A simulated annealing approach to the solution of minlp problems

An algorithm (M-SIMPSA) suitable for the optimization of mixed integer non-linear programming (MINLP) problems is presented. A recently proposed continuous non-linear solver (SIMPSA) is used to update the continuous parameters, and the Metropolis algorithm is used to update the complete solution vec...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computers & chemical engineering Jg. 21; H. 12; S. 1349 - 1364
Hauptverfasser: Cardoso, M.F., Salcedo, R.L., de Azevedo, S.Feyo, Barbosa, D.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 1997
Elsevier
Schlagworte:
Applications of mathematics to chemical engineering. Modeling. Simulation. Optimization
Applied sciences
Chemical engineering
Exact sciences and technology
Mixed integer non-linear programming
Simplex
Simulated annealing
Simplex
Mixed integer non-linear programming
Simulated annealing
Mixed integer programming
Non linear programming
Algorithm
Optimization
ISSN:0098-1354, 1873-4375
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:An algorithm (M-SIMPSA) suitable for the optimization of mixed integer non-linear programming (MINLP) problems is presented. A recently proposed continuous non-linear solver (SIMPSA) is used to update the continuous parameters, and the Metropolis algorithm is used to update the complete solution vector of decision variables. The M-SIMPSA algorithm, which does not require feasible initial points or any problem decomposition, was tested with several functions published in the literature, and results were compared with those obtained with a robust adaptive random search method. For ill-conditioned problems, the proposed approach is shown to be more reliable and more efficient as regards the overcoming of difficulties associated with local optima and in the ability to reach feasibility. The results obtained reveal its adequacy for the optimization of MINLP problems encountered in chemical engineering practice.
ISSN:0098-1354
1873-4375
DOI:10.1016/S0098-1354(97)00015-X