Table 1.

The procedures for implementing the CM-CSA algorithm.

Input:The Initial Position of the Crow Population Output: The Optimal Solution of the Problem (the Best Position in the Memory of All Crows) and the Optimal Individual
Step 1: Start
Step 2: Initialization. Randomly generate n crows in the d-dimensional feasible region, where each crow $X^{i,iter}=x_1,x_2,x_3,\cdots ,x_n$ represents a feasible solution, and initialize the maximum iteration evaluation times and the awareness probability AP. Step 3: Initialize the crow memory value and calculate the fitness value.
Step 4: Update the parameter $g_{best}$.
Step 5: Use Formula (11) to perform Cauchy mutation on $g_{best}$ and perform cross-border processing.
Step 6: Use Formula (13) to adaptively adjust the step length fl.
Step 7: Use Formula (14) to perform displacement update operation.
Step 8: Check the feasibility of the new location.
Step 9: Calculate the fitness of the new position and update the memory value.
Step 10: Repeat steps 4 to 9 until the termination condition is reached, and then stop the iteration.
Step 11: Output the optimal solution and the optimal individual. Step 12: End.