Pattern mining-based evolutionary multi-objective algorithm for beam angle optimization in intensity-modulated radiotherapy

Abstract

Evolutionary multi-objective algorithms have been applied to beam angle optimization (called BAO) for generating diverse trade-off radiotherapy treatment plans. However, their performance is not so effective due to the ignorance of using the specific clinical knowledge that can be obtain intuitively by clinical physicist. To address this issue, we suggest a pattern mining based evolutionary multi-objective algorithm called PM-EMA, in which two strategies for using the knowledge are proposed to accelerate the speed of population convergence. Firstly, to discover the potential beam angle distribution and discard the worse angles, the pattern mining strategy is used to detect the maximum and minimum sets of beam angles in non-dominated solutions of the population and utilize them to generate offspring to enhance the convergence. Moreover, to improve the quality of initial solutions, a tailored population initialization strategy is proposed by using the score of beam angles defined by this study. The experimental results on six clinical cancer cases demonstrate the superior performance of the proposed algorithm over six representative algorithms.