| GAs | Genetic Algorithms |
| ANNs | Artificial neural networks |
| IPS | Interior-point scheme |
| FFANNs | Feed-forward ANNs |
| ASA | Active-set algorithm |
| ANN-GA-ASA | Hybrid of ANNs, GAs, and ASA |
| MAD | Mean absolute deviation |
| VAF | Variance account for |
| S.I.R | Semi-interquartile range |
| Max | Maximum |
| X | New technical diagnostics |
| Ki | ith coefficient of environmental and economic indicator |
| C1, C2, C3 | Values of Initial conditions |
| WX | X components of W |
| WZ | Z components of W |
| wX, wY, wZ | Weighting factor of X, Y, and Z |
| E | Fitness function |
| IPA | Interior point algorithm |
| SQP | Sequential quadratic programming |
| ANN-GA-IPA | Hybrid of ANNs, GAs, and IPA |
| FFANN-GASQP | Hybrid of ANNs, GA, and SQP |
| PSO | Particle swarm optimization |
| ANN-PSO-IPS | Hybrid of ANNs, PSO, and IPS |
| TIC | Theil’s inequality coefficient |
| EVAF | Error in VAF |
| STD | Standard Deviation |
| Min | Minimum |
| Y | elimination costs of emergencies values |
| Z | the competence of the system of industrial elements |
| W | Known weights of ANNs or Chromosome of GAs |
| WY | Y component of weights |
| nX, nY, nZ | Scaling factors of X, Y, and Z |
| lX, lY, lZ | Biasing factor of X, Y, and Z |
| a, b | Real constant values |