A Comparative Study of Dynamic Resampling Strategies for Guided Evolutionary Multi-Objective Optimization
2013 (English)In: 2013 IEEE Congress on Evolutionary Computation, CEC 2013, IEEE conference proceedings, 2013, 1826-1835 p.Conference paper (Refereed)
In Evolutionary Multi-objective Optimization many solutions have to be evaluated to provide the decision maker with a diverse choice of solutions along the Pareto-front, in particular for high-dimensional optimization problems. In Simulation-based Optimization the modeled systems are complex and require long simulation times. In addition the evaluated systems are often stochastic and reliable quality assessment of system configurations by resampling requires many simulation runs. As a countermeasure for the required high number of simulation runs caused by multiple optimization objectives the optimization can be focused on interesting parts of the Pareto-front, as it is done by the Reference point-guided NSGA-II algorithm (R-NSGA-II) . The number of evaluations needed for the resampling of solutions can be reduced by intelligent resampling algorithms that allocate just as much sampling budget needed in different situations during the optimization run. In this paper we propose and compare resampling algorithms that support the R-NSGA-II algorithm on optimization problems with stochastic evaluation functions. © 2013 IEEE.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. 1826-1835 p.
Evolutionary multi-objective optimization, simulation-based optimization, guided search, reference point, decision support, stochastic systems, dynamic, resampling
Engineering and Technology Computer Science
Research subject Technology
IdentifiersURN: urn:nbn:se:his:diva-8531DOI: 10.1109/CEC.2013.6557782ISI: 000326235301106ScopusID: 2-s2.0-84881589703ISBN: 978-1-4799-0453-2ISBN: 978-1-4799-0452-5ISBN: 978-1-4799-0454-9OAI: oai:DiVA.org:his-8531DiVA: diva2:653950
2013 IEEE Congress on Evolutionary Computation, June 20-23, Cancún, México