Paper Details: Downloads: 729
Serial Number: P1120548003
Title: A Comparison between Genetic Algorithms and Sequential Quadratic Programming in Solving Constrained Optimization Problems
Authors: Alaa Sheta, HamzaTurabieh
Abstract: There are variety of problems in mechanical, electrical, chemical and aerospace engineering that can be formulated as NonLinear Programming (NLPs). The quality of the developed solution significantly affect the performance of such systems. In this paper, we investigate the ability of Genetic Algorithms (GAs) to tackle the constrained NLPs problems. Experimental results indicated that GAs can effectively solve these types of problems. GAs can overcome many problems encountered by traditional search techniques as gradient based methods. The performance of GAs is compared to the Sequential Quadratic Programming (SQP) method.
Keywords: Constraint Optimization, Genetic Algorithms, Quadratic Sequential Programming, Industrial Processes
Journal/Conference: International Journal of Artificial Intelligence and Machine Learning
Volume: 6
Issue: 1
Submission Date: 12/1/2005 12:00:00 AM
Review Date: 2/1/2006 12:00:00 AM
Publishing Date: 3/1/2006 12:00:00 AM
Article Downloads: 729
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