ID 原文 译文
25345 为求解该问题，算法采用二维向量编码，即调度向量记录工件的调度顺序，机床分配向量记录工件分配可用机床情况，解码过程充分考虑运输资源、工件间准备时间等约束条件。 To solve the problem, we propose an improved artificial bee colony algorithm, where each solution is represented by a two-dimensional vector, the scheduling vector is to record the operation processing sequence, and the machine assignment vector is to assign the candidate machine for each operation． In the decoding mechanism, the transportation and setup time constraints are investigated．
25346 在局部搜索策略方面，提出了五种不同的调度邻域结构，并根据目标特点，设计了一种机床分配邻域结构。 For the local search approaches, we develop five types of neighborhood structures for the scheduling part, and a well-designed machine assignment neighborhood structure for the machine assignment vector.
25347 为进一步提升算法的全局搜索能力，嵌入了模拟退火接受准则。 To enhance the global searching abilities, the simulated annealing acceptance method is embedded.
25348 实验结果验证了所提算法的优势显著。 Finally, the experiment comparisons verify the performance of the proposed algorithm．
25349 针对水声传感器网络中移动定位算法的误差和鲁棒性问题，提出两种蒙特卡罗移动定位算法:CＲMCL(Circular Ｒing Monte Carlo Localization)和 PＲMCL(Particle Swarm Optimization for Circular Ｒing Monte Carlo Localiza-tion)。 Aiming at error and robustness of localization algorithms in underwater acoustic sensor networks, we proposed two monte carlo mobile localization algorithms: circular ring monte carlo localization (CＲMCL) and particle swarm optimization for circular ring monte carlo localization (PＲMCL)．
25350 CＲMCL 利用 1 跳锚节点构建圆形采样区域和圆环过滤器。 CＲMCL used one-hop anchor nodes to construct circular sampling area and ring filter．
25351 通过定义样本密度得到合理的样本数，论证圆环参数与过滤区域面积的关系。 By defining sampling points density, we obtained reasonable sample number. The relationship between the ring parameter and the filtration area was demonstrated．
25352 通过仿真实验得到合理的圆环参数，并以此构建高效的过滤器，降低定位误差。 Then the reasonable ring parameter was obtained through the simulation experiments， and an efficient filter was constructed to reduce the localization error．
25353 PＲMCL 使用粒子群算法优化 CＲMCL 过滤后的样本，降低了无效样本的数目，增强了算法的鲁棒性。 PＲMCL used particle swarm optimization (PSO) algorithm to optimize the samples filtered by CＲMCL， which reduced the number of invalid samples and improved the robustness of localization．
25354 仿真表明，在不需要额外硬件的情况下，CＲMCL 和 PＲMCL 比蒙特卡罗及其改进算法误差小、鲁棒性强。 The simulation results show that CＲMCL and PＲMCL have lower localization error and better robustness than monte carlo localization and other improved algorithms without additional hardware．