A Novel Quantum-Behaved Bat Algorithm with Mean Best Position Directed for Numerical Optimization

Algorithm 2

Bat algorithm with mean best position (QMBA) pseudocode.

() Initialize the bat population    and

() Define pulse frequency , pulse rate and the loudness

()  While ( < max_iteration)

()   for    to  

()   Generate new solutions by calculating the distance between the bat and current global position, updating positions

    as equations (4)-(5).

()   if (rand > )

()    if (rand > )

()     Bats fly with quantum behavior and positions updated as (6)~(8).

()    else

()    The mean best position is used to guide other bats and posit1ons updated as (9).

()    end if

()   end if

()   if (rand <   &&  )

()    Accept the new solutions

()    Increase and reduce

()   end if

()  Rank the bats and find the current best .

() end while

() Output results and visualization