Applied Soft Computing最新文献

{"title":"Heterogeneous graph neural network with hierarchical attention for group-aware paper recommendation in scientific social networks","authors":"Gang Wang ,&nbsp;Li Zhou ,&nbsp;Junqiao Gong ,&nbsp;Xuan Zhang","doi":"10.1016/j.asoc.2024.112448","DOIUrl":"10.1016/j.asoc.2024.112448","url":null,"abstract":"<div><div>In recent years, the academic groups established in Scientific Social Networks (SSNs) have not only facilitated collaboration among researchers but also enriched the relations in SSNs, providing valuable information for paper recommendation tasks. However, existing paper recommendation methods rarely consider group information and they fail to fully leverage the group information due to the heterogeneous and complex relations between researchers, papers, and groups. In this paper, a heterogeneous graph neural network with hierarchical attention, named HHA-GPR, is proposed for group-aware paper recommendation. Firstly, a heterogeneous graph is constructed based on the interactions of researchers, papers, and groups in SSNs. Secondly, a random walk-based sampling strategy is utilized to sample highly correlated heterogeneous neighbors for researchers and papers. Thirdly, a hierarchical attention network with intra-type and inter-type attention mechanisms is designed to aggregate the sampled neighbors and comprehensively model the complex relations among the heterogeneous neighbors. More specifically, an intra-type attention mechanism is introduced to aggregate the neighbors of the same type, and an inter-type attention mechanism is employed to combine the embeddings of different types to form the ultimate node embedding. Extensive experiments are conducted on the real-world CiteULike and AMiner datasets, and the experimental results demonstrate that our proposed method outperforms other benchmark methods with an average improvement of 5.3 % in Precision, 5.6 % in Recall, and 5.1 % in Normalized Discounted Cumulative Gain (NDCG) across both datasets.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112448"},"PeriodicalIF":7.2,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142653650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GA based construction of maximin latin hypercube designs for uncertainty design of experiment with dynamic strategy management","authors":"Dong Liu ,&nbsp;Shaoping Wang ,&nbsp;Jian Shi ,&nbsp;Di Liu","doi":"10.1016/j.asoc.2024.112454","DOIUrl":"10.1016/j.asoc.2024.112454","url":null,"abstract":"<div><div>Flexible construction of maximin Latin Hypercube Designs (LHDs) meets the NP-hard problem known as the Maximum Diversity Problem (MDP). Traditional algorithms, such as Genetic Algorithms (GAs), face challenges like premature convergence and limited optimization performance, particularly due to the number of hyperparameters that require to be tuned and their limited ability to generalize across diverse problem domains. Thus, this paper proposed a self-adaptive method called GA with Dynamic Strategy Management for the flexibly and efficient construction of maximum LHDs. This method is based on premature convergence prediction, dynamic triggered optimization strategies, and performance control. Furthermore, nearly all critical factor, such as population initialization and selection, crossover, mutation, and local search, are involved in this framework. By comparing this method to LHD construction techniques (Simulated Annealing, Enhanced Stochastic Evolution, and Latin Hypercube Particle Swarm Optimization), as well as the adaptive GAs and state-of-the-art metaheuristics, the algorithm demonstrates superior performance due to its optimized structural self-organization.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112454"},"PeriodicalIF":7.2,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiscale spatio-temporal feature fusion based non-intrusive appliance load monitoring for multiple industrial industries","authors":"Lin Lin ,&nbsp;Jiang Liu ,&nbsp;Nantian Huang ,&nbsp;Shilin Li ,&nbsp;Yunshan Zhang","doi":"10.1016/j.asoc.2024.112445","DOIUrl":"10.1016/j.asoc.2024.112445","url":null,"abstract":"<div><div>The appliance types and power consumption patterns vary greatly across different industries. This can lead to unstable identification results of traditional appliance load monitoring methods in different industries. A non-intrusive appliance load monitoring (NIALM) method for multiple industries based on multiscale spatio-temporal feature fusion has been proposed. Firstly, the ConvNeXt Block with efficient channel attention has strong feature extraction capability. Spatial features of appliance state changes and micro-variations generated during operation can be extracted from mixed industrial load information by it. Meanwhile, the bidirectional gated recurrent neural network is used to learn the bidirectional dependencies of the load data, obtaining temporal features. Then, the multi-scale feature extraction module is used to extract temporal and spatial features from different depths of the network layers. And the extracted multi-scale temporal and spatial features are fully integrated. Finally, the proposed model is optimized using the Stochastic Weight Averaging method. During the training process, a certain number of model weights are randomly averaged, which can improve the model's generalization ability and identification accuracy. The experiment was conducted on six different industries. The evaluation indexes such as accuracy, F1 score, and Wasserstein distance are also used to verify the effectiveness and superiority of the method.