非支配排序均匀遗传算法

Non-dominated sorting uniform generic algorithm

根据第二代非支配排序遗传算法(NSGAⅡ)的不足之处,提出了一种新的多目标遗传算法——非支配排序均匀遗传算法(NSUGA)。新算法采用了多父本多点交叉方式,同时将均匀设计的思想用于算法的交叉操作;新算法还对拥挤距离的计算过程和算法的终止条件进行了改进。通过两个多目标优化测试函数的仿真计算对比,显示NSUGA算法在求解精度、计算效率和避免算法陷于局部最优解方面均优于NSGA II算法。

According to the shortage of non-dominated sorting generic algorithm Ⅱ（NSGA Ⅱ）,put forward one new multi-objective generic algorithm called non-dominated sorting uniform generic algorithm（NSUGA）.In the new algorithm,adopted the crossing mode of multi-parent and multi-point,and used the idea of uniform design in the algorithm＇s crossing action,improved moreover the crowding-distance calculation and the algorithm＇s terminating condition.Simulation results on two test problems show that NSUGA is better in the precision of seeking pareto solutions,the efficiency of computing and the avoidance of gaining local optimal solutions than NSGA II.