Nature; The most excellent teacher of humankind

The Shuffled Frog Leaping Algorithm (SFLA) is one of the most innovative optimization algorithms inspired by the social behavior of frogs in nature, and in terms of classification, among behavioral algorithms or Memetic Algorithms are included. Other names for the frog jump optimization algorithms include the frog jump algorithm, frog leaping algorithm, and SFLA algorithm. This algorithm was first proposed by Eusuff and Lansey in 2003, although many papers have since been submitted to improve this algorithm. At the bottom of the download link is the main article of the frog optimization algorithm.

What is the SFLA algorithm?

The SFLA algorithm is an algorithm based on Metaheuristic memetics. The memetic algorithm is a population-based algorithm used for complex and significant optimization problems. The main idea of ​​this algorithm is to use a local search method within the structure of the genetic algorithm to improve the aqueous performance of the search intensification process. The memetic algorithm first encrypts the sum of the initial answers, then Ibn algorithm calculates the utility of each response based on a fitness function and generates new solutions.

The SFLA algorithm is inspired by how frogs search for food. This algorithm uses the Nomometric method to search locally among subgroups of frogs. The frog hybrid jump algorithm uses the hybrid strategy and allows the exchange of messages in local search. This algorithm combines the advantages of the Nomometric algorithm and particle group optimization. In the frog hybrid jump algorithm, words are exchanged not only in local search but also in global search. Thus, Local and Global searches are well combined in this algorithm. The frog hybrid jump algorithm is highly searchable and easy to implement. The frog hybrid jump algorithm can solve many nonlinear, undetectable, and multi-state problems.

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