A Novel Harmony Search Algorithm Based on Teaching-Learning Strategies for 0-1 Knapsack Problems

Algorithm 7

Improvisation of new harmony in HSTL algorithm.

𝑋^new=𝑋^worst;   % select 𝑋^worst as Optimization target vector

 For i  = 1 to D

   If  rand() ⩽ HMCR   % (a) Harmony memory consideration

      𝑥^new_𝑖=𝑥^𝑗_𝑖𝑗=1,2,…,HMS

   Elseif  rand() ⩽ TLP   % (b) Teaching-Learning strategy

     If  rand() ⩽ 0.5     % Teaching

        𝑥^new_𝑖=𝑥^new_𝑖+rand()×𝑥^best_𝑖−0.5TF×𝑥^worst_𝑖+𝑥^new_𝑖

     Else       % Learning

       Randomly select r ₁ and r ₂ 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

     Endif

      𝑥^new_𝑖=minmax𝑥_𝑖^𝑛𝑒𝑤,𝑥^𝐿_𝑖,𝑥^𝑈_𝑖

   Elseif rand(0,1) ⩽ PAR       %(c) Local pitch adjusting strategy

      𝑥^new_𝑖=𝑥^new_𝑖±rand()×BW(𝑖)

      𝑥^new_𝑖=minmax𝑥^new_𝑖,𝑥^𝐿_𝑖,𝑥^𝑈_𝑖

   Endif

   If rand(0,1) ⩽ 𝑃_𝑚   %(d) Random mutation operator in feasible space

      𝑥^new_𝑖=𝑥^𝐿_𝑖+𝑥^𝑈_𝑖−𝑥^𝐿_𝑖×rand()

   Endif

Endfor