Accelerating the radiotherapy planning with a hybrid method of genetic algorithm and ant colony system

Authors:
Yongjie Li;Dezhong Yao
Affiliations:
School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China;School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Year:
2006

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Abstract

Computer-aided radiotherapy planning within a clinically acceptable time has the potential to improve the therapeutic ratio by providing the optimized and customized treatment plans for the tumor patients. In this paper, a hybrid method is proposed to accelerate the beam angle optimization (BAO) in the intensity modulated radiotherapy (IMRT) planning. In this hybrid method, the genetic algorithm (GA) is used to find the rough distribution of the solution, i.e., to give the initial pheromone distribution for the following ant colony system (ACS) optimization. Then, the ACS optimization is implemented to find the precise solution of the BAO problem. The comparisons of the optimization on a clinical nasopharynx case with GA, ACS and the hybrid method show that the proposed algorithm can obviously improve the computation efficiency.