Premium
Multiobjective optimization with a modified simulated annealing algorithm for external beam radiotherapy treatment planning
Author(s) -
Aubry JeanFrançois,
Beaulieu Frédéric,
Sévigny Caroline,
Beaulieu Luc,
Tremblay Daniel
Publication year - 2006
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.2390550
Subject(s) - simulated annealing , mathematical optimization , computer science , multi objective optimization , radiation treatment planning , inverse , optimization problem , software , adaptive simulated annealing , algorithm , mathematics , radiation therapy , medicine , programming language , geometry
Inverse planning in external beam radiotherapy often requires a scalar objective function that incorporates importance factors to mimic the planner's preferences between conflicting objectives. Defining those importance factors is not straightforward, and frequently leads to an iterative process in which the importance factors become variables of the optimization problem. In order to avoid this drawback of inverse planning, optimization using algorithms more suited to multiobjective optimization, such as evolutionary algorithms, has been suggested. However, much inverse planning software, including one based on simulated annealing developed at our institution, does not include multiobjective‐oriented algorithms. This work investigates the performance of a modified simulated annealing algorithm used to drive aperture‐based intensity‐modulated radiotherapy inverse planning software in a multiobjective optimization framework. For a few test cases involving gastric cancer patients, the use of this new algorithm leads to an increase in optimization speed of a little more than a factor of 2 over a conventional simulated annealing algorithm, while giving a close approximation of the solutions produced by a standard simulated annealing. A simple graphical user interface designed to facilitate the decision‐making process that follows an optimization is also presented.