Multi-Label Feature Selection Based on Improved Ant Colony Optimization Algorithm with Dynamic Redundancy and Label Dependence

The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.

Archimedean copula estimation of distribution algorithm based on artificial bee colony algorithm

The artificial bee colony （ABC） algorithm is a com- petitive stochastic population-based optimization algorithm. How- ever, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to in- sufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm （ACEDA） is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA called Archimedean copula estima- tion of distribution based on the artificial bee colony （ACABC） algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six bench- mark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimen- tal results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA and the global best （gbest）-guided ABC （GABC） algorithm in most of the experiments.

基于标签算法的异车型混合集送多属性车辆路径问题研究

文章对异车型混合集送的辆路径问题(Vehicle Routing Problem with heterogeneous fleet,backhaul and mixed-load,VRPHBM)进行研究,提出了一种基于多属性标签的蚁群系统算法(Multi-Label based Ant Colony System简称MLACS)。该算法利用面向对象理念,分别对客户、车辆及其行驶路径构建多属性标签,再通过蚁群算法的搜索规则对客户和车辆标签进行匹配,从而得出满意的车辆行驶路径。通过Solomon标准及其扩展算例和实际案例的验证表明,MLACS具有快速、灵活和稳定等特点,能够很好地解决VRPTW、VRPHBM以及多限制条件的实际应用问题。与本文列出的研究同类型问题文献的其他几种算法相比,MLACS算法在运算时间以及计算结果上明显具有优势,是求解该类问题的有效算法。

RFES: a real-time fire evacuation system for Mobile Web3D

There are many bottlenecks that limit the computing power of the Mobile Web3 D and they need to be solved before implementing a public fire evacuation system on this platform.In this study,we focus on three key problems:(1)The scene data for large-scale building information modeling(BIM)are huge,so it is difficult to transmit the data via the Internet and visualize them on the Web;(2)The raw fire dynamic simulator(FDS)smoke diffusion data are also very large,so it is extremely difficult to transmit the data via the Internet and visualize them on the Web;(3)A smart artificial intelligence fire evacuation app for the public should be accurate and real-time.To address these problems,the following solutions are proposed:(1)The large-scale scene model is made lightweight;(2)The amount of dynamic smoke is also made lightweight;(3)The dynamic obstacle maps established from the scene model and smoke data are used for optimal path planning using a heuristic method.We propose a real-time fire evacuation system based on the ant colony optimization(RFES-ACO)algorithm with reused dynamic pheromones.Simulation results show that the public could use Mobile Web3 D devices to experience fire evacuation drills in real time smoothly.The real-time fire evacuation system(RFES)is efficient and the evacuation rate is better than those of the other two algorithms,i.e.,the leader-follower fire evacuation algorithm and the random fire evacuation algorithm.

Graph Enhanced Transformer for Aspect Category Detection

Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category detection as a flat classification problem. However, aspect categories are inter-related, and they are usually organized with a hierarchical tree structure. To leverage the structure information, this paper proposes a hierarchical multi-label classification model to detect aspect categories and uses a graph enhanced transformer network to integrate label dependency information into prediction features. Experiments have been conducted on four widely-used benchmark datasets, showing that the proposed model outperforms all strong baselines.