Constrained optimization via genetic algorithms

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

Research output: Contribution to journalArticlepeer-review

540 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)242-254
Number of pages13
JournalSimulation
Volume62
Issue number4
Publication statusPublished - 1994 Apr 1

All Science Journal Classification (ASJC) codes

  • Software
  • Modelling and Simulation
  • Computer Graphics and Computer-Aided Design

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