Applied Soft Computing最新文献

{"title":"Application of ant colony optimization algorithm based on farthest point optimization and multi-objective strategy in robot path planning","authors":"Shuai Wu ,&nbsp;Ani Dong ,&nbsp;Qingxia Li ,&nbsp;Wenhong Wei ,&nbsp;Yuhui Zhang ,&nbsp;Zijing Ye","doi":"10.1016/j.asoc.2024.112433","DOIUrl":"10.1016/j.asoc.2024.112433","url":null,"abstract":"<div><div>With the continuous development of high technology and the continuous progress of intelligent industry, mobile robots are gradually widely used in various fields. In the field of mobile robot research, path planning is crucial. However, the current ant colony optimization algorithm applied to mobile robot path planning still has some limitations, such as early blind search, slower convergence speed, and lower path smoothness. To overcome these problems, this paper proposes an ant colony optimization algorithm based on farthest point optimization and multi-objective strategy. The algorithm introduces new heuristic information such as the normal distribution model, triangle inequality principle, smoothness function, safety value function, etc. It adopts multi-objective comprehensive evaluation indexes to judge the quality of paths. For the high-quality and poor-quality paths, the algorithm takes additional pheromone increments and decrements in pheromone concentration to speed up the algorithm’s convergence. Besides, the farthest point optimization strategy is used to improve the quality of the paths further. Finally, to verify the algorithm’s effectiveness, the algorithm is compared with 20 existing methods for solving the robot path planning problem, and the experimental results show that the algorithm exhibits better results in terms of convergence, optimal path length, and smoothness. Specifically, the algorithm can produce the shortest path in four different environments while realizing the least number of turns with faster convergence, further proving the effectiveness of the improved algorithm in this paper.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112433"},"PeriodicalIF":7.2,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654122","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":"Segmentation of the customers based on customer value: A three-way decision perspective","authors":"Xiang Li ,&nbsp;Zeshui Xu","doi":"10.1016/j.asoc.2024.112415","DOIUrl":"10.1016/j.asoc.2024.112415","url":null,"abstract":"<div><div>This paper establishes an innovative value evaluation framework based on the criterion-oriented three-way decision (3WD) in the double hierarchy linguistic term (DHLT) environment to help the customer manager finish customer segmentation. Customer relationship management is the key to the success of enterprises in the information economy era. The segmentation of customers based on their relative criteria can identify the customers who are high-value customers for enterprises. However, multi-criteria decision-making can only display the value ranking of customers, rather than the value segmentation of customers. The employment of 3WD solves this problem. Then we classify the customers based on the 3WD method. First, the criteria are evaluated by using DHLTs, while the weights of criteria are acquired according to the maximum deviation method. Second, the conditional probabilities are estimated by the improved TOPSIS method combined with gray relation analysis, while the threshold values are calculated by the relative utilities which are constructed on the basis of the criterion information. Subsequently, the segmentation of customers is obtained according to the maximum-utility principle. Lastly, case research about the segmentation of customers based on value is used to demonstrate the practicality of our method, while some strategies about customer relationship management are given based on customer segmentation for obtaining maximum returns with minimum investment.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112415"},"PeriodicalIF":7.2,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656737","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":"Adaptive decomposition-based evolutionary algorithm for many-objective optimization with two-stage dual-density judgment","authors":"Yongjun Sun,&nbsp;Jiaqi Liu,&nbsp;Zujun Liu","doi":"10.1016/j.asoc.2024.112434","DOIUrl":"10.1016/j.asoc.2024.112434","url":null,"abstract":"<div><div>In order to better balance the convergence and diversity of MOEA/D for many objective optimization problems (MaOPs) with various Pareto fronts (PFs), an adaptive decomposition-based evolutionary algorithm for MaOPs with two-stage dual-density judgment is proposed. To solve the problem that weighted Tchebycheff decomposition may produce weakly Pareto optimal solutions when the solution is not unique or the uniqueness is difficult to guarantee, an augmented weighted Tchebycheff decomposition is adopted. To balance the convergence and diversity of non-dominated solutions in the external archive, different sparsity-level evaluations using vector angles or Euclidean distances are used to measure the distribution of solutions at different stages. To improve the diversity of solution sets obtained by MOEA/D for various PFs, an adaptive weight vector adjustment method based on two-stage dual-density judgment is presented. For weight vector addition, the potential search area is found according to the two-stage density judgment, and then a two-stage sparsity level judgment on the solutions of this area is performed for a second density judgment. For weight vector deletion, the degree of crowding is used to delete the weight vectors with a high crowding degree. Compared with nine advanced multi-objective optimization algorithms on DTLZ and WFG problems, the results demonstrate that the performance of the proposed algorithm is significantly better than other algorithms.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112434"},"PeriodicalIF":7.2,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654123","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":"Tourist trip planning: Algorithmic foundations","authors":"Damianos Gavalas,&nbsp;Grammati Pantziou,&nbsp;Charalampos Konstantopoulos,&nbsp;Pieter Vansteenwegen","doi":"10.1016/j.asoc.2024.112280","DOIUrl":"10.1016/j.asoc.2024.112280","url":null,"abstract":"","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"166 ","pages":"Article 112280"},"PeriodicalIF":7.