|
| Algorithm | Reference number | Merits | Demerits | Compared with |
|
| Least-squares algorithm | [4] | (1) High calculation speed;
(2) High precision of solution | (1) The appropriate initial model needs to be given;
(2) The partial derivative needs to be calculated;
(3) Easy to fall into local minima | None |
|
| Levenberg–Marquardt algorithm combined with the singular value decomposition technique | [5] | (1) High calculation speed;
(2) Excellent stability | (1) The appropriate initial model needs to be given;
(2) The partial derivative needs to be calculated;
(3) Easy to fall into local minima | None |
|
| Occam algorithm | [6] | (1) High calculation speed;
(2) High precision of solution;
(3) Excellent stability | (1) The appropriate initial model needs to be given;
(2) The partial derivative needs to be calculated;
(3) Easy to fall into local minima | None |
|
| Genetic algorithm | [7] | (1) Excellent ability to escape from local minima;
(2) Independent of selecting the initial model;
(3) Calculation of partial derivatives is avoided | (1) Huge computational time cost;
(2) Low accuracy of calculation | None |
|
| Genetic algorithm combining elite selection and dynamic mutation strategy | [8] | (1) Excellent stability;
(2) Excellent ability to escape from local minima;
(3) Independent of selecting the initial model;
(4) Calculation of partial derivatives is avoided | (1) Huge computational time cost;
(2) Low accuracy of calculation | Marquardt algorithm |
|
| Genetic algorithms combining marginal posterior probability density estimation | [9] | (1) Excellent ability to escape from local minima;
(2) Independent of selecting the initial model;
(3) Calculation of partial derivatives is avoided | (1) Huge computational time cost;
(2) Low accuracy of calculation | None |
|
| Heat-bath simulated annealing algorithm | [10] | (1) Excellent ability to escape from local minima;
(2) Independent of selecting the initial model;
(3) Calculation of partial derivatives is avoided;
(4) Suitable for parallel programming | (1) Low accuracy of calculation | Levenberg–Marquardt algorithm and fast simulated annealing algorithm |
|
| Artificial neural network | [18] | (1) Excellent stability;
(2) High inversion efficiency | (1) Requires large amounts of training data;
(2) Training the network costs a lot of time | Monte Carlo approach and gray wolf optimizer |
|
| LSTM based on the first height last velocity | [19] | (1) Excellent stability;
(2) High inversion efficiency | (1) Requires large amounts of training data;
(2) Training the network costs a lot of time | None |
|
