Browsing by Author "Knobloch Roman"
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- ItemConvergence Rate of the Modified Differential Evolution Algorithm(AMER INST PHYSICS, 2 HUNTINGTON QUADRANGLE, STE 1NO1, MELVILLE, NY 11747-4501 USA, 2017-01-01) Mlýnek Jaroslav; Knobloch Roman; Srb Radek
- ItemForms of the problem presentation in the school practice.(2016-01-01) Břehovský Jiří; Knobloch Roman
- ItemImproving convergence properties of a differential evolution algorithm(American Institute of Physics Inc., 2016-01-01) Knobloch Roman; Mlýnek Jaroslav; Srb Radek
- ItemMathematical Model of the Metal Mould Surface Temperature Optimization(AIP Publishing LLC, 2015-01-01) Mlýnek Jaroslav; Knobloch Roman; Srb Radek
- ItemMethodology for measurement and elimination of robot positional errors in 3D printing(2019-01-01) Svrček Ján; Tůma Petr; Martinec Tomáš; Mlýnek Jaroslav; Knobloch Roman
- ItemThe model of non-stationary heat conduction in a metal mould(Springer, Cham, 2018-01-01) Mlýnek Jaroslav; Knobloch Roman
- ItemMODEL OF SHELL METAL MOULD HEATING IN THE AUTOMOTIVE INDUSTRY(Institute of Mathematics, Czech Academy of Sciences, 2018-01-01) Mlýnek Jaroslav; Knobloch Roman
- ItemOptimization of a heat radiation intensity and temperature field on the mould surface(European Council for Modelling and Simulation, 2016-01-01) Mlýnek Jaroslav; Knobloch Roman; Srb Radek
- ItemProbabilistic analysis of the convergence of the differential evolution algorithm(Fak. dopravní, ČVUT Praha, 2021-01-01) Knobloch Roman; Mlýnek Jaroslav
- ItemProduction of Polymer Frame Composites Using Industrial Robots(Czech Technical University in Prague, Faculty of Electrical Engineering, 2021-01-01) Mlýnek Jaroslav; Petrů Michal; Martinec Tomáš; Knobloch Roman
- ItemTemperature field Optimization on the Mould Surface(Springer Verlag, 2015-01-01) Mlýnek Jaroslav; Knobloch Roman; Srb Radek
- ItemThe classic differential evolution algorithm and its convergence properties(ACAD SCIENCES CZECH REPUBLIC, 2017-01-01) Mlýnek Jaroslav; Knobloch Roman; Srb RadekDifferential evolution algorithms represent an up to date and efficient way of solving complicated optimization tasks. In this article we concentrate on the ability of the differential evolution algorithms to attain the global minimum of the cost function. We demonstrate that although often declared as a global optimizer the classic differential evolution algorithm does not in general guarantee the convergence to the global minimum. To improve this weakness we design a simple modification of the classic differential evolution algorithm. This modification limits the possible premature convergence to local minima and ensures the asymptotic global convergence. We also introduce concepts that are necessary for the subsequent proof of the asymptotic global convergence of the modified algorithm. We test the classic and modified algorithm by numerical experiments and compare the efficiency of finding the global minimum for both algorithms. The tests confirm that the modified algorithm is significantly more efficient with respect to the global convergence than the classic algorithm.