ABSTRACT
Objective Because of high precision and mild side effects,intensity-modulated proton therapy (IMPT) has become a hot spot in the radiotherapy field.Nevertheless,the precision of IMPT is extremely sensitive to the range uncertainties.In this paper,a novel robust optimization method was proposed to reduce the effect of range uncertainty upon IMPT.Methods Firstly,the robust optimization model was established which contained three types of range including the increased range,the normal range and the shortened range.The objective function was expressed in quadratic function.The organ dose contribution matrix of each range was calculated by proton pencil beam algorithm.The range deviation was discretized and the probability of each range was obtained based on the Gauss distribution function.Finally,the conjugate gradient method was adopted to find the optimal solution to make the actual dose coverage of the target area and the organs at risk distributed within the expected dose as possible.Results The 3 sets of simulation tests provided by the AAPM TG-119 Report were utilized to evaluate the effectiveness of this method:nasopharyngeal carcinoma,prostate and "C"-type cases.Compared with conventional IMPT optimization approach,this novel method was less sensitive to the range uncertainty.When the range deviation occurred,the dose coverage of the target area and organs at risk of the nasopharyngeal carcinoma and prostate cases almost reached the expected dose,and the high dose coverage of the target area and organs at risk protection were improved in the"C"-type cases.Conclusions To compensate for the range uncertainty,this novel method can enhance the dose coverage of the target area and reduce the dose coverage of the organs at risk.
ABSTRACT
In engineering designed systems it is commonly considered that mathematical models, variables, and parameters are sufficiently reliable, i.e., there are no errors in modeling and estimation. However, the systems to be optimized can be sensitive to small changes in the designed variables causing significant changes in the objective function. Robust optimization is an approach for modeling optimization problems under uncertainty in which the modeler aims to find decisions that are optimal for the worst-case realization of the uncertainties within a given set of values. In this contribution, a self-adaptive heuristic optimization method, namely the Self-Adaptive Differential Evolution (SADE), is evaluated. Differently from the canonical Differential Evolution algorithm (DE), the SADE strategy is able to update the required parameters such as population size, crossover parameter, and perturbation rate, dynamically. This is done by considering a defined convergence rate on the evolution process of the algorithm in order to reduce the number of evaluations of the objective function. For illustration purposes, the SADE strategy is associated with the Mean Effective Concept (MEC) for insertion robustness, is applied to minimize forces applied in cables used for the rehabilitation of the human lower limbs by determining the positioning of motors. The results show that the methodology that was proposed (SADE+MEC) appears as an interesting strategy for the treatment of robust optimization problems.
No projeto de sistemas de engenharia é comum considerar que os modelos, as variáveis e os parâmetros são confiáveis, isto é, não apresentam erros de modelagem e de estimação. Entretanto, os sistemas a serem otimizados podem ser sensíveis a pequenas alterações nas variáveis de projeto causando significativas modificações no vetor de objetivos. Otimização robusta é uma abordagem para modelagem de problemas de otimização sob incerteza em que o modelador tem como objetivo encontrar decisões que são ideais para o pior caso de realização das incertezas dentro de um determinado conjunto de valores. Neste trabalho, um método de otimização heurística auto-adaptável, nomeada Self-Adaptive Differential Evolution (SADE), é avaliada. Diferentemente do algoritmo de Evolução Diferencial, a estratégia SADE é capaz de atualizar os parâmetros necessários, tais como o tamanho da população, o parâmetro de passagem e taxa de perturbação, de forma dinâmica. Isto é feito considerando uma taxa de convergência definido no processo de evolução do algoritmo, a fim de reduzir o número de avaliações da função objetivo. Para fins de ilustração, a estratégia SADE associado ao conceito de média efetiva, para inserção da robustez, é aplicada para minimizar as forças aplicadas nos cabos da estrutura robótica utilizada para a reabilitação dos membros inferiores humanos, determinando o posicionamento dos atuadores. Os resultados mostram que o método proposto neste trabalho configura-se como uma estratégia interessante para o tratamento de problemas de otimização robustos.