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112445"},"PeriodicalIF":7.2,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep-coupling neural network and genetic algorithm based on Sobol-PR for reactor lightweight optimization","authors":"Qingquan Pan ,&nbsp;Songchuan Zheng ,&nbsp;Xiaojing Liu","doi":"10.1016/j.asoc.2024.112458","DOIUrl":"10.1016/j.asoc.2024.112458","url":null,"abstract":"<div><div>We propose a deep-coupling neural network and genetic algorithm method based on Sobol-PR method for reactor lightweight shielding optimization. The Sobol method is first used to analyze the sensitivities between inputs and outputs of the neural network, and then these sensitivities are used to adjust the fitness function of the genetic algorithm dynamically. Meanwhile, two indicators (precision and recall rate) are introduced to facilitate the sample evaluation and selection, where the precision quantifies the prediction ability of the neural network, and the recall rate quantifies the optimization efficiency of the genetic algorithm. The deep coupling between the neural network and the genetic algorithm based on Sobol-PR method contributes to an integrated framework of “calculation-optimization-reconstruction-evaluation,” which is applied to the lightweight shielding design of a small helium-xenon-cooled reactor. It is found that the performance of the neural network and the genetic algorithm is improved, with the precision of the neural network reaches up to 99 % and the recall rate of the genetic algorithm reaches up to 84 %. Compared with the traditional method, the new method improves the ratio of ideal solutions by up to 3.8 times and the optimization depth by up to 3.2 times.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112458"},"PeriodicalIF":7.2,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective cooperation search algorithm based on decomposition for complex engineering optimization and reservoir operation problems","authors":"Xin-ru Yao ,&nbsp;Zhong-kai Feng ,&nbsp;Li Zhang ,&nbsp;Wen-jing Niu ,&nbsp;Tao Yang ,&nbsp;Yang Xiao ,&nbsp;Hong-wu Tang","doi":"10.1016/j.asoc.2024.112442","DOIUrl":"10.1016/j.asoc.2024.112442","url":null,"abstract":"<div><div>This study introduces a novel multi-objective cooperation search algorithm based on decomposition (MOCSA/D) to address multi-objective competitive challenges in engineering problem. Inspired by the optimization strategy of single-objective Cooperation Search Algorithm (CSA) and the decomposition framework of MOEA/D, MOCSA/D algorithm randomly generates initial solutions in the optimization space, and then repeatedly executes four search strategies until the end of iteration: Cooperative updating strategy gathers high-quality information to update solutions with balanced distribution. Reflective adjustment strategy expands the exploration range of the population, enabling the acquisition of solutions with strong optimization capabilities. Internal competition strategy selects superior individuals with better performance for subsequent optimization. Density updating strategy improves the competitiveness of optimized individuals within the population, fostering a more diverse solution set. Three numerical experiments (including DTLZ, WFG unconstrained test problems, ZXH_CF constrained test problems and RWMOP real-world multi-objective optimization problems) are tested to further comprehensively evaluate the dominant performance of MOCSA/D. The test results in different problem scenarios show that compared with the existing excellent evolutionary algorithms, MOCSA/D can always obtain a better, stable and uniform distribution of non-dominated solutions, and has higher solving efficiency and optimization quality under different performance evaluation metrics with the increasing difficulty of solving problems. Finally, the proposed algorithm is applied to the multi-objective reservoir engineering optimization problem to verify the feasibility of the decision scheme and the comprehensive benefit optimization of MOCSA/D. Overall, MOCSA/D can simplify the problem optimization difficulty based on decomposition mechanism, and improve the global optimization of population, path diversity and individual competition through different search strategies, which provides an advantageous tool for addressing multi-objective competitive challenges.