Research Article

A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems

Table 2

Comparison of results of AGPSO and four current MOPSOs on WFG1-WFG9 using HV.

ProblemObjAGPSONMPSOMaPSOdMOPSOSMPSO

WFG157.87e − 01(4.27e − 02)5.97e − 01(2.89e − 02) − 3.34e − 01(2.58e − 02) − 2.98e − 01(3.44e − 03) − 3.00e − 01(1.01e − 03) − 
88.75e − 01(3.77e − 02)7.00e − 01(1.38e − 01) − 2.74e − 01(2.45e − 02) − 2.35e − 01(6.26e − 03) − 2.50e − 01(1.54e − 03) − 
109.45e − 01(3.19e − 03)8.07e − 01(1.58e − 01) − 2.53e − 01(1.98e − 02) − 2.13e − 01(9.18e − 03) − 2.30e − 01(1.29e − 03) − 

WFG259.76e − 01(5.76e − 03)9.69e − 01(5.44e − 03) − 9.70e − 01(4.70e − 03) − 8.94e − 01(1.28e − 02) − 8.85e − 01(1.31e − 02) − 
89.82e − 01(4.12e − 03)9.83e − 01(3.56e − 03)+9.84e − 01(2.86e − 03)+8.92e − 01(1.48e − 02) − 8.48e − 01(2.52e − 02) − 
109.93e − 01(1.99e − 03)9.91e − 01(4.85e − 03)∼9.90e − 01(1.48e − 03) − 9.00e − 01(1.76e − 02) − 8.54e − 01(2.55e − 02) − 

WFG356.59e − 01(5.16e − 03)6.13e − 01(1.94e − 02) − 6.12e − 01(1.64e − 02) − 5.89e − 01(1.05e − 02) − 5.75e − 01(7.32e − 03) − 
86.78e − 01(6.20e − 03)5.77e − 01(1.52e − 02) − 5.96e − 01(1.88e − 02) − 4.94e − 01(4.58e − 02) − 5.84e − 01(1.51e − 02) − 
106.91e − 01(7.88e − 03)5.83e − 01(2.73e − 02) − 5.97e − 01(2.58e − 02) − 3.56e − 01(9.58e − 02) − 5.92e − 01(1.52e − 02) − 

vWFG457.74e − 01(5.34e − 03)7.64e − 01(6.09e − 03) − 7.22e − 01(2.96e − 02) − 6.51e − 01(1.08e − 02) − 5.09e − 01(1.73e − 02) − 
89.00e − 01(8.05e − 03)8.62e − 01(1.21e − 02) − 7.98e − 01(1.76e − 02) − 3.88e − 01(7.86e − 02) − 5.26e − 01(2.13e − 02) − 
109.37e − 01 (6.64e − 03)8.82e − 01(8.54e − 03) − 8.27e − 01(1.32e − 02) − 3.15e − 01(1.34e − 01) − 5.52e − 01(2.19e − 02) − 

WFG557.41e − 01 (6.12e − 03)7.35e − 01(3.73e − 03) − 6.63e − 01(1.18e − 02) − 5.85e − 01(1.53e − 02) − 4.30e − 01(1.18e − 02) − 
88.61e − 01 (4.35e − 03)8.32e − 01(6.58e − 03) − 6.91e − 01(1.12e − 02) − 1.81e − 01(8.43e − 02) − 4.52e − 01(1.08e − 02) − 
108.88e − 01(8.29e − 03)8.65e − 01(7.92e − 03) − 6.99e − 01(1.76e − 02) − 2.43e − 02(1.54e − 02) − 4.65e − 01(1.24e − 02) − 

WFG657.50e − 01(8.96e − 03)7.24e − 01(3.96e − 03) − 7.09e − 01(4.50e − 03) − 6.74e − 01(7.93e − 03) − 5.43e − 01(1.66e − 02) − 
88.72e − 01(1.26e − 02)8.08e − 01(5.70e − 03) − 8.03e − 01(2.66e − 03) − 4.89e − 01(4.87e − 02) − 6.49e − 01(8.38e − 03) − 
109.09e − 01(1.36e − 02)8.34e − 01(2.03e − 03) − 8.32e − 01(1.80e − 03) − 4.66e − 01(2.96e − 02) − 7.03e − 01(1.37e − 02) − 

WFG757.90e − 01(2.12e − 03)7.96e − 01(2.98e − 03)+7.89e − 01(4.61e − 03)∼5.39e − 01(2.72e − 02) − 4.44e − 01(2.06e − 02) − 
89.20e − 01(3.39e − 03)9.04e − 01(3.71e − 03) − 8.73e − 01(1.40e − 02) − 2.25e − 01(3.93e − 02) − 4.73e − 01(1.23e − 02) − 
109.55e − 01(9.11e − 03)9.36e − 01(6.97e − 03) − 9.17e − 01(1.17e − 02) − 2.07e − 01(6.36e − 02) − 5.14e − 01(2.10e − 02) − 

WFG856.63e − 01(3.75e − 03)6.73e − 01(5.41e − 03)+6.29e − 01(1.34e − 02) − 4.27e − 01(1.57e − 02) − 3.72e − 01(2.09e − 02) − 
87.85e − 01(7.46e − 03)7.97e − 01(3.79e − 02)∼6.91e − 01(2.13e − 02)-9.55e − 02(3.20e − 02) − 4.11e − 01(9.29e − 03) − 
108.36e − 01(1.30e − 02)8.62e − 01(4.12e − 02)+7.33e − 01(2.04e − 02) − 7.73e − 02(1.27e − 02) − 4.52e − 01(1.90e − 02) − 

WFG956.57e − 01(3.82e − 03)7.35e − 01(7.32e − 03)+6.23e − 01(7.01e − 03) − 5.97e − 01(1.99e − 02) − 4.46e − 01(1.61e − 02) − 
87.30e − 01(9.96e − 03)7.65e − 01(6.80e − 02)+6.55e − 01(1.17e − 02) − 2.73e − 01(6.06e − 02) − 4.55e − 01(2.39e − 02) − 
107.49e − 01(9.03e − 03)7.47e − 01(6.72e − 02) − 6.56e − 01(1.30e − 02) − 2.24e − 01(6.96e − 02) − 4.76e − 01(1.23e − 02) − 

Best/all20/276/271/270/270/27

Worse/similar/better19 −/2∼/6+25 −/1∼/1+27 −/0∼/0+27 −/0∼/0+