Table 2 Quality assessment of the results generated by the three initialization methods (\(\alpha = 0.05\)).

From: A spherical vector-based adaptive evolutionary particle swarm optimization for UAV path planning under threat conditions

Initialization

[Algorithm]

Traditional method based on CCS

[PSO]

Traditional method based on SCS

[SGWO,SWOA,SHHO,SDBO,SPSO]

[SAEPSO]

Optimization method in this paper

Environment

Max.

Min.

Mean

Std

T-test

Max.

Min.

Mean

Std

T-test

Max.

Min.

Mean

Std

T-test

Scenario 1

3

0

0.48

0.66

18

4

9.58

2.70

35

10

23.62

5.36

Scenario 2

2

0

0.31

0.54

11

1

4.77

2.08

21

5

11.72

3.50

Scenario 3

1

0

0.05

0.22

2

0

0.27

0.47

3

0

0.56

0.69

Scenario 4

1

0

0.06

0.24

16

2

7.45

3.06

30

7

16.33

4.51

Scenario 5

1

0

0.02

0.14

7

0

2.16

1.22

13

1

5.01

2.46

Scenario 6

1

0

0.01

0.10

6

0

1.32

1.23

8

0

3.11

1.95

  1. Bold indicates the best result among all the algorithms or methods compared in the comparative experiments.