Solving Phase Equilibrium Problems by Means of Avoidance-Based Multiobjectivization

  • Mike Preuss
  • Simon Wessing
  • Günter Rudolph
  • Gabriele Sadowski

Abstract

Phase-equilibrium problems are good examples for real-world engineering optimization problems with a certain characteristic. Despite their low dimensionality, finding the desired optima is difficult as their basins of attraction are small and surrounded by the much larger basin of the global optimum, which unfortunately resembles a physically impossible and therefore unwanted solution. We tackle such problems by means of a multiobjectivization-assisted multimodal optimization algorithm which explicitly uses problem knowledge concerning where the sought solutions are not in order to find the desired ones. The method is successfully applied to three phase-equilibrium problems and shall be suitable also for tackling difficult multimodal optimization problems from other domains.

CEA

cellular evolutionary algorithm

CMA

covariance matrix adaptation

DE

differential evolution

EA

evolutionary algorithm

EC

evolutionary computation

EMOA

evolutionary multiobjective algorithm

ES

evolution strategy

GA

genetic algorithm

GP

genetic programming

LLE

liquid–liquid equilibrium

MMA

multimemetic algorithm

MOAMO

multiobjectivization-assisted multimodal optimization

NSGA

nondominated sorting genetic algorithm

PC-SAFT

perturbed chain statistical associating fluid theory

SMS-EMOA

S-metric selection evolutionary multiobjective algorithm

TS

tabu search

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Mike Preuss
    • 1
  • Simon Wessing
    • 2
  • Günter Rudolph
    • 2
  • Gabriele Sadowski
    • 3
  1. 1.Inst. WirtschaftsinformatikWWU MünsterMünsterGermany
  2. 2.Fak. InformatikTechnische Universität DortmundDortmundGermany
  3. 3.Bio- und ChemieingenieurwesenTechnische Universität DortmundDortmundGermany

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