蚁群优化算法
遗传算法
数学优化
计算
计算机科学
启发式
人口
蚁群
最优化问题
放射治疗计划
算法
放射治疗
人工智能
数学
医学
环境卫生
内科学
作者
Yongjie Li,Wufan Chen,Dezhong Yao,Jiancheng Zheng,Jonathan Yao
标识
DOI:10.1109/cec.2005.1554871
摘要
Intensity-modulated radiotherapy (IMRT) is being increasingly used for treatment of malignant cancer. Beam angle optimization (BAO) is an important problem in IMRT. In this paper, an emerging population-based meta-heuristic algorithm named ant colony optimization (ACO) is introduced to solve the BAO problem. In the proposed algorithm, a multi-layered graph is designed to map the BAO problem to ACO, and a heuristic function based on the beam's-eye-view dosimetrics (BEVD) score is introduced. In order to verify the feasibility of the presented algorithm, a clinical prostate tumor case is employed, and the preliminary results demonstrate that ACO appears more effcient than genetic algorithm (GA) and can find the optimal beam angles within a clinically acceptable computation time.
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