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CALCULATION OF THE PARAMETERS OF MICROSCOPIC OPTICAL POTENTIAL BY ASYNCHRONOUS DIFFERENTIAL EVOLUTION ALGORITHM Zhabitskaya E.I., Zhabitsky M.V.1 , Zemlyanaya E.V., Lukyanov K.V. "Dubna" University, Russi, Dubn; Joint Institute for Nuclear Research, LIT, Russia, Dubna,141980, Joliot-Curie 6 Joint Institute for Nuclear Research, LNP, Russia, Dubna,141980, Joliot-Curie 6; Rock Flow Dynamics, Russi, Moscow

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Differential Evolution (DE) [1] is an evolutionary algorithm to solve derivative-free optimization problems of finding a global minimum, x = xj j=0,...,D-1 , of a function f (x): RD R: f (x )f (x) x . Classical Differential Evolution (CDE) [2] employs a synchronous generation-based evolution strategy. A novel Asynchronous Differential Evolution (ADE) [3] incorporates mutation, crossover and selection operations into an asynchronous strategy. It is well suited for parallel optimization. Constraints on control parameters for best/1/bin strategy of CDE and for four new strategies of a novel Asynchronous Differential Evolution (ADE) are founded in [4]. In oure work the spesification of parameters of microscopic optical potential for elastic + N scattering in the high-energy approximation [5] using elastic scattering data is done by the new ADE algorithm. References. 1. Price, K.V., Storn, R.M.: A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces.// J. of Global Optimization 11,1997, Pp. 341-359. 2. Price, K.V., Storn, R.M., Lampinen, J.A: Differential Evolution: A Practical Approach to Global Optimization. -- Springer-Verlag, Berlin Heidelberg, 2005. 3. Zhabitskaya, E.I., Zhabitsky, M.V.: Asynchronous Differential Evolution. // Springer 's Lecture Notes in Computer Science, 2011 -- in press. 4. Zhabitskaya, E.I.: Constraints on Control Parameters of Asynchronous Differential Evolution.// Springer 's Lecture Notes in Computer Science, 2011 -- in press. 5. .., .., .., ... K + // , 73, 2010, 1489­1496.