Abstract
In intensity-modulated radiation therapy, treatment planners aim to irradiate the tumour according to a medical prescription while sparing surrounding organs at risk as much as possible. Although this problem is inherently a multi-objective optimisation (MO) problem, most of the models in the literature are single-objective ones. For this reason, a large number of single-objective algorithms have been proposed in the literature to solve such single-objective models rather than multi-objective ones. Further, a difficulty that one has to face when solving the MO version of the problem is that the algorithms take too long before converging to a set of (approximately) non-dominated points. In this paper, we propose and compare three different strategies, namely random PLS (rPLS), judgement-function-guided PLS (jPLS) and neighbour-first PLS (nPLS), to accelerate a previously proposed Pareto local search (PLS) algorithm to solve the beam angle selection problem in IMRT. A distinctive feature of these strategies when compared to the PLS algorithms in the literature is that they do not evaluate their entire neighbourhood before performing the dominance analysis. The rPLS algorithm randomly chooses the next non-dominated solution in the archive and it is used as a baseline for the other implemented algorithms. The jPLS algorithm first chooses the non-dominated solution in the archive that has the best objective function value. Finally, the nPLS algorithm first chooses the solutions that are within the neighbourhood of the current solution. All these strategies prevent us from evaluating a large set of BACs, without any major impairment in the obtained solutions’ quality. We apply our algorithms to a prostate case and compare the obtained results to those obtained by the PLS from the literature. The results show that algorithms proposed in this paper reach a similar performance than PLS and require fewer function evaluations.
Highlights
Intensity-modulated radiation therapy (IMRT) is one of the most common techniques in cancer treatment
Unfilled circles correspond to the sample points belonging to those beam angle configuration (BAC) that were not explored
Work In this paper, the multi-objective optimisation (MO)-beam angle optimisation (BAO) problem is solved using three MO local search (MO-LS) algorithms derived from the well-known Pareto Local Search algorithm
Summary
Intensity-modulated radiation therapy (IMRT) is one of the most common techniques in cancer treatment It aims to eradicate tumour cells by irradiating the tumour region without compromising surrounding normal tissue and organs at risk (OARs). A sequencing problem needs to be solved to control the movement of the multi-leaf collimator leaves during delivery of the optimised fluence [1,3]. It is clear from the process above that selecting high-quality BAC(s) will allow us to obtain better treatment plans during the computation of the FMO problem. We focus on the problem of selecting a set of beam angles to produce high-quality treatment plans, while ignoring the MLC problem
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