Table 3.
Combination of SSA with ANNs
| Refs | Models | Objective | Advantages | Disadvantages |
|---|---|---|---|---|
| [84] |
SSA-RBF ISSA-RBF |
Predicting the temperature of the sensors | Find the optimal value for RBF parameters | Non-optimal updates of individual |
| Reduce data training time | Achieve a solution in the final iterations | |||
| Increase detection accuracy | ||||
| Error reduction | ||||
| [85] | SSA-ELM | The SSA-ELM model predicts the uniaxial compressive strength (UCS) of the cemented paste backfill (CPB) under different conditions | Increase forecast accuracy | Non-optimal updates of individual |
| Discover the optimal value for ELM parameters | High execution time | |||
| Settings for the number of layers and the number of nodes | ||||
| [86] | Firefly Algorithm SSA (FASSA-GRNN) | Prediction of industrial and laboratory materials | Enhance SSA search capability using FA | Slow convergence rate |
| Determining the optimal weight for GRNN | ||||
| Reduce the amount of output error | ||||
| [87] | SSA-ENN | The SSA-ENN strategy can improve road capacity and traffic stability | Reduce data training time | Achieve a solution in the final iterations |
| Increase detection accuracy | ||||
| Error reduction | ||||
| [88] | SSA-BP | The proposed SSA-BP algorithm can characterize the critical deformation dimensions (height, length, tilt angle) within the mean relative error of 10% | Find the optimal value for RBF parameters* | Slow convergence rate |
| Reduce data training time | ||||
| Increase detection accuracy | ||||
| Error reduction | ||||
| [89] | FA-SSA-BPNN | Optimization of sensor features and model parameters | Reduce data training time | Achieve a solution in the final iterations |
| Improve accuracy in data training | ||||
| [90] | ICEEMD-SSA-BPNN | Predicting the price of carbon and industrial materials | Settings for the number of layers and the number of nodes | High execution time |
| Improve the internal structure of the network | ||||
| Increase detection accuracy | ||||
| Improve accuracy in data training | ||||
| [91] | WMF-SSA-MLELM | Short-term multistep wind speed forecasting | Reduce data training time | Achieve a solution in the final iterations |
| Improve accuracy in data training | ||||
| Find the optimal value for network parameters | ||||
| [92] | SSA-BP | predicting possible threats based on commander mood (PTP-CE) | Improve the internal structure of the network | High execution time |
| Improve accuracy in data training | ||||
| Reduce data training time | ||||
| [93] | CMSSA-Elman | Short-term PV Power Forecasting Based on Time-Phased and Error Correction | Settings for the number of layers and the number of nodes | Achieve a solution in the final iterations |
| Error reduction | ||||
| [94] | SSA-BP | Optimization of the BP Neural Network Algorithm with SSA for the Processing of Coal Mine Water Source Data | Settings for the number of layers and the number of nodes | Slow convergence rate |
| Reduce data training time | ||||
| Find the optimal value for network parameters | ||||
| [95] | SSA-ELM | Predicting air pollution | Increase detection accuracy | Non-optimal updates of individual |
| Improve the internal structure of the network | ||||
| Find the optimal value for network parameters | ||||
| [96] | SSA-BP | Based on the SSA-BP Neural Network, an assessment algorithm for network security is developed | Reduce data training time | High execution time |
| Improve accuracy in data training | ||||
| [97] | Tent Cauchy SSA (TCSSA-BP) | Regression prediction of material grinding particle size | Settings for the number of layers and the number of nodes | Achieve a solution in the final iterations |
| Reduce data training time | ||||
| [98] | SSA-KELM | Intelligent Fault Diagnosis | Error reduction | High execution time |
| Improve accuracy in data training | ||||
| [99] | SSA-DBN | Predictability and accuracy of diagnosis | Find the optimal value for network parameters | Slow convergence rate |
| Settings for the number of layers and the number of nodes | ||||
| Error reduction | ||||
| Improve accuracy in data training | ||||
| [100] | SSA-BP | Forecasting hydropower generation | Improve accuracy in data training | Slow convergence rate |
| Improve the internal structure of the network | ||||
| Error reduction | ||||
| [101] | SSA-KELM | From water quality assessment to environmental water quality management | Settings for the number of layers and the number of nodes | High iterations |
| Improve the internal structure of the network | ||||
| [102] | SSA-BP neural network | Prediction of industrial and laboratory materials | Increase detection accuracy | Non-optimal updates of individual |
| Improve accuracy in data training | ||||
| Find the optimal value for network parameters | ||||
| Error reduction | ||||
| [103] | SSA-BP | Wind and solar power forecasting | Find the optimal value for network parameters | High iterations |
| Error reduction | ||||
| Improve accuracy in data training | ||||
| [104] | SSA-BP | Predicting the boiling point temperature of working fluid | Reduce data training time | Non-optimal updates of individual |
| Error reduction | ||||
| Increase detection accuracy | ||||
| [105] | SSA-KELM | Blood glucose prediction | Reduce data training time | Slow convergence rate |
| Improve accuracy in data training | ||||
| [106] | ISSA-FSCN(Fast stochastic configuration network) | Fire flame recognition | Good optimization ability | Slow convergence rate |
| Classification of flame images |