A Reduced Order Memetic Algorithm for Constraint Optimization in Radiation Therapy Treatment Planning

Abstract

In this paper, a novel hybrid genetic algorithm is presented for optimization in radiation therapy treatment planning. The proposed Reduced Order Memetic Algorithm (ROMA) is a combination of an evolutionary multi-objective optimization algorithm and gradient-based local search in a reduced order space. The gradient-based optimizer is used for a fast local search and is a variant of the sequential quadratic programming method. The execution time of the local search is improved by applying dynamically a principal component analysis to the solutions generated by the genetic optimizer and reducing the high-dimensionality search-space. In particular, for intensity modulated radiation therapy (IMRT) we observed reduction of the search-space dimensionality from several hundreds to less than twenty. Latin hypercube sampling was used to define the weights of the scalarization scheme for the local search fitness function for each individual solution. The proposed hybrid algorithm obtains efficiently a set of diverse non-dominated solutions for a large scale multi-objective problem such as in radiation treatment planning optimization. The applicability of the proposed algorithm is demonstrated for IMRT optimization for a case of prostate cancer.