基于混沌映射协同差分进化的最优路径规划

Optimal Trajectory Planning Based on Chaos Mapping Cooperative Differential Evolution

为提高轨迹优化的精度,提出一种基于混沌映射的协同差分进化算法。该模型不依赖数学模型和梯度信息,即可对优化目标进行分组寻优和信息共享,实现快速轨迹优化。在寻优后期的子代构建过程中引入混沌映射,使算法在保持种群多样性的同时,平衡了全局搜索能力和局部搜索能力。通过标准函数对比测试,所提算法在全局寻优能力方面取得明显的改进效果,将其应用于解决轨迹优化的实际问题,可以高效地获得全局最优轨迹,有效地提升差分进化算法的性能。

In order to improve the precision of trajectory optimization,a cooperative differential evolution algorithm based on chaotic mapping is proposed. The algorithm does not rely on mathematical model and gradient information. It can optimize by group and share information for the optimizing target,and achieve rapid trajectory optimization. We introduce the chaos mapping into the process of children construction in the later period of optimization,which makes the algorithm keep the diversity of the population and balance the ability of global search and local search. By comparing with standard functions,we validate the global optimization ability of the cooperative differential evolution algorithm. Furthermore,the practical application of the algorithm is applied to the trajectory optimization,which can obtain the global optimal trajectory.The performance of differential evolution algorithm is improved effectively.