Table 5 Energy consumption cost performance: the proposed algorithm comparing with evolutionary algorithms on smart IoT application.

From: Multi-objective quantum hybrid evolutionary algorithms for enhancing quality-of-service in internet of things

Sr No.

No. of gen.

No. of runs

MOEA-D algo

NSGA-III

MOPSO algo

MOWOA algo

Proposed algo

Worst_Fit

Mean_Fit

Worst_Fit

Mean_Fit

Worst_Fit

Mean_Fit

Worst_Fit

Mean_Fit

Worst_Fit

Mean_Fit

1

20

20

1.949441

1.658348

1.931799

1.570138

1.967083

1.684811

2.002367

1.720095

1.843589

1.561317

2

40

20

1.817283

1.545924

1.800837

1.463694

1.833729

1.570593

1.866621

1.603485

1.718607

1.455471

3

60

20

1.685125

1.4335

1.669875

1.35725

1.700375

1.456375

1.730875

1.486875

1.593625

1.349625

4

80

20

1.552967

1.321076

1.538913

1.250806

1.567021

1.342157

1.595129

1.370265

1.468643

1.243779

5

100

20

1.420809

1.208652

1.407951

1.144362

1.433667

1.227939

1.459383

1.253655

1.343661

1.137933

6

120

20

1.288651

1.096228

1.276989

1.037918

1.300313

1.113721

1.323637

1.137045

1.218679

1.032087

7

140

20

1.156493

0.983804

1.146027

0.931474

1.166959

0.999503

1.187891

1.020435

1.093697

0.926241

8

160

20

1.024335

0.87138

1.015065

0.82503

1.033605

0.885285

1.052145

0.903825

0.968715

0.820395

9

180

20

0.892177

0.758956

0.884103

0.718586

0.900251

0.771067

0.916399

0.787215

0.843733

0.714549

10

200

20

0.760019

0.646532

0.753141

0.612142

0.766897

0.656849

0.780653

0.670605

0.718751

0.608703

11

220

20

0.75361

0.64108

0.74679

0.60698

0.76043

0.65131

0.77407

0.66495

0.71269

0.60357

12

240

20

0.747201

0.635628

0.740439

0.601818

0.753963

0.645771

0.767487

0.659295

0.706629

0.598437

13

260

20

0.740792

0.630176

0.734088

0.596656

0.747496

0.640232

0.760904

0.65364

0.700568

0.593304

14

280

20

0.734383

0.624724

0.727737

0.591494

0.741029

0.634693

0.754321

0.647985

0.694507

0.588171

15

300

20

0.727974

0.619272

0.721386

0.586332

0.734562

0.629154

0.747738

0.64233

0.688446

0.583038

16

320

20

0.721565

0.61382

0.715035

0.58117

0.728095

0.623615

0.741155

0.636675

0.682385

0.577905

17

340

20

0.715156

0.608368

0.708684

0.576008

0.721628

0.618076

0.734572

0.63102

0.676324

0.572772

18

360

20

0.708747

0.602916

0.702333

0.570846

0.715161

0.612537

0.727989

0.625365

0.670263

0.567639

19

380

20

0.702338

0.597464

0.695982

0.565684

0.708694

0.606998

0.721406

0.61971

0.664202

0.562506

20

400

20

0.695929

0.592012

0.689631

0.560522

0.702227

0.601459

0.714823

0.614055

0.658141

0.557373

21

420

20

0.68952

0.58656

0.68328

0.55536

0.69576

0.59592

0.70824

0.6084

0.65208

0.55224

22

440

20

0.683111

0.581108

0.676929

0.550198

0.689293

0.590381

0.701657

0.602745

0.646019

0.547107

23

460

20

0.676702

0.575656

0.670578

0.545036

0.682826

0.584842

0.695074

0.59709

0.639958

0.541974

24

480

20

0.670293

0.570204

0.664227

0.539874

0.676359

0.579303

0.688491

0.591435

0.633897

0.536841

25

500

20

0.663884

0.564752

0.657876

0.534712

0.669892

0.573764

0.681908

0.58578

0.627836

0.531708

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