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A Local Search Based Evolutionary Multi-Objective Optimization Approach for Fast and Accurate Convergence
University of Jyväskylä.
Department of Mathematical Information Technology, University of Jyväskylä.
2008 (English)In: PARALLEL PROBLEM SOLVING FROM NATURE - PPSN X, PROCEEDINGS / [ed] G. Rudolph, T. Jansen, S. Lucas, C. Poloni, N. Beume, Springer Berlin/Heidelberg, 2008, 815-824 p.Conference paper (Refereed)
Abstract [en]

A local search method is often introduced in an evolutionary optimization technique to enhance it's speed and accuracy of convergence to true optimal solutions. In multi-objective optimization problems, the implementation of a local search is a non-trivial task, as determining a goal for the local search in presence of multiple conflicting objectives becomes a difficult proposition. In this paper, we borrow a multiple criteria decision making concept of employing A reference point based approach of minimizing an achievement scalarizing function and include it as a search operator of an EMO algorithm. Simulation results with NSGA-II on a number of two to four-objective problems with and without the local search approach clearly show the importance of local search in aiding a computationally faster and more accurate convergence to Pareto-optimal solutions. The concept is now ready to be coupled with a faster and more accurate procedure to make I,he overall procedure a competitive algorithm for multi-objective optimization.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2008. 815-824 p.
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-83160DOI: 10.1007/978-3-540-87700-4_81ISI: 000260673000081OAI: diva2:498754
10th International Conference on Parallel Problem Solving from Nature. Dortmund, GERMANY. SEP 13-17, 2008
QC 20120213Available from: 2012-02-12 Created: 2012-02-12 Last updated: 2012-02-13Bibliographically approved

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Miettinen, Kaisa
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ReferencesLink to record
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