An adaptive conjugate gradient accelerated evolutionary algorithm for multi-objective spot optimization in cancer intensity modulated proton therapy

Abstract

Intensity-modulate proton therapy is one of the most advanced cancer treatment techniques due to the Bragg peak characteristics of proton radiation. The personalized demand of different patients requires treatment optimization methods to quickly provide diverse treatment plans to select the best plan for a patient. However, most existing treatment optimization methods are transformed the multi-objective optimization problem into a single optimization problem. Moreover, the radiation physicists may adjust the objective weights repeatedly to produce a set of high-quality treatment plans. To address this problem, this paper proposes an adaptive conjugate gradient accelerated evolutionary algorithm (ACG-EA) to generate a set of diverse high-quality treatment plans simultaneously. The conjugate gradient method is employed as a directional mutation operator to accelerate the search process in the hybrid mutation operation. In addition, the weight parameters of the conjugate gradient are automatically updated based on the diversity and convergence of the current population. Compared with five representative multi-objective evolutionary algorithms, the experimental results have shown the competitive performance of the proposed ACG-EA on the hypervolume and dose-volume histogram indicators in six clinical cancer cases.