A hybrid backtracking search optimization algorithm for nonlinear optimal control problems with complex dynamic constraints

摘要: Nonlinear optimal control (NOC) problem with complex dynamic constraints (CDC) is difficult to compute even direct method. In this paper, a hybrid two-stage approach integrating an improved backtracking search optimization algorithm (IBSA) the hp-adaptive Gauss pseudo-spectral methods (hpGPM) proposed. Firstly, BSA enhance its convergent speed and global ability, by adopting harmony strategy adaptive amplitude factor individual optimum fitness feedback. Then, at beginning stage of process, initialization generator constructed using IBSA find near solution. When change in function approaches predefined value which small enough, process replaced hpGPM accelerate accurate By way, able more quickly accurately. Two NOC problems CDC are examined proposed algorithm, corresponding Monte Carlo simulations conducted. The comparison results show achieves better performance speed, accuracy robustness. feedback.The gauss pseudospectral method adopted overcome drawbacks fixed discrete points generated during conventional controls parameterization.A novel