A framework for finding robust optimal solutions over time

Yaochu JIN, Ke TANG, Xin YU, Bernhard SENDHOFF, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

68 Citations (Scopus)

Abstract

Dynamic optimization problems (DOPs) are those whose specifications change over time, resulting in changing optima. Most research on DOPs has so far concentrated on tracking the moving optima (TMO) as closely as possible. In practice, however, it will be very costly, if not impossible to keep changing the design when the environment changes. To address DOPs more practically, we recently introduced a conceptually new problem formulation, which is referred to as robust optimization over time (ROOT). Based on ROOT, an optimization algorithm aims to find an acceptable (optimal or sub-optimal) solution that changes slowly over time, rather than the moving global optimum. In this paper, we propose a generic framework for solving DOPs using the ROOT concept, which searches for optimal solutions that are robust over time by means of local fitness approximation and prediction. Empirical investigations comparing a few representative TMO approaches with an instantiation of the proposed framework are conducted on a number of test problems to demonstrate the advantage of the proposed framework in the ROOT context. © 2012 Springer-Verlag.
Original languageEnglish
Pages (from-to)3-18
Number of pages16
JournalMemetic Computing
Volume5
Issue number1
Early online date3 Sept 2012
DOIs
Publication statusPublished - Mar 2013
Externally publishedYes

Bibliographical note

This work is partly supported by an EPSRC Grant (no.EP/E058884/1) on “Evolutionary Algorithms for Dynamic Optimisation Problems: Design, Analysis and Applications”, the European Union 7th Framework Program under Grant No 247619, a Grant from Honda Research Institute Europe, two National Natural Science Foundation Grants (No. 61028009 and No. 61175065), and the National Natural Science Foundation of Anhui Province (No. 1108085J16).

Keywords

  • Dynamic optimisation
  • Evolutionary algorithms
  • Fitness approximation
  • Particle swarm optimisation
  • Robust optimisation over time (Root)

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