en تخصصي Special كاربردي Applicable <p>&lrm;Here&lrm;, &lrm;we give a two phases algorithm based on integrating differential evolution (DE) algorithm with modified hybrid genetic algorithm (MHGA) for solving the associated nonlinear programming problem of a nonlinear optimal control problem&lrm;. &lrm;In the first phase&lrm;, &lrm;DE starts with a completely random initial population where each individual&lrm;, &lrm;or solution&lrm;, &lrm;is a random matrix of control input values in time nodes&lrm;. &lrm;After phase 1&lrm;, &lrm;to achieve more accurate solutions&lrm;, &lrm;we increase the number of time nodes&lrm;. &lrm;The values of the associated new control inputs are estimated by linear or spline interpolations using the curves computed in the phase 1&lrm;. &lrm;In addition&lrm;, &lrm;to maintain the diversity in the population&lrm;, &lrm;some additional individuals are added randomly&lrm;. &lrm;Next&lrm;, &lrm;in the second phase&lrm;, &lrm;MHGA starts by the new population constructed by the above procedure and tries to improve the obtained solutions at the end of phase 1&lrm;. &lrm;We implement our proposed algorithm on some well-known nonlinear optimal control problems&lrm;. &lrm;The numerical results show the proposed algorithm can find almost better solution than other proposed algorithms&lrm;.</p> Nonlinear optimal control problem‎, ‎Differential evolution‎, ‎Modified hybrid genetic algorithm‎, ‎Successive quadratic programming‎, ‎Spline interpolation‎. 47 67 http://ijmsi.ir/browse.php?a_code=A-10-689-2&slc_lang=en&sid=1 S. Nezhadhosein s_nezhadhossin@yahoo.com 10031947532846003931 10031947532846003931 Yes Payame Noor University, Tehran A. Heydari 10031947532846003932 10031947532846003932 No Payame Noor University, Tehran R. Ghanbari 10031947532846003933 10031947532846003933 No Ferdowsi University of Mashhad