scholarly journals Algorithm for numerical solutions to the kinetic equation of a spatial population dynamics model with coalescence and repulsive jumps

2020 ◽  
Author(s):  
Igor Omelyan ◽  
Yuri Kozitsky ◽  
Krzysztof Pilorz
Keyword(s):  
Population Dynamics ◽  
Kinetic Equation ◽  
Numerical Solutions ◽  
Dynamics Model ◽  
Spatial Population ◽  
Population Dynamics Model ◽  
Spatial Population Dynamics

scholarly journals Modeling and designing self-organized aggregation in a swarm of miniature robots

2011 ◽  
Vol 30 (5) ◽  
pp. 615-626 ◽  
Author(s):  
Nikolaus Correll ◽  
Alcherio Martinoli
Keyword(s):  
Population Dynamics ◽  
Cluster Size ◽  
Control Parameters ◽  
Dynamics Model ◽  
Miniature Robots ◽  
Self Organized ◽  
Spatial Population ◽  
Population Dynamics Model ◽  
The Individual ◽  
Spatial Population Dynamics

We model the dynamics of self-organized robot aggregation inspired by a study on the aggregation of gregarious arthropods. In swarms of German cockroaches, aggregation into clusters emerges solely from local interactions between the individuals, whereas the probabilities of joining or leaving a cluster are a function of the cluster size. We propose a non-spatial population dynamics model that keeps track of the number of robots in clusters of specific size using control parameters of the individual robots and the probability of detecting another robot in the environment. The model is able to quantitatively and qualitatively predict the dynamics observed in extensive realistic multi-robot simulation, and provides qualitative agreement with data obtained from aggregation of Blattela germanica larvae. In particular, we show by analysis, numerical and realistic simulation that the emergence of a single aggregate requires a minimal communication range between individuals.

scholarly journals A random effects population dynamics model based on proportions-at-age and removal data for estimating total mortality

2012 ◽  
Vol 69 (11) ◽  
pp. 1881-1893 ◽  
Author(s):  
Verena M. Trenkel ◽  
Mark V. Bravington ◽  
Pascal Lorance
Keyword(s):  
Population Dynamics ◽  
Random Effects ◽  
Fixed Effects ◽  
Random Effect ◽  
Total Mortality ◽  
Estimation Bias ◽  
Effective Sample Size ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Study Parameter

Catch curves are widely used to estimate total mortality for exploited marine populations. The usual population dynamics model assumes constant recruitment across years and constant total mortality. We extend this to include annual recruitment and annual total mortality. Recruitment is treated as an uncorrelated random effect, while total mortality is modelled by a random walk. Data requirements are minimal as only proportions-at-age and total catches are needed. We obtain the effective sample size for aggregated proportion-at-age data based on fitting Dirichlet-multinomial distributions to the raw sampling data. Parameter estimation is carried out by approximate likelihood. We use simulations to study parameter estimability and estimation bias of four model versions, including models treating mortality as fixed effects and misspecified models. All model versions were, in general, estimable, though for certain parameter values or replicate runs they were not. Relative estimation bias of final year total mortalities and depletion rates were lower for the proposed random effects model compared with the fixed effects version for total mortality. The model is demonstrated for the case of blue ling (Molva dypterygia) to the west of the British Isles for the period 1988 to 2011.

A microbial treatment population dynamics optimization approach

2021 ◽  
pp. 1-15
Author(s):  
Jinding Gao
Keyword(s):  
Population Dynamics ◽  
Information Exchange ◽  
Information Diffusion ◽  
Optimization Problems ◽  
Microbial Control ◽  
Function Optimization ◽  
Optimization Approach ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Number Of Individuals

In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.

scholarly journals Spatial population dynamics of eastern oyster in the Chesapeake Bay, Maryland

2021 ◽  
Vol 237 ◽  
pp. 105854
Author(s):  
Marvin M. Mace ◽  
Kathryn L. Doering ◽  
Michael J. Wilberg ◽  
Amy Larimer ◽  
Frank Marenghi ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Chesapeake Bay ◽  
Eastern Oyster ◽  
Spatial Population ◽  
Spatial Population Dynamics
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