Clustering aided approach for decision making in computationally expensive multiobjective optimization
2009 (English)In: Optimization Methods and Software, ISSN 1055-6788, Vol. 24, no 2, 157-174 p.Article in journal (Refereed) Published
Typically, industrial optimization problems need to be solved in an efficient, multiobjective and global manner, because they are often computationally expensive (as function values are typically based on simulations), they may contain multiple conflicting objectives, and they may have several local optima. Solving such problems may be challenging and time consuming when the aim is to find the most preferred Pareto optimal solution.
In this study, we propose a method where we use an advanced clustering technique to reveal essential characteristics of the approximation of the Pareto optimal set, which has been generated beforehand. Thus, the decision maker (DM) is involved only after the most time consuming computation is finished. After the initiation phase, a moderate number of cluster prototypes projected to the Pareto optimal set is presented to the DM to be studied. This allows him/her to rapidly gain an overall understanding of the main characteristics of the problem without placing too much cognitive load on the DM. Furthermore, we also suggest some ways of applying our approach to different types of problems and demonstrate it with an example related to internal combustion engine design.
Place, publisher, year, edition, pages
2009. Vol. 24, no 2, 157-174 p.
multiple objective optimization, multicriteria optimization, Pareto optimality, efficient methods, learning-oriented methods
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-82886DOI: 10.1080/10556780802525331ISI: 000263717600001OAI: oai:DiVA.org:kth-82886DiVA: diva2:498555
QC 201202142012-02-122012-02-122012-02-14Bibliographically approved