Mathematical Problems in Engineering
Volume 2005 (2005), Issue 2, Pages 165-173
doi:10.1155/MPE.2005.165
Abstract
Constrained nonlinear programming problems often arise in many
engineering applications. The most well-known optimization methods
for solving these problems are sequential quadratic programming
methods and generalized reduced gradient methods. This study
compares the performance of these methods with the genetic
algorithms which gained popularity in recent years due to advantages
in speed and robustness. We present a comparative study that is performed
on fifteen test problems selected from the literature.