摘要
在算法MISA中使用了Pareto支配、P竞争选择法、退化个体的选择及实数编码策略。在仿真试验中,将算法MISA与NSGAⅡ所得模拟实验结果进行对比。通过比较发现,在三维目标优化方面,算法MISA无论是在个体的多样性,还是收敛性上都要比NSGAⅡ好,这种算法是一种可行的、有效的解决多目标优化问题的方法。
Obtain Pareto optimization solutions fast is one of the important purposes of multi-objective optimization.The artificial immune system can restrain or promote antibodies production to achieve this purpose.The proposed approach used Pareto dominance、P competition selection、 degenerate solutions selection and real encode.Simulation results showed that the MISA was able to find much better diversity of solutions and better convergence near the true Pareto-optimal front in three objectives compared to NSGAⅡ th...
出处
《武汉理工大学学报》
CAS
CSCD
北大核心
2008年第2期116-118,共3页
Journal of Wuhan University of Technology
基金
国家自然科学基金(60572015)
国家“973”重大基础研究前期研究专项(2004CCA02500)
关键词
人工免疫算法
多目标优化
PARETO支配
解的多样性
artificial immune system
multi-objective optimization
Pareto dominance
diversity of individual
