Izvestiya of Saratov University.

Mathematics. Mechanics. Informatics

ISSN 1816-9791 (Print)
ISSN 2541-9005 (Online)

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Ivanko E. E., Chervinsky S. M. Survival Rate of Model Populations Depending on the Strategy of Energy Exchange Between the Organisms. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, 2020, vol. 20, iss. 2, pp. 241-256. DOI: 10.18500/1816-9791-2020-20-2-241-256, EDN: NJNWUB

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Survival Rate of Model Populations Depending on the Strategy of Energy Exchange Between the Organisms

Ivanko Evgeny Evgenievich, Institute of Mathematics and Mechanics, Ural Branch of the Russian Academy of Sciences
Chervinsky Serge Mironovich, Institute of Mathematics and Mechanics, Ural Branch of the Russian Academy of Sciences

The paper addresses the influence of the energy exchange strategy between the organisms of a population in a gradually changing environment on the survival rate of this population. At the first stage of computational experiments, a “boundary” region is determined in the space of two parameters (mutation rate and energy supply rate), within which the survival of populations with zero energy exchange is ambiguous (lies in the interval from 5 to 95%). At the second stage, on the basis of a random sampling of experimental conditions from the boundary region, the dependence of the survival rate of model populations on the fraction of energy transferred during interaction from an organism with larger energy to an organism with smaller one is constructed. The performed experiments demonstrate: 1) the positive effect of altruistic energy exchange (where the organism with larger energy plays the role of a donor) on the survival rate of the populations and 2) the absence of an observable influence of the amount of energy transferred by the parent to the newborn on the survival rate of the populations. The results obtained may be of interest for the construction of artificial populations, for example, in the design of swarms of medical nanorobots or in the development of evolutionary metaheuristic algorithms for solving various optimization problems.

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