A multi-objective bi-level optimisation algorithm with global and local design variables Online publication date: Thu, 31-Dec-2015
by Hong Yoon Kim; Nickolas Vlahopoulos
International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO), Vol. 6, No. 4, 2015
Abstract: An optimisation algorithm that uses a bi-level structure in order to consolidate the solution among multiple discipline optimisations with both local and global design variables is presented. This algorithm is following the bi-level integrated system synthesis decomposition scheme but each discipline level optimisation has its own distinct objective function and constraints. The constraints from all the disciplines are accumulated at the system level optimisation in order to ensure that the final solution is feasible for all disciplines. The structure and the computational process of the algorithm are presented. Two analytical examples are solved for demonstrating the utility of the new algorithm. In all analyses, the optimal results converge on the Pareto front. It is further demonstrated that utilising different weights in the system level objective function allows constructing points on the Pareto front.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook