| CAO Rui-fen,LI Quo-li,SONG Gang,et al.An improved fast and elitist multi-objective genetic algorithm-ANSGA-Ⅱ for multi-objective optimization of inverse radiotherapy treatment planning[J].Chinese Journal of Radiological Medicine and Protection,2007,27(5):467-470 |
| An improved fast and elitist multi-objective genetic algorithm-ANSGA-Ⅱ for multi-objective optimization of inverse radiotherapy treatment planning |
| Received:December 05, 2006 |
| DOI： |
| KeyWords:Inverse planning Multi-objective optimization Multi-objective evolutionary optimization algorithm NSGA-Ⅱ |
| FundProject:国家“973”计划项目(2006CB708307),安徽省自然科学基金项目(070413081) |
| Author Name | Affiliation | | CAO Rui-fen | Institute of Plasma Physics, Chinese Academy of Science, Hefei 230031, China | | LI Quo-li | Institute of Plasma Physics, Chinese Academy of Science, Hefei 230031, China | | SONG Gang | Institute of Plasma Physics, Chinese Academy of Science, Hefei 230031, China | | 赵攀 | Institute of Plasma Physics, Chinese Academy of Science, Hefei 230031, China | | 林辉 | Institute of Plasma Physics, Chinese Academy of Science, Hefei 230031, China | | 吴爱东 | Institute of Plasma Physics, Chinese Academy of Science, Hefei 230031, China | | 黄晨昱 | Institute of Plasma Physics, Chinese Academy of Science, Hefei 230031, China | | 吴宜灿 | Institute of Plasma Physics, Chinese Academy of Science, Hefei 230031, China |
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| Abstract:: |
| Objective To provide a fast and effective multi-objective optimization algorithm for inverse radiotherapy treatment planning system. Methods Non-dominated Sorting Genetic Algorithm-NSGA-Ⅱ is a representative of multi-objective evolutionary optimization algorithms and excels the others. The paper produces ANSGA-Ⅱ that makes use of advantage of NSGA-Ⅱ, and uses adaptive crossover and mutation to improve its flexibility; according the character of inverse radiotherapy treatment planning, the paper uses the pre-known knowledge to generate individuals of every generation in the course of optimization, which enhances the convergent speed and improves efficiency. Results The example of optimizing average dose of a sheet of CT,including PTV、OAR、NT, proves the algorithm could find satisfied solutions in several minutes. Conclusions The algorithm could provide clinic inverse radiotherapy treatment planning system with selection of optimization algorithms. |
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