| MH |
Metaheuristic |
| SCP |
Set-covering problem |
| P |
Polynomial time |
| NP |
Nondeterministic polynomial time |
| EAs |
Evolutionary algorithms |
| MSA |
Binary monkey search algorithm |
| ALO |
Antlion optimization |
| CSA |
Crow search algorithm |
| GA |
Genethic Algorithm |
| MLST |
Minimum labeling spanning tree |
| VANETs |
Vehicular ad hoc networks |
| MST |
Minimum spanning tree |
| MILP |
Mixed-integer linear programs |
| ESN |
Echo state network |
| ELM |
Extreme Learning Machines |
| MRE |
Magnetorheological elastomer |
| CNN |
Convolutional neural network |
| NN |
Neural network |
| DE |
Differential evolution |
| GO |
Growth optimizer |
| BGO |
Binary Growth Optimizer |
| GR |
Growth resistance |
|
Current iteration |
| LF |
Learning factor |
| KA |
Knowledge acquisition |
| AF |
Attenuation factor |
| FE |
Current number of evaluations |
| MaxFE |
Maximum number of evaluations |
| ub |
Domain upper bound |
| lb |
Domain lower bound |
| D |
Population dimension |
| GWO |
Grey wolf optimizer |
| PSA |
Pendulum Search Algorithm |
| SCA |
Sine–cosine algorithm |