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112442"},"PeriodicalIF":7.2,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-module echo state network with variable skip length for chaotic time series prediction","authors":"Qianwen Liu,&nbsp;Fanjun Li,&nbsp;Shoujing Zheng,&nbsp;Xingshang Li","doi":"10.1016/j.asoc.2024.112441","DOIUrl":"10.1016/j.asoc.2024.112441","url":null,"abstract":"<div><div>Echo state networks (ESNs) have been extensively applied in time series prediction problems. However, the memory-nonlinearity trade-off problem severely limits the ability of ESNs to deal with chaotic time series prediction problems. In this study, a multi-module echo state network with variable skip length (MESN-VSL) is proposed to address this problem. First, the reservoir is divided into a nonlinear mapping module and multiple linear memory modules based on the idea of memory and nonlinearity separation. This idea can effectively balance the memory-nonlinearity trade-off problems. Second, a multi-module mechanism with skip length is put forward to model the characteristics of chaotic time series. The skip length and the number of linear memory modules of the MESN-VSL model are automatically determined based on the idea of phase-space reconstruction. Finally, the experimental results further demonstrate that the MESN-VSL model is superior to some existing models in chaotic time series prediction.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112441"},"PeriodicalIF":7.2,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A switching based forecasting approach for forecasting sales data in supply chains","authors":"Supriyo Ahmed ,&nbsp;Ripon K. Chakrabortty ,&nbsp;Daryl L. Essam ,&nbsp;Weiping Ding","doi":"10.1016/j.asoc.2024.112419","DOIUrl":"10.1016/j.asoc.2024.112419","url":null,"abstract":"<div><div>Forecasting future demand has been a challenging task for supply chain practitioners, which is further exacerbated due to the recent pandemic effects. While the literature suggests a potential for improved accuracy with ML/AI approaches compared to probabilistic distribution-based traditional forecasting methods, the extent of this enhancement may vary based on the specific case. It is recognized that traditional probabilistic forecasting approaches are often considered less accurate and may lead to errors, potentially influencing the estimation of overall business costs. Meanwhile, with the advancement of artificial intelligence (AI) approaches, such as machine learning (ML) and deep learning (DL), this misestimation of cost can be reduced by forecasting demand more accurately from historical data. Consequently, this paper applies several AI-based approaches to predict demand data. Since no fixed AI approach works best for all datasets, a switching-based forecasting approach (SBFA) is proposed to exploit the merit of different advanced ML/DL approaches for different days ahead of prediction. Based on the performance of validation data, the proposed system automatically switches between different approaches to determine a more appropriate forecasting approach. A two-echelon supply chain model with different attributes is developed to validate the proposed SBFA against a few traditional forecasting approaches. The reorder points of this supply chain model are calculated based on the predictions from conventional/ML/DL forecasting approaches. Predictions from SBFA and other approaches are analysed by calculating overall supply chain cost. Based on overall supply chain costs under static and dynamic lead time settings, the effectiveness and applicability of the proposed SBFA against traditional forecasting approaches are demonstrated.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112419"},"PeriodicalIF":7.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cyclic Generative Adversarial Networks with KNN-transformers for missing traffic data completion","authors":"Lie Luo ,&nbsp;Zouyang Fan ,&nbsp;Yumin Chen ,&nbsp;Xin Liu","doi":"10.1016/j.asoc.2024.112406","DOIUrl":"10.1016/j.asoc.2024.112406","url":null,"abstract":"<div><div>In the face of the huge amount of intelligent transportation data, it is necessary and important to collect and statistically process it. Due to adverse weather conditions, sensor malfunctions and other reasons, the collected data inevitably contains missing data. Aiming at the phenomenon of missing traffic data, we propose an interpolation method of missing traffic data based on Cyclic Generative Adversarial Networks with hybrid KNN-Transformer method (KT-CyclicGAN). This method effectively utilizes K-Nearest Neighbor as prior knowledge to guide network training, employs transformers to extract the spatiotemporal relationships present in traffic data, and reconstructs missing data. In GANs, it uses a multi weight sharing cyclic structure to thoroughly learn the spatiotemporal sequences in the traffic data, resulting in accurate imputed data. We assess the performance of the proposed model using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared, and the Concordance Correlation Coefficient (CCC), while simulating three missing data scenarios on the PEMSD4 dataset. The experimental results show that the algorithm proposed in this paper can deal with various missing scenarios and missing rates more effectively than the other five algorithms. Even with a high missing rate of up to 90%, the data imputed by KT-CyclicGAN can still fit the real data quite well.