Abstract

http://ssrn.com/abstract=913667
 
 

References (14)



 
 

Citations (8)



 


 



Some Experiments on Fitting of Gielis Curves by Simulated Annealing and Particle Swarm Methods of Global Optimization


Sudhanshu K. Mishra


North-Eastern Hill University (NEHU)

July 3, 2006


Abstract:     
In this paper an attempt has been made to fit the Gielis curves (modified by various functions) to simulated data. The estimation has been done by two methods - the Classical Simulated Annealing (CSA) and the Particle Swarm (PS) methods - of global optimization. The Repulsive Particle Swarm optimization algorithm has been used. It has been found that both methods are quite successful in fitting the modified Gielis curves to the data. However, the lack of uniqueness of Gielis parameters to data (from which they are estimated) is corroborated.

Number of Pages in PDF File: 7

Keywords: Gielis super-formula, supershapes, Simulated annealing, Particle Swarm method, Repulsive Particle Swarm method of Global optimization, nonlinear programming, multiple sub-optimum, global, local optima, fit, data, empirical, estimation, parameters, curve fitting, Shape Recovery

JEL Classification: C15, C63

working papers series





Download This Paper

Date posted: July 12, 2006  

Suggested Citation

Mishra, Sudhanshu K., Some Experiments on Fitting of Gielis Curves by Simulated Annealing and Particle Swarm Methods of Global Optimization (July 3, 2006). Available at SSRN: http://ssrn.com/abstract=913667 or http://dx.doi.org/10.2139/ssrn.913667

Contact Information

Sudhanshu K. Mishra (Contact Author)
North-Eastern Hill University (NEHU) ( email )
NEHU Campus
Shillong, 793022
India
03642550102 (Phone)
HOME PAGE: http://www.nehu-economics.info
Feedback to SSRN


Paper statistics
Abstract Views: 1,922
Downloads: 303
Download Rank: 58,116
References:  14
Citations:  8

© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollo2 in 0.360 seconds