数学优化
模拟退火
最大值和最小值
算法
帕累托原理
多目标优化
可行区
计算机科学
集合(抽象数据类型)
数学
停留时间
临床心理学
医学
数学分析
程序设计语言
作者
Natasa Milickovic,Michael Lahanas,M. Papagiannopoulou,Nikolaos Zamboglou,Dimos Baltas
标识
DOI:10.1088/0031-9155/47/13/306
摘要
In high dose rate (HDR) brachytherapy, conventional dose optimization algorithms consider multiple objectives in the form of an aggregate function that transforms the multiobjective problem into a single-objective problem. As a result, there is a loss of information on the available alternative possible solutions. This method assumes that the treatment planner exactly understands the correlation between competing objectives and knows the physical constraints. This knowledge is provided by the Pareto trade-off set obtained by single-objective optimization algorithms with a repeated optimization with different importance vectors. A mapping technique avoids non-feasible solutions with negative dwell weights and allows the use of constraint free gradient-based deterministic algorithms. We compare various such algorithms and methods which could improve their performance. This finally allows us to generate a large number of solutions in a few minutes.
科研通智能强力驱动
Strongly Powered by AbleSci AI