By Topic

A Hybrid Ant Colony Differential Evolution and its application to water resources problems

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
$33 $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)
Musrrat Ali ; Department of Paper Technology, Indian Institute of Technology Roorkee, Saharanpur campus, India ; Millie Pant ; Ajith Abraham

Differential Evolution (DE) is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from slow convergence rate and takes large computational time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This research introduces a modified differential evolution, called Ant Colony Differential Evolution, ACDE. The ACDE algorithm initializes the population using opposition based learning, in mutation phase it applies random localization technique and it simulates the movement of ants to refine the best solution found in each generation. Also, it maintains a single set of population while updating the population for next generation. ACDE validated on a test bed of 7 benchmark problems and two real life problems and the numerical results are compared with original DE. It is found that ACDE requires less computational effort to locate global optimal solution without compromising with the quality of solution.

Published in:

Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on

Date of Conference:

9-11 Dec. 2009