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

Jan-Willem Klinkenberg, Michael T. M. Emmerich (emmerich@liacs.nl), André H. Deutz, Ofer M. Shir and Thomas Bäck
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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
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-642-04045-0_26

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

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