Bare-Bones Teaching-Learning-Based Optimization

Algorithm 2

BBTLBO( ).

(1) Begin

(2)   Initialize 𝑁 (number of learners), 𝐷 (number of dimensions) and hybridization factor 𝑢

(3)   Initialize learners 𝑋 and evaluate all learners 𝑋

(4)   while (stopping condition not met)

(5)      for each learner 𝑋_𝑖 of the class % Teaching phase

(6)         TF=round(1+rand(0,1))

(7)        Donate the 𝑁𝑇𝑒𝑎𝑐ℎ𝑒𝑟 and the 𝑁𝑀𝑒𝑎𝑛 in its neighborhood for each learner

(8)        Updating each learner according (11)

(9)        Accept 𝑛𝑒𝑤𝑋_𝑖 if 𝑓(𝑛𝑒𝑤𝑋_𝑖) is better than 𝑓(𝑋_𝑖)

(10)       endfor

(11)       for each learner 𝑋_𝑖 of the class % Learning phase

(12)         Randomly select one learner 𝑋_𝑘, such that 𝑖≠𝑘

(13)          if rand(0, 1) < 0.5

(14)           Updating each learner according (3)

(15)          else

(16)           Donate the 𝑁𝑇𝑒𝑎𝑐ℎ𝑒𝑟 in its neighborhood for each learner

(17)           Updating each learner according (12)

(18)          endif

(19)         Accept 𝑛𝑒𝑤𝑋_𝑖 if 𝑓(𝑛𝑒𝑤𝑋𝑖) is better than 𝑓(𝑋_𝑖)

(20)       endfor

(21)   endwhile

(22) end