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Post-analysis of multi-objective optimization solutions using decision trees
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Number of Authors: 3
2015 (English)In: Intelligent Data Analysis, ISSN 1088-467X, E-ISSN 1571-4128, Vol. 19, no 2, 259-278 p.Article in journal (Refereed) Published
Abstract [en]

Evolutionary algorithms are often applied to solve multi-objective optimization problems. Such algorithms effectively generate solutions of wide spread, and have good convergence properties. However, they do not provide any characteristics of the found optimal solutions, something which may be very valuable to decision makers. By performing a post-analysis of the solution set from multi-objective optimization, relationships between the input space and the objective space can be identified. In this study, decision trees are used for this purpose. It is demonstrated that they may effectively capture important characteristics of the solution sets produced by multi-objective optimization methods. It is furthermore shown that the discovered relationships may be used for improving the search for additional solutions. Two multi-objective problems are considered in this paper; a well-studied benchmark function problem with on a beforehand known optimal Pareto front, which is used for verification purposes, and a multi-objective optimization problem of a real-world production system. The results show that useful relationships may be identified by employing decision tree analysis of the solution sets from multi-objective optimizations.

Place, publisher, year, edition, pages
2015. Vol. 19, no 2, 259-278 p.
Keyword [en]
Multi-objective optimization, post-optimality analysis, decision trees
National Category
Information Systems
Research subject
Computer and Systems Sciences
URN: urn:nbn:se:su:diva-117324DOI: 10.3233/IDA-150716ISI: 000353062400004OAI: diva2:812510
Available from: 2015-05-19 Created: 2015-05-18 Last updated: 2015-12-01Bibliographically approved
In thesis
1. Learning from Multi-Objective Optimization of Production Systems: A method for analyzing solution sets from multi-objective optimization
Open this publication in new window or tab >>Learning from Multi-Objective Optimization of Production Systems: A method for analyzing solution sets from multi-objective optimization
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The process of multi-objective optimization involves finding optimal solutions to several objective functions. However, these are typically in conflict with each other in many real-world problems, such as production system design. Advanced post-optimization analysis can be used to provide the decision maker with information about the underlying system. The analysis can be based on the combination of simulation-based multi-objective optimization and learning from the obtained solution set. The goal of the analysis is to gain a deeper understanding of the problem at hand, to systematically explore and evaluate different alternatives, and to generate essential information and knowledge to support the decision maker to make more informed decisions in order to optimize the performance of the production system as a whole.

The aim of this work is to explore the possibilities on how post-optimization analysis can be used in order to provide the decision maker with essential information about an underlying system and in what way this information can be presented. The analysis is mainly done on production system development problems, but may also be transferred to other application areas.

The research process of the thesis has been iterative, and the initial approach for post-optimization analysis has been refined several times. The distance-based approach developed in the thesis is used to allow the extraction of information about the characteristics close to a user-defined reference point. The extracted rules are presented to the decision maker both visually, by mapping the rules to the objective space, and textually. The method has been applied to several industrial cases for proof-by-demonstration as well as to an artificial case with information known beforehand to verify the distance-based approach, and the extracted rules have also been used to limit the search space in the optimization. The major finding in the thesis is that to learn from optimization solution sets of production system problems with stochastic behavior, a distance-based approach is advantageous compared with a binary classification of optimal vs. non-optimal solutions.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2014. 109 p.
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 14-002
Data mining, Post-optimization analysis, Production system analysis
National Category
Computer and Information Science
Research subject
Computer and Systems Sciences
urn:nbn:se:su:diva-99018 (URN)978-91-7447-836-5 (ISBN)
Public defence
2014-03-14, Sal A, Forum, Isafjordsgatan 39, Kista, 13:00 (English)

At the time of the doctoral defence the following articles were unpublished and had a status as follows: Paper 5: Epubl ahead of print; Paper 6: Accepted.

Available from: 2014-01-20 Created: 2014-01-10 Last updated: 2015-12-01Bibliographically approved

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