Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

А Novel AP Placement Algorithm Based on User Distribution for Indoor WLAN System

AP deployment is significant for indoor WLAN system to achieve seamless coverage. The available algorithms do not take user distribution into consideration so that poor user coverage and imbalanced network load occur. Therefore, this paper proposed a novel AP placement algorithm to bridge the AP deployment with user distribution. The proposed algorithm employs statistics theory to model the user distribution as its location and probability. Then we obtain the AP location based on the fuzzy C-clustering algorithm. The proposed algorithm is practical for implementation, which means the actual signal transmission isn't required in our proposed method. The simulation results show that the proposed algorithm could automatically achieve a good AP deployment with different user distribution, and provide a good performance in the maximum users and AP load balance in WLAN.

大型变压器螺旋式绕组扭转变形的仿真研究

变压器发生短路时,螺旋式绕组所受的短路电动力将包含很大的切向力.轴向电流分量与辐向漏磁场共同作用会产生较大的扭矩.文章中建立了螺旋式绕组的二维和三维磁场-结构力场耦合模型,对变压器的漏磁场、螺旋式绕组的切向力进行了仿真计算.得到了变压器短路时低压螺旋式绕组的切向力大小、分布规律.通过两种建模结果的对比分析,检验了计算方法的可靠性.在此基础上,研究了螺旋角和轴向预压紧力对螺旋式绕组切向位移的影响,得到了产品设计中减小绕组切向位移的-些应用结论.

A Semi-Supervised WLAN Indoor Localization Method Based on l1-Graph Algorithm

For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be collected in offline phase. Therefore,collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper,the traditional semisupervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed,and the result shows that the k-NN or ε-NN graph are sensitive to data noise,which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem,it proposes a l1-graph-algorithm-based semi-supervised learning( LG-SSL) indoor localization method in which the graph is built by l1-norm algorithm. In our system,it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase,and then uses LG-SSL to estimate user's location in online phase. Extensive experimental results show that,benefit from the robustness to noise and sparsity ofl1-graph,LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.