Research Article

A Many-Objective Optimization Algorithm Based on Weight Vector Adjustment

Table 9

Summary of statistical test results in Table 6.

NSGA-III-WAObjective numbervs. NSGA-IIIvs. VAEAvs. RVEAvs. MOEA/Dvs. MOEA/D-M2M

HV3+: 2, =: 4, −: 0+: 6, =: 0, −: 0+: 3, =: 1, −: 2+: 6, =: 0, −: 0+: 3, =: 2, −: 1
5+: 4, =: 1, −: 1+: 4, =: 2, −: 0+: 5, =: 1, −: 0+: 6, =: 0, −: 0+: 5, =: 1, −: 0
8+: 5, =: 1, −: 1+: 6, =: 0, −: 0+: 4, =: 0, −: 2+: 6, =: 0, −: 0+: 6, =: 0, −: 0
10+: 4, =: 2, −: 0+: 6, =: 0, −: 0+: 5, =: 0, −: 1+: 6, =: 0, −: 0+: 6, =: 0, −: 0
15+: 5, =: 1, −: 0+: 4, =: 1, −: 1+: 6, =: 0, −: 0+: 6, =: 0, −: 0+: 6, =: 0, −: 0

Note: “+,” “=,” and “−” represent wins, equal to, and lose.