Research Article
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 |
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