An Improved Harmony Search Based on Teaching-Learning Strategy for Unconstrained Optimization Problems

Algorithm 4

The improvisation process of new harmony in HSTL algorithm.

𝑥^new=𝑥^𝑟 (𝑟=1, 2,…, HMS); // randomly select 𝑥^𝑟 as optimization target vector

For  𝐢=𝟏  to  𝐃  do

 If  rand( ) ≤ HMCR      //(a) Harmony memory consideration

   𝑥^new_𝑖=𝑥^𝑗_𝑖 (𝑗=1,2,…,HMS)

 Else  If rand( ) ≤ TLP  //(b) Teaching-Learning strategy

   If rand( ) ≤ 0.5

    // Teaching

    𝑥^new_𝑖=𝑥^new_𝑖+rand()×[𝑥^best_𝑖−0.5𝑇_𝐹×(𝑥^worst_𝑖+𝑥^new_𝑖)]

   Else

    // Learning

    Randomly select 𝑟₁ and 𝑟₂ from {1,2,…,HMS}

     If  𝑥^𝑟1 is better than 𝑥^𝑟2

      𝑥^new_𝑖=𝑥^new_𝑖+rand()×(𝑥^𝑟1_𝑖−𝑥^𝑟2_𝑖)

     Else

      𝑥^new_𝑖=𝑥^new_𝑖+rand()×(𝑥^𝑟2_𝑖−𝑥^𝑟1_𝑖)

      End

    End

       𝑥^new_𝑖=min(max(𝑥^new_𝑖,𝑥^𝐿_𝑖),𝑥^𝑈_𝑙)

 Else  If rand(0, 1) ≤ PAR  // (c) Local pitch adjusting strategy

   𝑥^new_𝑖=𝑥^new_𝑖±rand()×BW(𝑖)

   𝑥^new_𝑖=min(max(𝑥^new_𝑖,𝑥^𝐿_𝑖),𝑥^𝑈_𝑙)

 Else  If rand(0, 1) ≤ 𝑃_𝑚   // (d) Random mutation operator

   𝑥^new_𝑖=𝑥^𝐿_𝑖+(𝑥^𝑈_𝑖−𝑥^𝐿_𝑖)×rand()

 End  If

End  For