Biological Flower Pollination Algorithm with Orthogonal Learning Strategy and Catfish Effect Mechanism for Global Optimization Problems

Algorithm 2

Catfish effect mechanism.

Input

 Number of population NP, population dimensionality .

 Current iteration number , current iterative population .

 Objective function of minimization or maximization problems.

 The storage vector of historical best fitness.

 Consecutive iteration number .

Output

  “Catfish” individuals CX.

Begin

 If

 % The global best individual has not improved in consecutive iterations.

  If

 % Sort the population from good to bad based on fitness.

   sort_f, sort_ind = sort((SX)).

 % Choose 10% worst individuals as WX based on sort_ind.

   WX = SX(sort_ind .

 % Generate the “catfish” individuals CX by using (12).

   For to the number of WX

   For

    .

   End for

   End for

   Compute the fitness value .

  End if

  Replace all the worst individuals WX with “catfish” individuals .

 End if

End