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International Conference on Natural Computation

ICNC 2006: Advances in Natural Computation pp 340–349Cite as

Accelerating the Radiotherapy Planning with a Hybrid Method of Genetic Algorithm and Ant Colony System

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4222))

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.

This work is supported by a grant from the 973 Project of China (Grant No. 2003CB716106) and grants from NSFC of China (Grant No. 30500140 and 30525030).

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Author information

Authors and Affiliations

  1. School of Life Science and Technology, University of Electronic Science and Technology of China, 610054, Chengdu, China

    Yongjie Li & Dezhong Yao

Authors
  1. Yongjie Li
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  2. Dezhong Yao
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Editor information

Editors and Affiliations

  1. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  3. School of Electronic Engineering, Xidian Univ., P.O. Box, 710071, Xi’an, P.R. China

    Xinbo Gao

  4. College of Mathematics and Information Science, Hebei Normal University, 050016, Shijiazhuang, Hebei, P.R. China

    Jing Liu

  5. Multi-Agent Systems Lab,Department of Computer Science, University of Science and Technology of China, 230026, Hefei, China

    Feng Wu

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© 2006 Springer-Verlag Berlin Heidelberg

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Li, Y., Yao, D. (2006). Accelerating the Radiotherapy Planning with a Hybrid Method of Genetic Algorithm and Ant Colony System. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_42

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  • DOI: https://doi.org/10.1007/11881223_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45907-1

  • Online ISBN: 978-3-540-45909-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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