ترغب بنشر مسار تعليمي؟ اضغط هنا

Modeling ant battles by means of a diffusion-limited Gillespie algorithm

54   0   0.0 ( 0 )
 نشر من قبل Gianluca Martelloni
 تاريخ النشر 2015
  مجال البحث علم الأحياء
والبحث باللغة English




اسأل ChatGPT حول البحث

We propose two modeling approaches to describe the dynamics of ant battles, starting from laboratory experiments on the behavior of two ant species, the invasive Lasius neglectus and the authocthonus Lasius paralienus. This work is mainly motivated by the need to have realistic models to predict the interaction dynamics of invasive species. The two considered species exhibit different fighting strategies. In order to describe the observed battle dynamics, we start by building a chemical model considering the ants and the fighting groups (for instance two ants of a species and one of the other one) as a chemical species. From the chemical equations we deduce a system of differential equations, whose parameters are estimated by minimizing the difference between the experimental data and the model output. We model the fluctuations observed in the experiments by means of a standard Gillespie algorithm. In order to better reproduce the observed behavior, we adopt a spatial agent-based model, in which ants not engaged in fighting groups move randomly (diffusion) among compartments, and the Gillespie algorithm is used to model the reactions inside a compartment.



قيم البحث

اقرأ أيضاً

Stochastic simulations are one of the cornerstones of the analysis of dynamical processes on complex networks, and are often the only accessible way to explore their behavior. The development of fast algorithms is paramount to allow large-scale simul ations. The Gillespie algorithm can be used for fast simulation of stochastic processes, and variants of it have been applied to simulate dynamical processes on static networks. However, its adaptation to temporal networks remains non-trivial. We here present a temporal Gillespie algorithm that solves this problem. Our method is applicable to general Poisson (constant-rate) processes on temporal networks, stochastically exact, and up to multiple orders of magnitude faster than traditional simulation schemes based on rejection sampling. We also show how it can be extended to simulate non-Markovian processes. The algorithm is easily applicable in practice, and as an illustration we detail how to simulate both Poissonian and non-Markovian models of epidemic spreading. Namely, we provide pseudocode and its implementation in C++ for simulating the paradigmatic Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and a Susceptible-Infected-Recovered model with non-constant recovery rates. For empirical networks, the temporal Gillespie algorithm is here typically from 10 to 100 times faster than rejection sampling.
Animals live in groups to defend against predation and to obtain food. However, for some animals --- especially ones that spend long periods of time feeding --- there are costs if a group chooses to move on before their nutritional needs are satisfie d. If the conflict between feeding and keeping up with a group becomes too large, it may be advantageous to some animals to split into subgroups of animals with similar nutritional needs. We model the costs and benefits of splitting by a herd of cows using a cost function (CF) that quantifies individual variation in hunger, desire to lie down, and predation risk. We model the costs associated with hunger and lying desire as the standard deviations of individuals within a group, and we model predation risk as an inverse exponential function of group size. We minimize the cost function over all plausible groups that can arise from a given herd and study the dynamics of group splitting. We explore our model using two examples: (1) we consider group switching and group fission in a herd of relatively homogeneous cows; and (2) we examine a herd with an equal number of adult males (larger animals) and adult females (smaller animals).
The outbreak of novel coronavirus-caused pneumonia (COVID-19) in Wuhan has attracted worldwide attention. Here, we propose a generalized SEIR model to analyze this epidemic. Based on the public data of National Health Commission of China from Jan. 20 th to Feb. 9th, 2020, we reliably estimate key epidemic parameters and make predictions on the inflection point and possible ending time for 5 different regions. According to optimistic estimation, the epidemics in Beijing and Shanghai will end soon within two weeks, while for most part of China, including the majority of cities in Hubei province, the success of anti-epidemic will be no later than the middle of March. The situation in Wuhan is still very severe, at least based on public data until Feb. 15th. We expect it will end up at the beginning of April. Moreover, by inverse inference, we find the outbreak of COVID-19 in Mainland, Hubei province and Wuhan all can be dated back to the end of December 2019, and the doubling time is around two days at the early stage.
135 - Zhuoran He , Tingtao Zhou 2021
Modern scientific research has become largely a cooperative activity in the Internet age. We build a simulation model to understand the population-level creativity based on the heuristic ant colony algorithm. Each researcher has two heuristic paramet ers characterizing the goodness of his own judgments and his trust on literature. In a population with all kinds of researchers, we find that as the problem scale increases, the contributor distribution significantly shifts from the independent regime of relying on ones own judgments to the cooperative regime of more closely following the literature. The distribution also changes with the stage of the research problem and the computing power available. Our work provides some preliminary understanding and guidance for the dynamical process of cooperative scientific research in various disciplines.
The Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) is an extension of the well-known Vehicle Routing Problem (VRP), which takes into account the dynamic nature of the problem. This aspect requires the vehicle routes to be updated in an on going manner as new customer requests arrive in the system and must be incorporated into an evolving schedule during the working day. Besides the vehicle capacity constraint involved in the classical VRP, DVRPTW considers in addition time windows, which are able to better capture real-world situations. Despite this, so far, few studies have focused on tackling this problem of greater practical importance. To this end, this study devises for the resolution of DVRPTW, an ant colony optimization based algorithm, which resorts to a joint solution construction mechanism, able to construct in parallel the vehicle routes. This method is coupled with a local search procedure, aimed to further improve the solutions built by ants, and with an insertion heuristics, which tries to reduce the number of vehicles used to service the available customers. The experiments indicate that the proposed algorithm is competitive and effective, and on DVRPTW instances with a higher dynamicity level, it is able to yield better results compared to existing ant-based approaches.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا