以深度优先搜索作为基本算法，用路径删除和结点删除方法产生多重解，用最小成本法求出最优解。
Based on depth first search, the article USES route deletion and node deletion methods to produce multi-solutions, and then determine the optimum solution with least costing.
本文分析、比较了现有的几种路径搜索算法，最后采用一种比较简单的单门限算法，并通过仿真得到参数最优值。
After analyzing and comparing some path search algorithms, the paper ultimately adopted a simple single threshold algorithm and attained optimal parameters through simulation.
针对现有大区域范围路径规划算法存在的一些问题，提出一种限制搜索区域的多比例尺最优路径规划算法。
In allusion to some problems of existing route planning algorithm of large area, a multi-scale optimal route planning algorithm within restricted searching area was proposed.
然后，用启发式宽度优先搜索算法进行路径规划，产生从初始位置到目标位置的最优路径，引导虚拟人对环境进行漫游。
Then, a heuristic breadth-first search is applied for path planning to find an optimal path from an original position to an aim, directing virtual human walkthrough in environments.
与经典优化算法不同的特点在于，遗传算法是从多个初始点开始寻优，沿多路径搜索实现全局或准全局最优。
The GA uses a population of points at a time in contrast to the single point approach by the traditional ones, and will guarantee to find the global optimum.
所以，最优路径规划最终都可以归结为在特定的道路网络中搜索总代价最小的目标路径问题。
So the most optimal path planning is regard as the problem of searching for roads which have the least cost in a road net.
路径规划是按照某一性能指标搜索一条从起始状态到目标状态的最优或近似最优的无碰路径。
Path planning is searching a optimal path without touch form the start to the end under way a performance indicators.
包括启发式路径搜索算法、基于原始-对偶算法的最优路径选择算法、信道分配调整算法等三个部分。
This algorithm consists of heuristic-based search algorithm, primal-dual-based optimal path selection algorithm and channel assignment adjustment algorithm.
在求解随机最优路径方面，其问题分两种情形：一种以成功概率为优化目标，搜索约束条件下完成运输任务最大概率的路径；
Two scenarios are considered: one sets the success probability as the objective and tries to find out the route with the maximum probability subject to time constraint;
在求解随机最优路径方面，其问题分两种情形：一种以成功概率为优化目标，搜索约束条件下完成运输任务最大概率的路径；
Two scenarios are considered: one sets the success probability as the objective and tries to find out the route with the maximum probability subject to time constraint;
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