In this population algorithm, a population of frogs (answers) consists, each frog will have a chromosome-like structure in the genetic algorithm. It seeks to strike a balance between extensive scrutiny in the space of possible answers. This algorithm combines the advantages of two categories of genetics-based algorithms (such as memetics) and social behavior-based algorithms (such as the PSO bird algorithm). Description of the frog mutation algorithm The frog hybrid jump algorithm can solve many nonlinear, undetectable, and multi-state problems. The frog hybrid jump algorithm is highly searchable and easy to implement. Thus, Local and Global searches are well combined in this algorithm. In the frog hybrid jump algorithm, words are exchanged not only in local search but also in global search. This algorithm combines the advantages of the Nomometric algorithm and particle group optimization. The frog hybrid jump algorithm uses the hybrid strategy and allows the exchange of messages in local search.

This algorithm uses the Nomometric method to search locally among subgroups of frogs. The SFLA algorithm is inspired by how frogs search for food. 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 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 is a population-based algorithm used for complex and significant optimization problems. The SFL A algorithm is an algorithm based on Metaheuristic memetics.

Frog leap solution