Solving Multiobjective Flexible Scheduling Problem by Improved DNA Genetic Algorithm

被引：0
|
作者：
Li, Jianxiong [1 ,2 ]
Nie, Shuzhi [3 ]
Yang, Fan [1 ,2 ]
机构：
[1] South China Univ Technol, Sch Software Eng, Guangzhou, Guangdong, Peoples R China
[2] Guangzhou Vocat & Tech Inst Ind & Commerce, Dept Comp Sci & Eng, Guangzhou, Guangdong, Peoples R China
[3] South China Univ Technol, Sch Mech & Automot Eng, Guangzhou, Guangdong, Peoples R China
来源：
2010 2ND INTERNATIONAL ASIA CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS (CAR 2010), VOL 2 | 2010年
关键词：
DNA computation; RNA computation; improved genetic algorithm; pareto sorting; multi-objective scheduling;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Build mathematical models for multi-objective flexible scheduling problems, put forward a improved genetic algorithm based on DNA computation, combine it with Pareto non-dominated sorting method to work out multi-objective flexible scheduling optimization problems. In order to ensure the diversity of optimal solution sets, RNA four-digit-system encoder mode and genetic operator based on DNA computation were adopted, designed subsection crossover and dynamic mutation operation. Through simulation, test the designed algorithm performance; by comparing with conventional genetic algorithm test results, it proved the efficiency of the algorithm.
引用
收藏
页码：458 / 461
页数：4
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