基于GAAA算法的深海集矿车的最优路径规划

Study of Mining Vehicle Optimal Path Planning Control Based on GAAA Algorithm

针对提高深海集矿车的集矿效率,实现最优路径规划的目标,建立了从起始点到达目的点时,集矿机所需时间最短、耗能最少的多目标优化问题的模型,并通过对子目标加权将多目标优化问题转换成单目标优化;在蚁群算法的基础上,采用了将遗传算法与蚁群算法相融合的算法,即GAAA算法;对集矿车作业路径进行寻优控制,在实现高集矿覆盖率和集矿效率的同时,提高了集矿车整机作业效率;仿真实验表明,GAAA算法用于集矿车路径寻优是可行的和有效的。

In order to improve the efficiency of deep ocean mining vehicle, in this article we build a multi--objective optimal model with minimal time and energy consumption for the vehicle, then transfer the multi--objective optimization to a single objective through setting up a set of weight parameters, The application of genetic algorithms （GA） inosculated with ant algorithms （AA） is studied . The simulation results show that the algorithm greatly improves the computational efficiency and it is feasible.