Research Article

The Effects of Using Chaotic Map on Improving the Performance of Multiobjective Evolutionary Algorithms

Table 3

The normalized results of .

ZDT1ZDT2ZDT3ZDT4ZDT6
cimcimcimcimcim

Baker0.6170.0800.0490.6820.1490.0940.6680.1090.0030.220−0.0400.1670.5670.105−0.090
Cat−0.0600.0760.1010.0130.0840.024−0.0070.0900.1740.1910.1090.158−0.0280.022−0.096
Circle−0.142−0.040−0.0160.013−0.121−0.007−0.1530.028−0.0160.151−0.0690.098−0.176−0.072−0.002
Cubic−0.5440.064−0.025−0.626−0.0410.006−0.334−0.0490.0770.032−0.7810.071−0.2880.0400.008
Gauss0.3070.5130.0890.3060.5070.0700.4540.5850.0370.1590.0050.1910.114−0.0100.132
ICMIC−0.4150.5580.132−0.2800.6090.144−0.2950.5100.0040.003−0.3800.088−0.1620.2520.163
Logistic0.0700.2420.1890.0720.204−0.0310.0120.1580.1270.017−0.8190.1830.1520.1290.132
Sinusoidal0.07710.6160.12110.7420.16910.734−0.091−1−0.1380.1480.6881
Tent0.6550.1770.0620.7040.103−0.0050.7310.043−0.0880.190−0.008−0.0580.5690.124−0.003
Zaslavskii−0.0510.4620.032−0.1500.5180.064−0.0860.4990.110−0.103−0.3390.108−0.0600.1740.183