A Reduced-Cost SMS-EMOA Using Kriging, Self-Adaptation, and Parallelization

Jan-Willem Klinkenberg, Michael T. M. Emmerich (), André H. Deutz, Ofer M. Shir and Thomas Bäck
Additional contact information
Michael T. M. Emmerich: Leiden University

A chapter in Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems, 2010, pp 301-311 from Springer

Abstract: Abstract The SMS-EMOA is a simple and powerful evolutionary metaheuristic for computing approximations to Pareto front based on the dominated hypervolume indicator (S-metric). However, as other state-of-the-art metaheuristics, it consumes a high number of function evaluations in order to compute accurate approximations. To reduce its total computational cost and response time for problems with time consuming evaluators, we suggest three adjustments: Step-size adaptation, Kriging metamodeling, and Steady-State Parallelization. We show that all these measures contribute to the acceleration of the SMS-EMOA on continuous benchmark problems as well as on a application problem – the quantum mechanical optimal control with shaped laser pulses.

Keywords: SMS-EMOA; Evolutionary multiobjective optimization; Expensive evaluation; Self-adaptation; Metamodels. (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-642-04045-0_26

Ordering information: This item can be ordered from
http://www.springer.com/9783642040450

DOI: 10.1007/978-3-642-04045-0_26

Access Statistics for this chapter

More chapters in Lecture Notes in Economics and Mathematical Systems from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().