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Knowledge-driven reference-point based multi-objective optimization: First results
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-3124-0077
2019 (English)In: GECCO 2019 Companion: Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion / [ed] Manuel López-Ibáñez, Association for Computing Machinery (ACM), 2019, p. 2060-2063Conference paper, Published paper (Refereed)
Abstract [en]

Multi-objective optimization problems in the real world often involve a decision maker who has certain preferences for the objective functions. When such preferences can be expressed as a reference point, the goal of optimization changes from generating a complete set of Pareto-optimal solutions to generating a small set of non-dominated solutions close to the reference point. Reference-point based optimization algorithms are used for this purpose. The preferences of the decision maker in the objective space can be interpreted as knowledge in the decision space. Extracting this knowledge iteratively from the solutions generated during optimization, and feeding it back into the optimization algorithm can in principle improve convergence towards the reference point. Since the knowledge is extracted during runtime, this approach is termed as online knowledge-driven optimization. In this paper a recent knowledge discovery technique called flexible pattern mining is used to extract explicit rules that are used to generate new solutions in R-NSGA-II. The performance of the proposed FPM-R-NSGA-II is demonstrated on 3, 5 and 10 objective DTLZ problems. In addition to converging to a set of preferred solutions, FPM-R-NSGA-II also converges to a set of explicit rules which describe the decision maker's preferences in the decision space. 

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2019. p. 2060-2063
Keywords [en]
Decision making, Knowledge discovery, Multi-objective optimization, Reference-point, Data mining, Evolutionary algorithms, Iterative methods, Pareto principle, Solution mining, Decision maker's preferences, Knowledge discovery techniques, Multi-objective optimization problem, Nondominated solutions, Optimization algorithms, Pareto optimal solutions, Preferred solutions, Reference points, Multiobjective optimization
National Category
Computer Sciences
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
URN: urn:nbn:se:his:diva-17581DOI: 10.1145/3319619.3326911Scopus ID: 2-s2.0-85070592837ISBN: 978-1-4503-6748-6 (print)OAI: oai:DiVA.org:his-17581DiVA, id: diva2:1345343
Conference
Genetic and Evolutionary Computation Conference, GECCO 2019, Prague, Czech Republic, July 13-17, 2019
Available from: 2019-08-23 Created: 2019-08-23 Last updated: 2019-09-17Bibliographically approved

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Smedberg, Henrik

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Citation style
  • apa
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