Constrained optimization via genetic algorithms

Abdollah Homaifar, Charlene X. Qi, Steven H. Lai

研究成果: Article同行評審

573 引文 斯高帕斯(Scopus)

摘要

This paper presents an application of genetic algorithms (GAs) to nonlinear constrained optimization. GAs are general purpose optimization algorithms which apply the rules of natural genetics to explore a given search space. When GAs are applied to nonlinear constrained problems, constraint handling becomes an important issue. The proposed search algorithm is realized by GAs which utilize a penalty function in the objective function to account for violation. This extension is based on systematic multi-stage assignments of weights in the penalty method as opposed to single-stage assignments in sequential unconstrained minimization. The experimental results are satisfactory and agree well with those of the gradient type methods.

原文English
頁(從 - 到)242-254
頁數13
期刊Simulation
62
發行號4
出版狀態Published - 1994 四月 1

All Science Journal Classification (ASJC) codes

  • 軟體
  • 建模與模擬
  • 電腦繪圖與電腦輔助設計

指紋

深入研究「Constrained optimization via genetic algorithms」主題。共同形成了獨特的指紋。

引用此