A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization

Algorithm 2

Bat algorithm.

Begin

 Step  1: Initialization. Set the generation counter 𝑡=1; Initialize the population of NP bats 𝑃

    randomly and each bat corresponding to a potential solution to

    the given problem; define loudness 𝐴_𝑖, pulse frequency 𝑄_𝑖

    and the initial velocities 𝑣_𝑖(𝑖=1,2,…,NP); set pulse rate 𝑟_𝑖.

 Step  2: While the termination criteria is not satisfied or  𝑡 < MaxGeneration do

    Generate new solutions by adjusting frequency, and updating velocities

    and locations/solutions [(4)–(6)]

    if (rand > 𝑟_𝑖) then

     Select a solution among the best solutions;

     Generate a local solution around the selected best solution

    end if

    Generate a new solution by flying randomly

    if (rand < 𝐴_𝑖 & 𝑓(𝑥_𝑖) < 𝑓(𝑥_∗)) then

     Accept the new solutions

     Increase 𝑟_𝑖 and reduce 𝐴_𝑖

    end if

    Rank the bats and find the current best 𝑥_∗

    𝑡=𝑡+1;

 Step  3: end while

 Step  4: Post-processing the results and visualization.

End.