Efficient Ranking and Selection for Stochastic Simulation Model based on Hypothesis Test

Cited 3 time in webofscience Cited 0 time in scopus
  • Hit : 116
  • Download : 0
This paper proposes an efficient ranking and selection algorithm for a stochastic simulation model. The proposed algorithm evaluates an uncertainty to assess whether the observed best design is truly optimal, based on hypothesis test. Then, it conservatively allocates additional simulation resources to reduce uncertainty with an intuitive allocation rule in each iteration of a sequential procedure. This conservative allocation provides a high robustness to noise for the algorithm. The results of several experiments demonstrated its improved performance compared to the other algorithms in the literature. The algorithm can be an efficient way to solve optimization problems in real-world systems where significant noise exists.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2018-09
Language
English
Article Type
Article
Keywords

PARTICLE SWARM OPTIMIZATION; COMPUTING BUDGET ALLOCATION; SEQUENTIAL-PROCEDURES; ORDINAL OPTIMIZATION; OPTIMAL SUBSET; SYSTEM; ENVIRONMENT; 2-STAGE; NUMBER

Citation

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, v.48, no.9, pp.1555 - 1565

ISSN
2168-2216
DOI
10.1109/TSMC.2017.2679192
URI
http://hdl.handle.net/10203/245561
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 3 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0