该算法采用非线性路径长度计算方法。
This algorithm is used nonlinear method length gauge calculates a method.
该算法采用了更加有效的双向采样策略。
该算法采用本体论和相关反馈技术相结合的方法。
And the method combines ontology technology and relative feedback technology.
该算法采用新的启发式变异算子和局部搜索算子。
Heuristics mutation operator and local search scheme are designed in the algorithm.
该算法采用原本用于空间插值的普通克里金方法创建虚拟井。
The algorithm applied the ordinary Kriging method that was originally used in spatial interpolation to construct virtual Wells.
该算法采用简单的查表法来完成区间再分割中的计算关键性运算。
The algorithm relied on simple table lookups for performing the computationally critical operations of interval subdivision.
该算法采用乒乓缓存策略，使得数据的传输和编码能够同时进行。
The strategy of Ping-Pong buffer allows data transfer and code to perform simultaneously.
该算法采用一种新的矩阵映射方式，该算法操作简单且容易实现。
The algorithm is simple in operation and easy to implement by using a new suggested mapping scheme.
该算法采用期望最大化(EM)聚类分析方法来识别分类及其顺序。
The algorithm USES the Expectation Maximization (EM) clustering method to identify clusters and their sequences.
该算法采用矩阵编码方案，建立罚函数满足课表问题中的多重约束条件。
Matrix used as encoding of the algorithm, and a penalty function is adopted to satisfy the multi-constraint.
该算法采用频域分块估计候选基音周期的范围，提高了算法的计算速度。
The proposed algorithm adopts frequency partition to estimate the range of pitch period, improving the computational speed.
该算法采用了简单快速的自适应空间域滤波和代数运算，提高了图像质量。
Simple, fast and adaptive spatial filtering and arithmetic operation are adopted by the algorithm and image quality is improved.
该算法采用分段线性表示，同时使用改进的模式距离来度量序列间的距离。
The algorithm adopts the piecewise linear representation and improved series pattern distance measure.
该算法采用了混合编码，改进了适应度函数和交叉操作，扩大了搜索范围。
The algorithm adopts hybrid coding, does non-monotonic transformation to the fitness function and improves the crossover operation, expanding the searching scope.
该算法采用一种新的噪声检测方法将图像中的像素分为信号点和噪声点两类。
In the algorithm, a new noise detection scheme is adopted to separate the pixels in the image into signal pixels and noise pixels.
该算法采用了两两竞赛的选择算子、聚集度、违约度来处理多目标约束优化。
This paper proposes genetic algorithms using tournament selection, niche count, and violation degree to solve this problem.
该算法采用可变长度染色体（路由串）和它的基因（节点）应用于编码问题。
Variable-length chromosomes (routing strings) and their genes(nodes) have been used for encoding the problem.
该算法采用量子比特概率编码方式构造染色体，由量子旋转门操作实现种群进化。
This algorithm codes the chromosomes in the way of quantum bit probability, and makes the population evolve by the operation of quantum gate.
该算法采用两种虚拟种群的方法对常规遗传算法及浮点数遗传算法的种群实行改进。
The advanced GA USES two virtual population methodologies to process the population of standard binary and real code GA for RPO problem.
该算法采用分段线性化定量求解控制量，实现了定性与定量相结合的智能控制方法。
This algorithm adopts the separated linear control technique and combines qualitative analysis with quantitative analysis.
本文提出EEPR算法，该算法采用一个节点剩余能量和ETX值作为路由度量，同时。
In this paper, we proposed EEPR algorithm which employs both the residual energy of a node and the ETX value as the routing metrics, at the same time.
该算法采用浮点编码方式，定义了二元实向量类型的适应值及适应值间的严格偏序关系。
The system uses real-number encoding, and defines the fitness value by a two-dimensional vector, inducing a strict partial ordering on the population individuals.
该算法采用后验信息修正模型的噪声方差和马尔可夫转移矩阵，使IMM具有自适应能力。
The algorithm USES posterior information to modify model's noise variance and markov transition matrix, so as to make IMM have adaptive ability.
该算法采用了相位梯度算法(PGA)的处理结构，利用了多个距离单元上的散射点信号。
In the algorithm, the phase gradient algorithm (PGA) was performed, and the signals of multiple scatters on a few range bins were utilized.
而且，该算法采用了有效的迭代剪枝技术，大大压缩了候选模式的数量，降低了通信代价。
Moreover, this algorithm adopts an effective iterative and pruning strategy that could compress the scale of candidate patterns and reduce the communication cost.
该算法采用强化学习中值迭代策略，在运行中能够从环境中获取相应知识，提高其搜索能力。
By adopting the value iterative strategies of reinforcement learning, the algorithm can absorb the corresponding knowledge from its environment during its running and improve its search ability.
该算法采用更适合无功优化特点的分组整数实数混合编码方式，并采用映射法计算适应度函数。
A grouped, integer and real number mixed coding method is applied in this algorithm. The mapped-method is adopted to calculate the fitness function.
该算法采用更适合无功优化特点的分组整数实数混合编码方式，并采用映射法计算适应度函数。
A grouped, integer and real number mixed coding method is applied in this algorithm. The mapped-method is adopted to calculate the fitness function.
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