滑翔飞行器多目标弹道优化的进化-配点混合求解策略

针对高超声速滑翔飞行器弹道多目标优化问题,综合考虑计算效率和精度,结合分解进化算法与配点法提出一种混合求解策略。根据滑翔飞行器动力学模型和弹道设计中需要考虑的约束条件,建立飞行器多目标弹道优化模型。利用控制量离散化方法将多目标弹道优化问题转化为带约束的多目标参数优化问题,并采用罚函数法处理约束条件,随后利用分解多目标进化算法进行求解。为了提高弹道优化的精度,将椭球聚合法与配点法相结合,以多目标进化算法得到的Pareto解作为初始解进行迭代求解。通过典型的复杂约束多目标弹道优化的算例表明,所提出的混合求解策略能够获得满足复杂约束要求的Pareto最优解集,实现有效的多目标弹道优化。

Evolutionary-collocation hybrid optimization strategy for the multiobjective trajectory design of glider flight vehicle

A hybrid optimization method combining the evolutionary algorithm and the collocation method was presented for solving the MTOP (multiobjective trajectory optimization problem) of the hypersonic glider vehicle, in which the efficiency and accuracy were balanced. According to the flight dynamic equation of the glider vehicle and the constraint condition arising in the design of the glider vehicle trajectory, the multiobjective trajectory optimization model was developed. The MTOP was transformed into the multiobjective parameter optimization problem with constraints by using the control variables discretization method; the constraint condition was dealt with the penalty function method, and the MOEA/D (multiobjective evolutionary algorithm based on decomposition) was employed to solve the problem. In order to improve the accuracy of the result, the ellipsoid aggregation method was integrated into the collocation method, in which the Pareto solution produced by the MOEA/D was the initial solution. Simulation results for MTOP with the complicated constraints demonstrate that the proposed hybrid method can generate a set of Pareto solutions, in which the gliding trajectories satisfy all the complicated constraints.