By Topic

Solving Constrained Multi-objective Optimization Problems Using Non-dominated Ranked Genetic 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

3 Author(s)
Al Jadaan, O. ; Dept.CSE, Osmania Univ., Hyderabad ; Rao, C.R. ; Rajamani, L.

A criticism of evolutionary algorithms might be the lack of efficient and robust generic methods to handle constraints. The most widespread approach for constrained search problems is to use penalty methods, because of their simplicity and ease of implementation. Nonetheless, the most difficult aspect of the penalty function approach is to find an appropriate penalty parameters. In this paper, a method combining the new non-dominated ranked genetic algorithm (NRGA), with a parameterless penalty approach are exploited to devise the search to find Pareto optimal set of solutions. The new parameterless penalty and the nondominated ranked genetic algorithm (PP-NRGA) continuously find better Pareto optimal set of solutions. This new algorithm have been evaluated by solving four test problems, reported in the multi-objective evolutionary algorithm (MOEA) literature. Performance comparisons based on quantitative metrics for accuracy, coverage, and spread are presented.

Published in:

Modelling & Simulation, 2009. AMS '09. Third Asia International Conference on

Date of Conference:

25-29 May 2009