Computational Intelligence and Neuroscience

Research Article

A Many-Objective Optimization Algorithm Based on Weight Vector Adjustment

Table 11

Summary of statistical test results in Table 10.
NSGA-III-WAObjective numbervs. NSGA-IIIvs. VAEAvs. RVEAvs. MOEA/Dvs. MOEA/D-M2M
IGD3+: 4, =: 0, −: 0+: 3, =: 0, −: 1+: 2, =: 0, −: 2+: 3, =: 0, −: 1+: 4, =: 0, −: 0
5+: 4, =: 0, −: 0+: 4, =: 0, −: 0+: 3, =: 0, −: 1+: 4, =: 0, −: 0+: 3, =: 0, −: 1
8+: 3, =: 0, −: 1+: 1, =: 1, −: 2+: 3, =: 0, −: 1+: 4, =: 0, −: 0+: 3, =: 1, −: 0
10+: 3, =: 0, −: 1+: 2, =: 1, −: 1+: 2, =: 1, −: 1+: 4, =: 0, −: 0+: 3, =: 1, −: 0
15+: 4, =: 0, −: 0+: 2, =: 1, −: 1+: 2, =: 1, −: 1+: 4, =: 0, −: 0+: 3, =: 0, −: 1
Note: “+,” “=,” and “−” represent wins, equal to, and lose.