On the Effect of Scalarising Norm Choice in a ParEGO implementation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10173)


Computationally expensive simulations play an increasing role in engineering design, but their use in multi-objective optimization is heavily resource constrained. Specialist optimizers, such as ParEGO, exist for this setting, but little knowledge is available to guide their configuration. This paper uses a new implementation of ParEGO to examine three hypotheses relating to a key configuration parameter: choice of scalarising norm. Two hypotheses consider the theoretical trade-off between convergence speed and ability to capture an arbitrary Pareto front geometry. Experiments confirm these hypotheses in the bi-objective setting but the trade-off is largely unseen in many-objective settings. A third hypothesis considers the ability of dynamic norm scheduling schemes to overcome the trade-off. Experiments using a simple scheme offer partial support to the hypothesis in the bi-objective setting but no support in many-objective contexts. Norm scheduling is tentatively recommended for bi-objective problems for which the Pareto front geometry is concave or unknown.


Expensive optimization Surrogate-based optimization Performance evaluation 



This work was supported by Jaguar Land Rover and the UK-EPSRC grant EP/L025760/1 as part of the jointly funded Programme for Simulation Innovation. The authors thank Joshua Knowles for discussions on ParEGO and surrogate-based optimization that helped inspire the research directions in this paper.


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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Automatic Control and Systems EngineeringUniversity of SheffieldSheffieldUK
  2. 2.Department of Mechanical EngineeringOrt Braude College of EngineeringKarmielIsrael

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