A Bat Algorithm with Mutation for UCAV Path Planning

Algorithm 1

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 [(7)]

      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

      Generate a new solution by flying randomly

      if (rand < 𝐴_𝑖 and 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.