By Topic

Multi-objective optimization using self-adaptive differential evolution algorithm

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

4 Author(s)
Huang, V.L. ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore ; Zhao, S.Z. ; Mallipeddi, R. ; Suganthan, P.N.

In this paper, we propose a multiobjective self-adaptive differential evolution algorithm with objective-wise learning strategies (OW-MOSaDE) to solve numerical optimization problems with multiple conflicting objectives. The proposed approach learns suitable crossover parameter values and mutation strategies for each objective separately in a multi-objective optimization problem. The performance of the proposed OW-MOSaDE algorithm is evaluated on a suit of 13 benchmark problems provided for the CEC2009 MOEA Special Session and Competition ( on Performance Assessment of Constrained/Bound Constrained Multi-Objective Optimization Algorithms.

Published in:

Evolutionary Computation, 2009. CEC '09. IEEE Congress on

Date of Conference:

18-21 May 2009