Abstract

A novel time-optimal memetic whale optimization algorithm (WOA) integrating the Gauss pseudo-spectral methods (GPM), is proposed in this paper for the hypersonic vehicle entry trajectory optimization problem with no-fly zones. The WOA is featured with the strong global search ability and non-sensitive to the initial values, but also shows poor searching convergence speed around the global optimum. Conversely, GPM may be sensitive to the initial solution and easily trapped in a local optimum, but it also possesses more rapid convergence speed around the optimum and higher searching accuracy. Thus, a memetic optimization algorithm which contains a two-stage approach mechanism is proposed for searching the global optimum. The first searching stage, which is driven by an improved WOA (IWOA), works as an initializer of the entire searching due to its strong global search ability and non-sensitive to the initial values. The local optimum reservation and adaptive amplitude factor updating strategy are established to improve the convergent speed and the global search ability of the WOA. Once the changing of fitness value satisfies the predefined criterion, the next searching stage driven by GPM will take the place of the IWOA to expedite the search process around optimum and to obtain a precise global optimal solution. By this hybrid way, the proposed optimization algorithm may find an optimum more quickly and accurately. Simulation results show the proposed algorithm possesses faster convergence speed, higher accuracy, and stronger robustness for the hypersonic vehicle entry trajectory optimization.

中文翻译：

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具有禁飞区的高超声速飞行器再入轨迹的时间最优模因鲸优化算法

摘要

提出了一种结合高斯伪谱方法（GPM）的时空最优模因鲸优化算法（WOA），以解决超音速飞行器进入飞行区禁飞的问题。WOA具有强大的全局搜索能力，并且对初始值不敏感，但在全局最优值附近也显示出较差的搜索收敛速度。相反，GPM可能对初始解很敏感，很容易陷入局部最优值，但它在最优值附近具有更快的收敛速度和更高的搜索精度。因此，提出了一种包含两步逼近机制的模因优化算法来寻找全局最优解。由改进的WOA（IWOA）驱动的第一搜索阶段，由于其强大的全局搜索能力并且对初始值不敏感，因此可以用作整个搜索的初始化程序。建立局部最优预留和自适应幅度因子更新策略，以提高WOA的收敛速度和全局搜索能力。一旦适应度值的变化满足预定标准，则由GPM驱动的下一个搜索阶段将代替IWOA，以加快围绕最优的搜索过程并获得精确的全局最优解。通过这种混合方式，所提出的优化算法可以更快，更准确地找到最优值。仿真结果表明，该算法具有较高的收敛速度，较高的精度和较强的高超声速车辆进入轨迹优化鲁棒性。