By Topic

An Improved Hybrid Genetic Algorithm for Solving Multi-modal Function Global Optimization Problem

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

3 Author(s)
Dahai Zhang ; Univ. of Wuhan, Wuhan ; Qijuan Chen ; Jingyu Liu

In this paper we propose an improved hybrid genetic algorithm to overcome the deficiencies of the conventional algorithms in solving multi-modal function global optimization problems. The improved algorithm combines the niche genetic algorithm and steepest descent method: niche elimination operator is introduced to the algorithm to keep the diversity of the population and to ensure the search space is complete and more global optimization solutions can be obtained; the steepest descent operator is used to strengthen local search ability and improve the search accuracy and search efficiency. The new Algorithm is applied to optimizing multi-modal function, and the fact shows that the improved genetic algorithm can find all of the solutions of the complex multi-modal function and it has better optimization ability and precision than the old one.

Published in:

Automation and Logistics, 2007 IEEE International Conference on

Date of Conference:

18-21 Aug. 2007