Structurization of information based on the combination of genetic, swarm and monkey algorithms

The paper considers an optimization algorithm for a swarm of particles. In the article, the algorithm emulates the interaction between participants to exchange information. Particle swarm optimization has been applied in many areas in optimization and in combination with other existing algorithms. This method searches for the optimal solution using agents called particles, whose trajectories are regulated by the stochastic and deterministic component. Each particle is affected by its “best” position achieved and the “best” position of the group, but it tends to move randomly. Genetic and bee algorithms are considered. A combined algorithm based on the operation of the monkey algorithm and the genetic algorithm is proposed. Experimental studies have been carried out.