Evolutionary algorithms approach to the solution of mixed integer non-linear programming problems

The global optimization of mixed integer non-linear problems (MINLP), constitutes a major area of research in many engineering applications. In this work, a comparison is made between an algorithm based on Simulated Annealing (M-SIMPSA) and two Evolutionary Algorithms: Genetic Algorithms (GAs) and E...

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Veröffentlicht in:Computers & chemical engineering Jg. 25; H. 2; S. 257 - 266
Hauptverfasser: Costa, Lino, Oliveira, Pedro
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 15.03.2001
Schlagworte:
Evolution strategies
Evolutionary algorithms
Genetic algorithms
Mixed integer non-linear programming
Mixed integer non-linear programming
Evolutionary algorithms
Evolution strategies
Genetic algorithms
ISSN:0098-1354, 1873-4375
Online-Zugang:Volltext
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Zusammenfassung:The global optimization of mixed integer non-linear problems (MINLP), constitutes a major area of research in many engineering applications. In this work, a comparison is made between an algorithm based on Simulated Annealing (M-SIMPSA) and two Evolutionary Algorithms: Genetic Algorithms (GAs) and Evolution Strategies (ESs). Results concerning the handling of constraints, through penalty functions, with and without penalty parameter setting, are also reported. Evolutionary Algorithms seem a valid approach to the optimization of non-linear problems. Evolution Strategies emerge as the best algorithm in most of the problems studied.
ISSN:0098-1354
1873-4375
DOI:10.1016/S0098-1354(00)00653-0