Accelerating the Radiotherapy Planning with a Hybrid Method of Genetic Algorithm and Ant Colony System

Li, Yongjie; Yao, Dezhong

doi:10.1007/11881223_42

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…While heuristics seek promising BACs, exact algorithms can compute the optimal solution of the associated FMO problem for a specific BAC. 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].…”

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

confidence: 99%

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

Cabrera‐Guerrero

Lagos

2022

Mathematics

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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.

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

confidence: 99%

“…(c) Hypervolume for all algorithms for semi-random initial BACs (14-29). (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).…”

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confidence: 99%

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

Cabrera‐Guerrero

Lagos

2022

Mathematics

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“…During the last three decades or so, many researchers have worked on the problem of finding efficient treatment plans for radiation therapy for cancer treatment. Most of their efforts have been focused on the problem of finding the optimal fluence map that can be delivered to the patients given a predefined BAC (see, e.g., [2][3][4][5]). Unfortunately, significantly less attention has been paid to the problem of finding the best BAC.…”

Section: Intensity Modulated Radiation Therapy: An Overviewmentioning

confidence: 99%

“…In Li et al [11], the authors propose an ant colony optimisation algorithm that uses a similar representation to Li et al [10]. In Li and Yao [3], authors propose a hybrid algorithm that combines ant colony optimisation and genetic algorithms. The authors claim that their approach is faster than previously implemented genetic algorithms.…”

Section: Intensity Modulated Radiation Therapy: An Overviewmentioning

confidence: 99%

Local Search Algorithms for the Beam Angles’ Selection Problem in Radiotherapy

Cabrera‐Guerrero

Rodríguez

Lagos

et al. 2018

Mathematical Problems in Engineering

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One important problem in radiation therapy for cancer treatment is the selection of the set of beam angles radiation will be delivered from. A primary goal in this problem is to find a beam angle configuration (BAC) that leads to a clinically acceptable treatment plan. Further, this process must be done within clinically acceptable times. Since the problem of selecting beam angles in radiation therapy is known to be extremely hard to solve as well as time-consuming, both exact algorithms and population-based heuristics might not be suitable to solve this problem. In this paper, we compare two matheuristic methods based on local search algorithms, to approximately solve the beam angle optimisation problem (BAO). Although the steepest descent algorithm is able to find locally optimal BACs for the BAO problem, it takes too long before convergence, which is not acceptable in clinical practice. Thus, we propose to use a next descent algorithm that converges quickly to good quality solutions although no (local) optimality guarantee is given. We apply our two matheuristic methods on a prostate case which considers two organs at risk, namely, the rectum and the bladder. Results show that the matheuristic algorithm based on the next descent local search is able to quickly find solutions as good as the ones found by the steepest descent algorithm.

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“…While in Li et al. (2005b) a pure ant colony system is combined with a conjugate gradient algorithm, the same authors present a hybrid strategy combining a genetic algorithm and an ant colony system to solve the BAO problem in Li and Yao (). The authors propose this hybrid approach in order to obtain a balance between exploration and exploitation attributes of the hybrid algorithm.…”

Section: Introductionmentioning

confidence: 99%

A matheuristic approach to solve the multiobjective beam angle optimization problem in intensity‐modulated radiation therapy

Ehrgott

Mason

et al. 2016

Int Trans Operational Res

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Selecting a suitable set of beam angles is an important but difficult task in intensity-modulated radiation therapy (IMRT) for cancer treatment. From a single objective point of view, this problem, known as the beam angle optimization (BAO) problem, is solved by finding a beam angle configuration (BAC) that leads to the best dose distribution, according to some objective function. As there exists a trade-off between the main goals in IMRT (to irradiate the tumor according to some prescription and to avoid surrounding healthy tissue), it makes sense to solve this problem from a multiobjective (MO) point of view. When doing so, a solution of the BAO problem is no longer a single BAC, but instead a set of BACs that lead to a set of dose distributions that, depending on both dose prescription and physician preferences, can be selected as the preferred treatment. We solve this MO problem using a two-phase strategy. During the first phase, a deterministic local search algorithm is used for selecting a set of locally optimal BACs, according to a single-objective function. During this search, an optimal dose distribution for each BAC, with respect to the single-objective function, is calculated using an exact nonlinear programming algorithm. During the second phase, a set of nondominated points is generated for each promising locally optimal BAC and a dominance analysis among them is performed. The output of the procedure is a set of (approximately) efficient BACs that lead to good dose distributions. To demonstrate the viability of the method, the two-phase strategy is applied to a prostate case.

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Accelerating the Radiotherapy Planning with a Hybrid Method of Genetic Algorithm and Ant Colony System

Cited by 10 publications

References 20 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

Local Search Algorithms for the Beam Angles’ Selection Problem in Radiotherapy

A matheuristic approach to solve the multiobjective beam angle optimization problem in intensity‐modulated radiation therapy

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