基于改进鸽群算法的高超声速飞行器轨迹优化

采用基于罚函数思想的约束处理技术改进鸽群智能算法,应用于复杂的带约束的飞行器轨迹优化问题。以高超声速飞行器爬升段轨迹优化为例,建立其包含微分方程约束、路径约束和终端约束的优化数学模型,通过罚函数构造算法的适应值函数并对优化变量添加边界强约束,将改进的鸽群智能(PIO)算法和粒子群(PSO)算法对高超声速飞行器爬升段优化数学模型进行对比仿真分析。仿真结果表明,改进的鸽群智能算法在解决此类复杂带约束优化问题中展现出了更好的优化效率,具有良好的工程应用价值。

Hypersonic vehicle trajectory optimization based on improved pigeon-inspired optimization algorithm

Based on the constraint processing technology of penalty function, pigeon-inspired optimization(PIO) is improved to deal with the complexly constrained optimization problems such as hypersonic vehicle trajectory optimization.For the climbing trajectory optimization for hypersonic vehicle, optimization model containing differential constraints, trajectory constraints and terminal constraints is established.With modified fitness function by the penalty function concept and enforced constraints of optimal variables, a comparative simulation of PIO and PSO is performed on the above-mentioned optimization problem.Simulation results show that more optimal efficiency and engineering value of PIO is shown to solve the complexly constrained optimization problems.