Table 7 The comparison of obtained solutions for fixed-dimensional multimodal functions.

From: Hybrid rice optimization algorithm inspired grey wolf optimizer for high-dimensional feature selection

F

D

EI

PSO

SCA

WOA

HRO

GWO

MSGWO1

I-GWO

MSGWO2

HRO-GWO

F7

2

Best

9.980e−01

9.980e−01

9.980e−01

9.980e−01

9.980e−01

9.980e−01

9.980e−01

9.980e−01

9.980e−01

Avg

9.980e−01

1.064e+00

1.886e+00

9.980e−01

4.518e+00

1.296e+00

1.097e+00

3.844e+00

9.980e−01

Wor.

9.980e−01

2.982e+00

1.096e+01

9.980e−01

1.267e+01

2.982e+00

2.982e+00

1.076e+01

9.980e−01

Std

2.629e−07

3.562e−01

1.864e+00

1.900e−09

4.457e+00

4.324e−01

4.324e−01

3.570e+00

0.000e+00

F8

2

Best

3.000000

3.000000

3.000000

2.999999

3.000000

3.000000

3.000000

3.000000

2.999999

Avg

3.055987

3.000007

3.000004

2.999999

3.000004

3.000006

3.000000

3.000001

2.999999

Wor.

3.341624

3.000030

3.000035

2.999999

3.000017

3.000031

3.000000

3.000004

2.999999

Std

8.164e−02

8.581e−06

7.592e−06

5.790e−16

3.672e−06

7.628e−06

2.329e−07

9.602e−07

3.765e−16

F9

3

Best

− 3.862782

− 3.862025

− 3.862781

− 3.862782

− 3.862782

− 3.862782

− 3.862782

− 3.862782

− 3.862782

Avg

− 3.862781

− 3.855774

− 3.861263

− 3.862782

− 3.862186

− 3.862183

− 3.862037

− 3.859235

− 3.862782

Wor.

− 3.862778

− 3.852876

− 3.854901

− 3.862782

− 3.854901

− 3.854901

− 3.854930

− 3.854900

− 3.862782

Std

2.559e−05

2.928e−03

2.450e−03

8.882e−16

1.941e−03

1.732e−03

2.193e−03

3.920e−03

4.200e−15

F10

6

Best

− 3.315432

− 3.170276

− 3.322003

− 3.322021

− 3.322020

− 3.322019

− 3.322020

− 3.322020

− 3.322021

Avg

− 3.244581

− 2.976402

− 3.260889

− 3.285593

− 3.240311

− 3.183589

− 3.222477

− 3.214057

− 3.262561

Wor.

− 3.075045

− 1.921499

− 3.077863

− 3.198163

− 3.086691

− 3.015834

3.133509

− 3.086690

− 3.203102

Std

7.368e−02

2.404e−01

7.802e−02

5.473e−02

8.585e−02

8.967e−02

5.123e−02

8.442e−02

5.946e−02

F11

4

Best

− 9.942076

− 4.981549

− 10.153152

− 10.153200

− 10.153185

− 10.153112

− 10.153138

− 5.055197

− 10.153200

Avg

− 7.672811

− 2.985946

− 9.642503

− 7.162445

−9.645591

− 7.863472

− 8.883117

− 5.055195

− 9.689436

Wor.

− 3.254147

− 0.879667

− 5.055189

− 2.630472

− 5.055196

− 5.055190

− 5.055197

− 5.055193

− 5.055198

Std

1.845e+00

1.795e+00

1.529e+00

3.663e+00

1.523e+00

2.531e+00

2.200e+00

1.098e−06

1.814e+00

F12

4

Best

− 10.536325

− 8.444841

− 10.536371

− 10.536410

− 10.536400

− 10.536372

− 10.536399

− 10.536310

− 10.536410

Avg

− 10.535102

− 4.984155

− 10.155362

− 10.130943

− 10.536323

− 10.265787

− 10.265851

− 5.398869

− 10.536328

Wor.

− 10.532792

− 0.945504

− 5.128472

− 2.427335

− 10.536210

− 5.128479

− 5.128478

− 5.128474

− 10.464117

Std

1.024e−03

1.761e+00

1.345e+00

1.767e+00

4.951e−05

1.179e+00

1.179e+00

1.179e+00

1.564e−02

Rank

w/t/l

0/4/14

0/1/17

0/1/17

1/12/5

1/2/15

0/1/17

0/2/16

0/2/16

4/12/2

  1. F represents functions. D is for dimension of the problem. EI denotes evaluation indicator including the best, average, worst, and standard deviation of fitness.