic.es organization: Process Engineering Group, Instituto de Investigaciones Marinas (IIM-CSIC), 36208 Vigo, Spain |
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| Keywords | 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 |
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| Snippet | Two novel extensions for the well known ant colony optimization (ACO) framework are introduced here, which allow the solution of mixed integer nonlinear... |
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| SubjectTerms | 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 |
| Title | Extended ant colony optimization for non-convex mixed integer nonlinear programming |
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