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针对边值固定动态优化问题的数值求解,提出了一种集成随机性方法与确定性方法的一种新的混合算法。迭代遗传算法(IGA)把初始种群及繁衍产生的后代不断供给两点梯度法,两点梯度法以其为初值搜索满足边值固定约束的可行控制策略并回送给迭代遗传算法,迭代遗传算法则根据可行控制策略对应的目标函数值进行选择与进化操作。该混合算法简便易行。实例研究显示了该混合算法的可行性与稳健性,能以足够的精度满足边值约束。
Aiming at the numerical solution of the fixed-value dynamic optimization problem, a new hybrid algorithm that combines the stochastic and deterministic methods is proposed. The iterative genetic algorithm (IGA) supplies the initial population and multiplying generations with two-point gradient method. The two-point gradient method uses it as an initial value to search for feasible control strategies that satisfy the fixed constraint of the boundary value and sends it back to the iterative genetic algorithm Then select and evolve according to the objective function value corresponding to the feasible control strategy. The hybrid algorithm is simple and easy. The case study shows the feasibility and robustness of the hybrid algorithm, which can satisfy the boundary value constraints with sufficient accuracy.