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112406"},"PeriodicalIF":7.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image inpainting for periodic discrete density defects via frequency analysis and an adaptive transformer-GAN network","authors":"Hui Ding ,&nbsp;Yuhan Huang ,&nbsp;Nianzhe Chen ,&nbsp;Jiacheng Lu ,&nbsp;Shaochun Li","doi":"10.1016/j.asoc.2024.112410","DOIUrl":"10.1016/j.asoc.2024.112410","url":null,"abstract":"<div><div>Image inpainting based on deep learning has made significant progress in addressing regular and coherent irregular defects. However, little has been studied on periodic discrete density (PDD) defects that are prevalent in microscopic images obtained by advanced instruments like transmission electron microscopes (TEM) and scanning tunneling microscopes (STM). The PDD defects usually introduce low-frequency noise in the fast Fourier transform (FFT) images, preventing the extraction of useful information particularly in the low-frequency regions. Despite its significant impact, no method has been reported to date to efficiently remove the PDD-induced noise from the FFT of high-resolution microscopic images. In this study, we introduced a novel GAN-based two-stage network (FGTNet), a novel coarse-to-fine inpainting framework, which is built upon the architecture of Generative Adversarial Networks (GAN) and transformer blocks. By integrating the information from both frequency and spatial domains, contextual structures are preserved and high-frequency details are generated in our method. We also proposed an adaptive-window transformer block (A-LeWin) to enhance the spatial feature representation and to fully use the information around the defects. To validate our approach, we constructed a specialized microscopic image dataset with 2730 training samples and 105 testing samples. For comparison, we also extended the experiments to the public Describable Texture Dataset (DTD) and coherence defects that are often discussed in the field of image inpainting. The experiment results indicate that our method performs well on six pixel-level and perceptual-level metrics, and shows the best performance and visual effect of coherent texture.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112410"},"PeriodicalIF":7.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fairness in online vehicle-cargo matching: An intuitionistic fuzzy set theory and tripartite evolutionary game approach","authors":"Binzhou Yang ,&nbsp;Ke Han ,&nbsp;Wenrui Tu ,&nbsp;Qian Ge","doi":"10.1016/j.asoc.2024.112418","DOIUrl":"10.1016/j.asoc.2024.112418","url":null,"abstract":"<div><div>This paper explores the concept of fairness and equitable matching in an on-line vehicle-cargo matching setting, addressing the varying degrees of satisfaction experienced by shippers and carriers. Relevant indicators for shippers and carriers in the on-line matching process are categorized as attributes, expectations, and reliability, which are subsequent quantified to form satisfaction indicators. Employing the intuitionistic fuzzy set theory, we devise a transformed vehicle-cargo matching optimization model by combining the fuzzy set’s membership, non-membership, and uncertainty information. Through an adaptive interactive algorithm, the matching scheme with fairness concerns is solved using CPLEX. The effectiveness of the proposed matching mechanism in securing high levels of satisfaction is established by comparison with three benchmark methods. To further investigate the impact of considering fairness in vehicle-cargo matching, a shipper-carrier-platform tripartite evolutionary game framework is developed under the waiting response time cost (WRTC) sharing mechanism. Simulation results show that with fairness concerns in vehicle-cargo matching, all stakeholders are better off: The platform achieves positive revenue growth, and shippers and carriers receive positive subsidy. This study offers both theoretical insights and practical guidance for the long-term and stable operation of the on-line freight stowage industry.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112418"},"PeriodicalIF":7.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}