Input: p(population quantity), M(iterations), dim(dimension), d(proportion of discoverers)

1. Define the objective function F(X), where the variable X = (X₁, X₂, …, X_n), the variable X_i = (x₁, x₂, …, x_m)

2. Randomly initialize the positions of n sparrows X and define their relevant parameters

3. Use function F(X) to obtain the population fitness value matrix

4. for i ← 0 to M do

5. Sort the fitness value matrix from small to large to obtain a matrix named fit

6. R = min(fit)

7. if R < safety value then

8. for i ← 0 to p*d do

9. Update sparrow position using $GridSearchCV\left(X_i^t,R\right)$ in formula (3)

10. end for

11. else

12. for i ← 0 to p*d do

13. Update sparrow position using $X_i^t+Q\ast L$ in formula (3)

14. end for

15. for i ← 0 to p*(1-d) do

16. if (i + p*d) > p/2 then

17. Update sparrow position using $X_i^t+I\ast \frac{X_{worst}^t-X_i^t}{\sqrt[2]{i}}$ in formula (4)

18. else

19. Update sparrow position using $X_{best}^t+Q\ast L$ in formula (4)

20. end for

21. θ ← 0.8

22. for i ← 0 to p do

23. if i >= θ*p then Update sparrow position using $X_i^t+\frac{\beta \cdot \left(f_i-f_{best}\right)}{f_{worst}-f_{best}}\cdot \left(X_{best}^t-X_i^t\right)$ in formula (5)

24. end for

25. Update the global optimal fitness value and the individual optimal position of the population

26. end for

Outout:

BestX: location of global optimal fitness value

Convergence_Curve: global optimal fitness value for each iteration