A Memetic Differential Evolution Algorithm Based on Dynamic Preference for Constrained Optimization Problems

Table 4

Results of the approximate two-sample 𝑡-test between the other two algorithms and our MDEDP for 12 instances.

Algorithms

𝑡-test

g02

g05

g07

g09

g10

g13

g14

g17

g18

g19

g21

g23

Var2-MDEDP

𝑡0

−𝟐.𝟖𝟎^b

1.97

𝟓.𝟎𝟐^a

𝟓.𝟒𝟖^a

𝟒.𝟐𝟓^a

𝟑.𝟓𝟏^a

𝟓.𝟒𝟕^a

𝟏𝟑.𝟎𝟑^a

−𝟐.𝟏𝟏^a

𝟕.𝟏𝟕^a

—

𝟏𝟏.𝟑𝟎^a

𝑓

44

30

30

30

30

30

30

53

30

30

—

30

SAMSDE-MDEDP

𝑡0

−𝟐.𝟎𝟕^b

1.52

𝟓.𝟗𝟐^a

𝟖.𝟎𝟓^a

𝟒.𝟗𝟏^a

𝟑.𝟔𝟓^a

𝟏𝟗.𝟕𝟎^a

𝟖.𝟑𝟖^a

−𝟐.𝟏𝟏^a

𝟕.𝟒𝟖^a

𝟔.𝟔𝟎^a

𝟏𝟑.𝟗𝟑^a

𝑓

51

30

30

30

30

30

30

49

30

30

57

30