A Bat-Inspired Sparse Recovery Algorithm for Compressed Sensing

Algorithm 3

A Bat-Inspired Sparse Recovery Algorithm for CS.

	Input: Sensing matrix , measurement vector , sparsity level , termination threshold , maximum allowed iterative number .
	Output: The estimated signal
	Fitness function:
	Initialization: Initialize the positions and velocities of bats (Section 3.2).
	Termination condition: If or , terminated the iteration.
	Iteration:
	Step 1: A set is formed with elements selected randomly from the best solution , define .
	Step 2: If , a set consists of elements selected randomly from the set , update . If , the set consists of elements selected randomly from the set , update .
	Step 3: The position of the -th bat is a set composed of the indices corresponding to the maximum absolute values in .
	Step 4: If , select an optimal solution from the best solutions set and then replace elements of this solution with other elements in .
	Step 5: Calculate the new fitness .
	Step 6: If , adjust and .
	Step 7: If , update the best solutions set . If , update the best solution .
	Step 8: If termination condition is satisfied, stop iteration; otherwise, continue.