ID 原文 译文
25265 针对传统多目标优化算法在求解 Pareto 解集时存在全局搜索能力与局部寻优能力无法得到有效平衡的问题，提出了一种基于多邻居结构的自适应元胞差分算法。 To solve the problem that the global search ability and local search ability of traditional multi-objective evolutionary algorithm cannot be effectively balanced when solving the Pareto solution set, an adaptive cellular differential evolutionary algorithm based on multi-neighborhood structure is proposed．
25266 该算法在保留传统元胞差分算法进化特点的基础上，使用更加丰富的多邻居结构替换原有的单一邻居结构，并且依据相应元胞个体的性能优劣来对其邻居结构进行选择分配。 Based on the characteristics of the traditional cellular differential evolutionary algorithm, the improved algorithm uses a richer multi-neighbor structure to replace the original single neighbor structure, and the neighbor structure is adjusted reasonably according to the performance of the corresponding individual．
25267 同时，面对进化过程中的复杂性能需求，算法定义了一种周期性变化的变异策略来实现不同进化阶段的自适应调节。 At the sametime, in the face of the complex requirements in the whole evolution process，the algorithm defines a mutation strategy with periodic variation to realize the adaptive adjustment in different evolution stages．
25268 最后，利用 DTLZ 系列测试函数对算法性能进行测试，并通过与四种经典的多目标优化算法相比较，证明了改进后的算法拥有更好的收敛性与分布性。 Finally, the DTLZ series of test functions are used to test the performance of the algorithm. Compared with four classical multi-objective optimization algorithms, it is proved that the improved algorithm has better convergence performance and diversity of solution set．
25269 利用到达角(Angel Of Arrival，AOA)进行目标定位是被动监测领域广泛采用的技术之一。 Angle of arrival based positioning technology is commonly used in the field of passive surveillance．
25270 然而，在多基站多目标环境中，通常难以直接获得 AOA 量测数据间的关联关系，因此需要在目标定位前进行有效的量测数据关联。 However, in a multisensor-multitarget situation, it is difficult to determine the association between measurements directly, and an effective data association is required before target positioning．
25271 本文针对 AOA 量测数据的关联问题，提出了一种基于多向次序关联的 AOA 量测数据关联方法。 Aiming to solve the problem, this paper presents a new data association approach of angel of arrival (AOA) based on multidirection-ordered association．
25272 该方法首先构建了一种用于描述数据间关联程度的代价函数，并利用雅克比方法估计误差分量的方差。 Firstly, the approach designs a cost function to describe the possibility of association between measurements, and uses the Jacobian to estimate the variance of components of error vector．
25273 其次结合分配算法和寻优思想，分别计算局部关联方向和基站的关联次序，最终得到关联结果。 Secondly, to compute the association results, the assignment and optimization ideas are used to compute the directions of partial association and the order of association between sensors, respectively．
25274 实验验证了本文方法对密集目标和随机目标量测数据关联的有效性。 The simulation results show that the approach is effective for the association of measurements of intensive targets and random targets．