Cart (Loading....) | Create Account
Close category search window
 

Global optimization using a multi-point type quasi-chaotic optimization method with the simultaneous perturbation gradient approximation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Okamoto, T. ; Grad. Sch. of Eng., Chiba Univ., Chiba, Japan ; Hirata, H.

In this study, we propose a new global optimization method in which the simultaneous perturbation gradient approximation is introduced into a multi-point type chaotic optimization method. The multi-point type chaotic optimization method, which has been proposed recently, is a global optimization method to solve unconstrained optimization problems in which multiple search points which implement global search driven by a chaotic gradient dynamic model are synchronized to their elite search points. The chaotic optimization method uses gradient as a driving force for search points. Hence, its application is confined to a class of problems in which gradient of the objective function can be computed. In this study, we introduce the simultaneous perturbation gradient approximation into the multi-point type chaotic optimization method in order to compute gradient approximately so that the chaotic optimization method can be applied to a class of problems whose objective function values only can be computed. Then, we confirm effectiveness of the proposed method through applications to several unconstrained multi-peaked optimization problems with 100 variables comparing to other major meta-heuristics.

Published in:

Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on

Date of Conference:

10-13 Oct. 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.