An Improved Artificial Bee Colony Algorithm Based on Balance-Evolution Strategy for Unmanned Combat Aerial Vehicle Path Planning

Pseudocode 1

(1) initialize solution population using (4)

(2) set 𝑡𝑟𝑖𝑎𝑙(𝑖)=1 (𝑖=1,2,…,𝑆𝑁)

(3) for  𝑖𝑡𝑒𝑟=1∶ MCN, do

(4)  for  𝑖𝑡𝑒𝑚=1∶𝑆𝑁, do

(5)   crossover and mutate using (14) in as many as 𝑡𝑟𝑖𝑎𝑙(𝑖𝑡𝑒𝑚) randomly selected

     elements for the 𝑖𝑡𝑒𝑚-th employed bee

(6)   adopt greedy selection

(7)   if better position is found for the 𝑖𝑡𝑒𝑚-th employed bee, then

(8)    𝑡𝑟𝑖𝑎𝑙(𝑖𝑡𝑒𝑚)←1

(9)   else

(10)     𝑡𝑟𝑖𝑎𝑙(𝑖𝑡𝑒𝑚)←𝑡𝑟𝑖𝑎𝑙(𝑖𝑡𝑒𝑚)+1

(11)  end if

(12) end for

(13) calculate each 𝑃_𝑖 using (6)

(14)  set 𝑖𝑡𝑒𝑚=1

(15)  while  𝑖𝑡𝑒𝑚≤𝑆𝑁, do

(16)   crossover and mutate using (16) in one randomly selected element for the 𝑖𝑡𝑒𝑚-th onlooker bee

(17)   adopt greedy selection

(18)   if better position is found, then

(19)   𝑡𝑟𝑖𝑎𝑙(𝑖𝑡𝑒𝑚)←1

(20)   else

(21)   𝑡𝑟𝑖𝑎𝑙(𝑖𝑡𝑒𝑚)←𝑡𝑟𝑖𝑎𝑙(𝑖𝑡𝑒𝑚)+1

(22)   end if

(23) 𝑖𝑡𝑒𝑚←𝑖𝑡𝑒𝑚+1

(24)  end while

(25)  if  𝑡𝑟𝑖𝑎𝑙(𝑖)>𝐷,𝑖∈Ω, then

(26)   set 𝑡𝑟𝑖𝑎𝑙(𝑖)=𝐷,∀𝑖∈Ω

(27)  end if

(28)  if mean(𝑡𝑟𝑖𝑎𝑙)≥0.9⋅𝐷, then

(29)   re-initialize randomly selected 90% employed bees using (4)

(30)  end if

(31)  memorize current best solution

(32) end for

(33) output global optimum