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

Guillermo Cabrera-Guerrero (), Andrew J. Mason (), Andrea Raith () and Matthias Ehrgott ()
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Guillermo Cabrera-Guerrero: Pontificia Universidad Católica de Valparaíso
Andrew J. Mason: University of Auckland
Andrea Raith: University of Auckland
Matthias Ehrgott: Lancaster University Management School

Journal of Heuristics, 2018, vol. 24, issue 2, No 4, 205-238

Abstract: 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.

Keywords: Intensity modulated radiation therapy; Multi-objective beam angle optimisation; Beam angle configuration; Pareto local search (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10732-018-9365-1

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