A Reduced Variable Neighbourhood Search Algorithm for the Beam Angle Selection Problem in Radiation Therapy

Gutierrez, Maicholl; Cabrera‐Guerrero, Guillermo

doi:10.1109/sccc51225.2020.9281180

Cited by 1 publication

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Within this kind of hybrid strategy, we can find genetic algorithms [19][20][21], particle swarm optimisation [22], ant colony systems [23,24] and simulated annealing [5,[25][26][27][28]. Local search strategies have also been applied to the single-objective BAO problem [3,16,[29][30][31][32][33][34]. Other methods such as response surface [35], surrogate-based methods [36], guided pattern search [37] and mixed-integer programming approaches [38][39][40] have also been proposed.…”

Section: The Multi-objective Beam Angle Optimisation Problem Mo-bao: Literature Reviewmentioning

confidence: 99%

“…(d) Cumulative hypervolume for all algorithms for semi-random initial BACs (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29). (e) Hypervolume for all algorithms for random initial BACs (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44). (f) Cumulative hypervolume for all algorithms for random initial BACs (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44).…”

mentioning

confidence: 99%

“…(e) Hypervolume for all algorithms for random initial BACs (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44). (f) Cumulative hypervolume for all algorithms for random initial BACs (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Comparing Multi-Objective Local Search Algorithms for the Beam Angle Selection Problem

Cabrera‐Guerrero

Lagos

2022

Mathematics

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: The Multi-objective Beam Angle Optimisation Problem Mo-bao: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Comparing Multi-Objective Local Search Algorithms for the Beam Angle Selection Problem

Cabrera‐Guerrero

Lagos

2022

Mathematics

Self Cite

View full text Add to dashboard Cite

show abstract

A Reduced Variable Neighbourhood Search Algorithm for the Beam Angle Selection Problem in Radiation Therapy

Cited by 1 publication

References 26 publications

Comparing Multi-Objective Local Search Algorithms for the Beam Angle Selection Problem

Comparing Multi-Objective Local Search Algorithms for the Beam Angle Selection Problem

Contact Info

Product

Resources

About