.png){kind=icon}
.png){kind=icon}
Return
Genetic algorithms optimization for normalized normal constraint method under Pareto construction
.png){kind=icon}
PRE
AI
DOI：10.1016/j.advengsoft.2008.04.004.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents the resolution of multiobjective optimization problems as a tool in engineering design. In the literature, the solutions of this problems are based on the Pareto frontier construction. Therefore, Substantial efforts have been made in recent years to develop methods for the construction of Pareto frontiers that guarantee uniform distribution and exclude the non-Pareto and local Pareto points. The normalized normal constraint is a recent contribution that generates a well-distributed Pareto frontier. Nevertheless, these methods are susceptible of improvement or modifications to obtain the same level of results more efficiently. This paper proposes a modification of the original normalized normal constraint method using a genetic algorithms in the optimization task. The results presented in this paper show a suitable behavior for the genetic algorithms method compared to classical Gauss-Newton optimization methods which are used by the original normalized normal constraint method. (C) 2008 Elsevier Ltd. All rights reserved.
Keywords:
Pareto frontier
Multiobjective optimization
Non-linear optimization
Genetic algorithms
Engineering
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.7
Papers:
3.3K
Citations:
1.2W