A Hybrid Metaheuristic DE/CS Algorithm for UCAV Three-Dimension Path Planning

Algorithm 5

Algorithm of DE/CS for UCAV three-dimension path planning.

Begin

 Step 𝟏 : Initializing. Set the generation counter 𝐺=1; randomly generate UCAV path

    (population/nest) of 𝑛 individuals and each egg in a nest corresponding to a

    potential optimal path to the given problem; set discovery rate 𝑝_𝑎; set the mutation

    scaling factor 𝐹 and crossover constant CR.

 Step 𝟐 : Evaluate the population of 𝑃 according to (5).

 Step 𝟑 :  While the termination criteria is not satisfied or  𝐺 < MaxGeneration do

     Sort the UCAV path (population/nest) from best to worst.

     Store the Keep best nests to KeepNest.

     Get a cuckoo (say, 𝑖) and replace its solution by performing Algorithm 3.

     Evaluate its cost (fitness) 𝐽_𝑖 according to (5).

     Choose a path among 𝑛 (say, 𝑗) randomly.

     if (𝐽_𝑖>𝐽_𝑗)

      Replace 𝑗 by the new solution.

     end if

     𝐾=rand(NP,𝐷)>𝑝_𝑎.

     𝑃₁=𝑃; 𝑃₂=𝑃.

     for  𝑖=1 to NP do

        Step=rand∗(𝑌_𝑖−𝑍_𝑖);

        𝑋_new=𝑋_𝑖+Step⨀𝐾(𝑖,∶);

     end for

     for  𝑖=1 to NP do

         if 𝐹(𝑋_new)<𝐹(𝑋_𝑖)  then

          𝑋_new=𝑋_𝑖; 𝐹(𝑋_new)=𝐹(𝑋_𝑖)

         end if

     end for

     Keep the best solutions/paths.

     Sort the population/nest/paths from best to worst and find the current best.

     Replace the Keep worst nests with the Keep best nests KeepNest stored.

     Pass the current best to the next generation.

     𝐺=𝐺+1.

 Step   𝟒 : end while

 Step   𝟓 : Inversely transform the coordinates in final optimal path into the original coordinate,

    and output

End.