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UID:20260412T115750EDT-5117ie4ePa@132.216.98.100
DTSTAMP:20260412T155750Z
DESCRIPTION:Dynamic Games and Applications Seminar\n\nSpeaker: Massimiliano
  Ferrara – Mediterranea University of Reggio Calabria\, Italy\n\nWebinar l
 ink\n	Webinar ID: 841 3695 9888\n	Passcode: 120834\n\nAbstract:\n\nIn this t
 alk we are going to presents a novel evolutionary computation-based Padeě 
 approximation (EPA) scheme for constructing a closed-form approximate solu
 tion of a nonlinear dynamical model of Covid-19 disease with a crowding ef
 fect that is a growing trend in epidemiological modeling. In the proposed 
 framework of the EPA scheme\, the crowding effect-driven system is transfo
 rmed to an equivalent nonlinear global optimization problem by assimilatin
 g Padeě rational functions. The initial conditions\, boundedness\, and pos
 itivity of the solution are dealt with as problem constraints. Keeping in 
 view the complexity of formulated optimization problem\, a hybrid of diffe
 rential evolution (DE) and a convergent variant of the Nelder-Mead Simplex
  algorithm is also proposed to obtain a reliable\, optimal solution. The c
 omparison of the EPA scheme results reveals that optimization results of a
 ll formulated optimization problems for the Covid-19 model with crowding e
 ffect are better than those of several modern metaheuristics. EPA-based so
 lutions of the Covid-19 model with crowding effect are in good agreement w
 ith those of a well-practiced nonstandard finite difference (NSFD) scheme.
  The proposed EPA scheme is less sensitive to step lengths and converges t
 o true equilibrium points unconditionally.\n
DTSTART:20220127T160000Z
DTEND:20220127T170000Z
LOCATION:CA\, ZOOM
SUMMARY:Evolutionary optimized Padeě approximation scheme for analysis of C
 ovid-19 model with crowding effect
URL:https://www.mcgill.ca/cim/channels/event/evolutionary-optimized-padee-a
 pproximation-scheme-analysis-covid-19-model-crowding-effect-337076
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