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Pareto local search algorithms for the multi-objective beam angle optimisation problem

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Abstract

Due to inherent trade-offs between tumour control and sparing of organs at risk, optimisation problems arising in intensity modulated radiation therapy planning are naturally modelled as multi-objective optimisation problems. Nevertheless, the vast majority of studies in the literature consider single objective approaches to these problems. The beam angle optimisation problem, that we address ion this paper, is one of these problems. It attempts to identify “good” beam angle configurations that allow the delivery of efficient treatment plans. In this paper two bi-objective local search algorithms are developed for the bi-objective beam angle optimisation problem, namely Pareto local search (PLS) and a variation of PLS we call adaptive PLS (aPLS). Both algorithms are able to find a set of (approximately) efficient beam angle configurations. While the PLS algorithm aims to find a set of efficient BACs by performing a very focused search over a specific region of the objective space, the aPLS algorithm aims to produce a set of efficient BACs that are well-distributed over the objective space. We test both algorithms on two prostate cancer cases and compare them to our previously proposed single objective local search algorithm.

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Acknowledgements

The authors wish to acknowledge the contribution of NeSI high-performance computing facilities to the results of this research. NZ’s national facilities are provided by the NZ eScience Infrastructure and funded jointly by NeSI’s collaborator institutions and through the Ministry of Business, Innovation and Employment’s Research Infrastructure programme. URL https://www.nesi.org.nz. G. Cabrera-Guerrero wishes to thank both FONDECYT/INICIACION/11170456 and CONICYT/REDI170036 projects for partially support this research.

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Authors and Affiliations

  1. Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaiso, Chile

    Guillermo Cabrera-Guerrero

  2. Department of Engineering Science, University of Auckland, Auckland, New Zealand

    Guillermo Cabrera-Guerrero, Andrew J. Mason & Andrea Raith

  3. Department of Management Science, Lancaster University Management School, Lancaster, UK

    Matthias Ehrgott

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  1. Guillermo Cabrera-Guerrero
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Correspondence to Guillermo Cabrera-Guerrero.

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Cabrera-Guerrero, G., Mason, A.J., Raith, A. et al. Pareto local search algorithms for the multi-objective beam angle optimisation problem. J Heuristics 24, 205–238 (2018). https://doi.org/10.1007/s10732-018-9365-1

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