Application of the Computational Intelligence Method to Modeling the Dynamics of Multidimensional Population System

Petrov, Alexey A.; Druzhinina, O. V.; Masina, Olga N.

doi:10.1007/978-3-031-21438-7_45

Cited by 1 publication

(3 citation statements)

References 15 publications

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“…population-migration model (2) In this section of the paper, we develop the approach proposed in [35] to the optimization search for the parameters of population models. The approach considered in this paper makes it possible to obtain such sets of model parameters that ensure the achievement of the extremum of the quality criterion.…”

Section: Analysis Of Stationary States and Construction Of Projection...mentioning

confidence: 99%

“…The approach considered in this paper makes it possible to obtain such sets of model parameters that ensure the achievement of the extremum of the quality criterion. We propose to use a quality criterion by analogy with [35] to study model (2).…”

Section: Analysis Of Stationary States and Construction Of Projection...mentioning

confidence: 99%

“…This assessment is performed through a comparative analysis of deterministic and stochastic models with selected sets of parameters. When studying high-dimensional models, the choice of parameters can be carried out by applying evolutionary algorithms [12,[33][34][35]. Various systems of population dynamics (with competition, mutualism, migration) based on self-consistent models are considered in [12,14,28] and other papers.…”

Section: Introductionmentioning

confidence: 99%

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Construction, stochastization and computer study of dynamic population models “two competitors - two migration areas”

Vasilyeva

Demidova

Druzhinina³

et al. 2023

Discrete and Continuous Models

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When studying deterministic and stochastic population models, the actual problems are the formalization of processes, taking into account new effects caused by the interaction of species, and the development of computer research methods. Computer research methods make it possible to analyze the trajectories of multidimensional population systems. We consider the “two competitors - two migration areas” model, which takes into account intraspecific and interspecific competition in two populations, as well as bidirectional migration of both populations. For this model, we take into account the variability of the reproduction rates of species. A formalized description of the four-dimensional model “two competitors - two migration areas” and its modifications is proposed. Using the implementation of the evolutionary algorithm, a set of parameters is obtained that ensure the coexistence of populations under conditions of competition between two species in the main area, taking into account the migration of these species. Taking into account the obtained set of parameters, a positive stationary state is found. Two-dimensional and three-dimensional projections of phase portraits are constructed. Stochastization of the model “two competitors - two migration areas” is carried out based on the method of self-consistent one-step models constructing. The Fokker-Planck equations are used to describe the structure of the model. A transition to a four-dimensional stochastic differential equation in the Langevin form is performed. To carry out numerical experiments, a specialized software package is used to construct and study stochastic models, and a computer program based on differential evolution is developed. Algorithms for generating trajectories of the Wiener process and multipoint distributions and modifications of the Runge-Kutta method are used. In the deterministic and stochastic cases, the dynamics of the trajectories of populationmigration systems is studied. A comparative analysis of deterministic and stochastic models is carried out. The results can be used in modeling of different classes of dynamic systems.

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Section: Analysis Of Stationary States and Construction Of Projection...mentioning

confidence: 99%

Section: Analysis Of Stationary States and Construction Of Projection...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Construction, stochastization and computer study of dynamic population models “two competitors - two migration areas”

Vasilyeva

Demidova

Druzhinina³

et al. 2023

Discrete and Continuous Models

Self Cite

View full text Add to dashboard Cite

show abstract

Application of the Computational Intelligence Method to Modeling the Dynamics of Multidimensional Population System

Cited by 1 publication

References 15 publications

Construction, stochastization and computer study of dynamic population models “two competitors - two migration areas”

Construction, stochastization and computer study of dynamic population models “two competitors - two migration areas”

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