.png){kind=icon}
Return
Evolutionary algorithms approach to the solution of mixed integer non-linear programming problems
.png){kind=icon}
PRE
AI
DOI：10.1016/S0098-1354(00)00653-0.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2001 Elsevier Science Ltd. All rights reserved.
Keywords:
evolutionary algorithms
genetic algorithms
evolution strategies
mixed integer non-linear programming
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
3.9
Papers:
8.1K
Citations:
1.7W
Organization
No organization information available
Cited Papers
The effects of crosslink density on thermo-mechanical properties of shape-memory hydro-epoxy resin
PREAI
ShareSave
ShareSavePericardial prostaglandin biosynthesis prevents the increased incidence of reperfusion-induced ventricular fibrillation produced by efferent sympathetic stimulation in dogs..png)
Circulation
OAAI
CirculationIF0
OAAIShareSaveShareSaveShareSaveShareSave
View more