Table 15 Optimization results of different algorithms on speed reducer problem design.

From: Adaptive dynamic crayfish algorithm with multi-enhanced strategy for global high-dimensional optimization and real-engineering problems

Algorithm

\(\:{\varvec{x}}_{1}\)

\(\:{\varvec{x}}_{2}\)

\(\:{\varvec{x}}_{3}\)

\(\:{\varvec{x}}_{4}\)

\(\:{\varvec{x}}_{5}\)

\(\:{\varvec{x}}_{6}\)

\(\:{\varvec{x}}_{7}\)

Optimum Cost

AD-COA-L

2.606452

0.710209

7.3

7.3

3.381091

5.274581

2674.265

2.606452

COA

2.805034

0.7

7.331474

7.3

3.349004

5.286384

2712.064

2.805034

GJO

2.805279

0.7

7.545646

7.32871173

3.355445

5.286641

2716.623

2.805279

RSA

2.805527

0.7

7.3

7.30000003

3.348862

5.28637

2696.12

2.805527

WOA

2.646551

0.7

7.385085

7.39972896

3.348652

5.286488

2721.568

2.646551

GWO

2.793395

0.700026

7.334125

7.42196935

3.348564

5.287437

2715.745

2.793395

HHO

2.751335

0.703378

7.578303

7.3

3.368571

5.288383

2716.935

2.751335

PSO

2.813802

0.7

7.337899

7.3

3.354786

5.297043

2723.614

2.813802

WSO

2.805527

0.7

7.3

7.30000008

3.348862

5.28637

2711.884

2.805527

jDE

2.805527

0.7

7.300145

7.3

3.348862

5.28637

2711.884

2.805527

ASMA

2.735725

0.707382

7.891635

7.57565249

3.808051

5.359336

2933.446

2.735725