Efficient Direct Aperture Optimization via Evolutionary Computation With Customized Variation Operators

As a popular technique for intensity-modulated radiotherapy, direct aperture optimization (DAO) aims at generating treatment plans for cancer cases without the relaxation of optimization models. Conventional DAO methods are mainly based on mathematical programming, which can quickly generate a single plan but is inefficient in offering multiple candidate plans for clinical doctors. Recently, metaheuristics have been employed by DAO to offer many plans at a time; however, they are criticized for showing low efficiency in the evaluation and repair of iteratively generated offspring solutions. To provide an efficient DAO method, this work proposes a multi-objective evolutionary algorithm with customized variation operators. These operators can not only generate promising plans but also ensure their validity, and thus the search efficiency is improved by the acceleration of convergence and the elimination of repair operations. The experimental results demonstrate that the proposed DAO method is superior over existing heuristics and metaheuristics in terms of both effectiveness and efficiency.