Table 10 Feature selection results on wind dataset using binary metaheuristics.

From: Optimizing solar and wind forecasting with iHow optimization algorithm and multi-scale attention networks

Optimizer

Avg. Error

Avg. Size

Avg. Fitness

Best Fitness

Worst Fitness

Std. Dev.

biHOW

0.3925

0.3453

0.4557

0.3575

0.4560

0.2780

bHHO

0.4102

0.5458

0.4724

0.3927

0.4596

0.2832

bGWO

0.4495

0.6791

0.4807

0.4342

0.5442

0.3014

bPSO

0.5430

0.6448

0.5698

0.5501

0.6178

0.3816

bWAO

0.5428

0.8082

0.5776

0.5417

0.6178

0.3838

bBBO

0.5112

0.8086

0.5755

0.5652

0.6517

0.4265

bMVO

0.5197

0.7413

0.5995

0.5247

0.6427

0.4323

bSFS

0.4126

0.5482

0.4748

0.3951

0.4620

0.2856

bSAO

0.4560

0.6876

0.4961

0.3858

0.4874

0.2949

bJAYA

0.4462

0.7116

0.4810

0.4451

0.5212

0.2872

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