ID 原文 译文
26145 实验结果证明了 ＲSA 的有效性。 The effectiveness and efficiency of the ＲSA for the k-SPPGTW are demonstrated by some preliminary experimental results．
26146 同时存在区间显式指标和模糊隐式指标的高维混合指标优化问题是一类难以求解的不确定多目标优化问题。 The multidimensional hybrid indices optimization problem is a kind of uncertainty multi-objective optimization problems that is difficult to solve．
26147 针对该问题，首先，分别对高维显式指标和隐式指标的主要参数按确定性多目标优化，根据获得的相关权值，将高维显式指标和高维隐式指标分别降维成一维等效区间适应值和一维等效模糊适应值，二者合成个体等效指标体;然后，依据等效指标体的占优情况，通过确定自适应参考点和偏好区域面积选择个体; First，we can get relevant weights by optimizing the main parameters of explicit and implicit in-dices. According to these weights, multidimensional explicit indices can be reduced to an equivalent-interval fitness, and multidi-mensional implicit indices can be reduced to an equivalent-fuzzy fitness. Equivalent-interval fitness and equivalent-fuzzy fitness can be synthesized to an equivalent-index body. Then, we select advantage individual on the basis of equivalent-index bodies dominant situation according to adaptive reference point and preference area size．
26148 最后，在大规模种群 NSGA-II 范式下，采用隐式指标估计策略和种群聚类方法实现交互式进化优化算法。 Finally, we adopt an implicit-indices estimation strategy with cluster method to realize interactive evolutionary algorithm within the framework of NSGA-II．
26149 将本文算法应用于 2 种混合性能指标优化问题，验证所提算法的有效性和泛化性。 The proposed algorithm is applied to two optimization problems with hybrid indices, and the results validate its efficiency and generalization．
26150 为了提高多目标粒子群优化算法解的分布性，文中提出了一种自适应分解式多目标粒子群优化算法(Adaptive Multiobjective Particle Swarm Optimization based on Decomposed Archive，AMOPSO-DA)。 To improve the distribution performance of multiobjective particle swarm optimization algorithm, an adaptive multiobjective particle swarm optimization algorithm, based on the decomposed archive, named AMOPSO-DA, is developed in this paper．
26151 首先，设计了一种基于优化解空间分布信息的外部档案更新策略，有效提升了 AMOPSO-DA 的空间搜索能力; First, an external archive update strategy, based on the spatial distribution information of optimal solutions, is designed to improve the searching ability of AMOPSO-DA．
26152 其次，提出了一种基于粒子进化方向信息的飞行参数调整方法，有效平衡了 AMOPSO-DA 的探索和开发能力。 Second, an adaptive flying parameter adjustment strategy, based on the evolutionary direction information of each particle, is proposed to balance the exploration ability and the exploitation ability．
26153 最后，将提出的 AMOPSO-DA 应用于多目标优化问题，实验结果表明，文中提出的 AMOPSO-DA 能够获得分布性较好的优化解。 Finally, this proposed AMOPSO-DA is applied to some multiobjective optimization problems. The experiment results demonstrate that AMOPSO-DA can obtain well-distributed optimal solutions．
26154 当前社会网络已取代传统媒体成为信息交流的重要平台，社会网络中的信息具有传播速度快，范围广，即时性强等优点。 The current social network has replaced traditional media as an important platform for information exchange. The information in social networks has the advantages of fast dissemination, wide range, and strong immediacy．