:: Volume 12, Number 1 (4-2017) ::
IJMSI 2017, 12(1): 47-67 Back to browse issues page
Integrating Differential Evolution Algorithm with Modified Hybrid GA for Solving Nonlinear Optimal Control Problems
S. Nezhadhosein , A. Heydari, R. Ghanbari

‎Here‎, ‎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. ‎In the first phase‎, ‎DE starts with a completely random initial population where each individual‎, ‎or solution‎, ‎is a random matrix of control input values in time nodes‎. ‎After phase 1‎, ‎to achieve more accurate solutions‎, ‎we increase the number of time nodes‎. ‎The values of the associated new control inputs are estimated by linear or spline interpolations using the curves computed in the phase 1‎. ‎In addition‎, ‎to maintain the diversity in the population‎, ‎some additional individuals are added randomly‎. ‎Next‎, ‎in the second phase‎, ‎MHGA starts by the new population constructed by the above procedure and tries to improve the obtained solutions at the end of phase 1‎. ‎We implement our proposed algorithm on some well-known nonlinear optimal control problems‎. ‎The numerical results show the proposed algorithm can find almost better solution than other proposed algorithms‎.

Keywords: Nonlinear optimal control problem‎, ‎Differential evolution‎, ‎Modified hybrid genetic algorithm‎, ‎Successive quadratic programming‎, ‎Spline interpolation‎.
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Type of Study: Applicable | Subject: Special

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Volume 12, Number 1 (4-2017) Back to browse issues page