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Finding Robust Solutions Using Local Search

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Journal of Mathematical Modelling and Algorithms

Abstract

This paper investigates how a local search metaheuristic for continuous optimisation can be adapted so that it finds broad peaks, corresponding to robust solutions. This is relevant in problems in which uncertain or noisy data is present. When using a genetic or evolutionary algorithm, it is standard practice to perturb solutions once before evaluating them, using noise from a given distribution. This approach however, is not valid when using population-less techniques like local search and other heuristics that use local search. For those algorithms to find robust solutions, each solution needs to be perturbed and evaluated several times, and these evaluations need to be combined into a measure of robustness. In this paper, we examine how many of these evaluations are needed to reliably find a robust solution. We also examine the effect of the parameters of the noise distribution. Using a simple tabu search procedure, the proposed approach is tested on several functions found in the literature.

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Authors and Affiliations

  1. University of Antwerp, Prinsstraat 13, 2000, Antwerpen, Belgium

    Kenneth Sörensen

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  1. Kenneth Sörensen
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Sörensen, K. Finding Robust Solutions Using Local Search. Journal of Mathematical Modelling and Algorithms 3, 89–103 (2004). https://doi.org/10.1023/B:JMMA.0000026710.74315.3e

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  • DOI: https://doi.org/10.1023/B:JMMA.0000026710.74315.3e

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