Bare-Bones Teaching-Learning-Based Optimization

Algorithm 1

TLBO( ).

(1) Begin

(2)    Initialize 𝑁 (number of learners) and 𝐷 (number of dimensions)

(3)    Initialize learners 𝑋 and evaluate all learners 𝑋

(4)   Donate the best learner as Teacher and the mean of all learners 𝑋 as Mean

(5)    while (stopping condition not met)

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

(7)      TF=round(1+rand(0,1))

(8)      for  𝑗=1 : 𝐷

(9)          𝑛𝑒𝑤𝑋_𝑖𝑗=𝑋_𝑖𝑗+rand(0,1)∗(𝑇𝑒𝑎𝑐ℎ𝑒𝑟(𝑗)−TF∗𝑀𝑒𝑎𝑛(𝑗))

(10)       endfor

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

(12)     endfor

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

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

(15)         if  𝑓(𝑋_𝑖) better 𝑓(𝑋_𝑘)

(16)            for  𝑗=1 : 𝐷

(17)              𝑛𝑒𝑤𝑋_𝑖𝑗=𝑋_𝑖𝑗+rand(0,1)∗(𝑋_𝑖𝑗−𝑋_𝑘𝑗)

(18)            endfor

(19)         else

(20)            for  𝑗=1 : 𝐷

(21)              𝑛𝑒𝑤𝑋_𝑖𝑗=𝑋_𝑖𝑗+rand(0,1)∗(𝑋_𝑘𝑗−𝑋_𝑖𝑗)

(22)            endfor

(23)         endif

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

(25)     endfor

(26)     Update the Teacher and the Mean

(27)  endwhile

(28) end