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707403","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":"An effective surrogate-assisted rank method for evolutionary neural architecture search","authors":"Yu Xue,&nbsp;Anjing Zhu","doi":"10.1016/j.asoc.2024.112392","DOIUrl":"10.1016/j.asoc.2024.112392","url":null,"abstract":"<div><div>Evolutionary neural architecture search (ENAS) is able to automatically design high-performed architectures of deep neural networks (DNNs) for specific tasks. In recent years, surrogate models have gained significant traction because they can estimate the performance of neural architectures, avoiding excessive computational costs for training. However, most existing surrogate models primarily predict the performance of architectures directly or predict pairwise comparison relationships, which makes it challenging to obtain the rank of a group of architectures when training samples are limited. To address this problem, we propose TCMR-ENAS, an effective triple-competition model-assisted rank method for ENAS. TCMR-ENAS employs a novel triple-competition surrogate model combined with a score-based fitness evaluation method to predict group performance rank. Moreover, a progressive online learning method is proposed to enhance the predictive performance of the triple-competition surrogate model in the framework of modified genetic search. To validate the effectiveness of TCMR-ENAS, we conducted a series of experiments on NAS-Bench-101, NAS-Bench-201, NATS-Bench and NAS-Bench-301, respectively. Experimental results show that TCMR-ENAS can achieve better performance with lower computational resources. The accuracies of searched architectures achieve the best results compared with those of the state-of-the-art methods with limited training samples. In addition, the factors that may influence the effectiveness of TCMR-ENAS are explored in the ablation studies.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112392"},"PeriodicalIF":7.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586405","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 shared multi-scale lightweight convolution generative network for few-shot multivariate time series forecasting","authors":"Minglan Zhang ,&nbsp;Linfu Sun ,&nbsp;Jing Yang ,&nbsp;Yisheng Zou","doi":"10.1016/j.asoc.2024.112420","DOIUrl":"10.1016/j.asoc.2024.112420","url":null,"abstract":"<div><div>Time series forecasting is an important time series data mining technique. Among them, multivariate time series (MTS) forecasting has received extensive attention in many fields. However, many existing MTS forecasting models usually rely on a large amount of labeled data for model training, and data collection and labeling are difficult in real systems. The insufficient amount of data makes it difficult for the model to fully learn the intrinsic patterns and features of the data, which not only increases the prediction error, but also makes it hard to obtain satisfactory prediction results. To address this challenge, we propose a shared multi-scale lightweight convolution generative (SMLCG) network for few-shot multivariate time series forecasting by using samples generation strategy. The overall goal is to design a shared multi-scale feature generation prediction framework that generates data highly similar to the original sample and enriches the training sample to improve prediction accuracy. Specifically, the MTS is divided into different scales, and the multi-scale feature fusion module is utilized to capture and fuse the MTS information in different spatial dimensions to eliminate the heterogeneity among the data. Then, the key information in the multi-scale features is captured by a lightweight convolution generative network, and the feature weights are dynamically assigned to explore the change information. In addition, a spatio-temporal memory module is designed based on the parameter sharing strategy to capture the spatio-temporal dynamic relationship of sequences by learning the common knowledge in multi-scale features, thus improving the robustness and generalization ability. Through comprehensive experiments on four publicly available datasets and comparisons with other reported models, it is demonstrated that the SMLCG model can efficiently generate approximate samples in the few-shot case and provide excellent prediction results. The architecture of SMLCG serves as a valuable reference for practical solutions to address the few-shot problem in multivariate time series.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112420"},"PeriodicalIF":7.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656735","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":"An altitude-aware fuzzy approach for energy efficiency in UAV-assisted 3D Wireless Sensor Networks","authors":"Seyyit Alper Sert ,&nbsp;Adnan Yazici","doi":"10.1016/j.asoc.2024.112424","DOIUrl":"10.1016/j.asoc.2024.112424","url":null,"abstract":"<div><div>In Wireless Sensor Networks (WSNs) that use multi-hop topologies, issues like energy holes and hotspots have become prominent. To address these, recent research has proposed using mobile sinks with abundant resources. These include mobile robots, drones, and notably, Unmanned Aerial Vehicles (UAVs), as solutions to alleviate these challenges. This paper introduces a novel altitude-aware fuzzy approach aimed at improving energy efficiency in UAV-supported 3D WSNs. The proposed methodology comprises two key components. Firstly, a tailored fuzzy clustering algorithm is developed to manage the spatial structure of the 3D WSN, optimizing energy consumption. Secondly, a hybrid grey wolf optimization algorithm is utilized to fine-tune the parameters of the fuzzy clustering algorithm, ensuring optimal performance. The synergistic and seamless integration of these components addresses the energy efficiency challenges inherent in UAV-assisted 3D WSNs. The significance of this approach lies in its capacity to navigate the escalating complexity and energy demands of modern sensor networks, offering a harmonious blend of theoretical innovation and practical applicability. Experimental analysis and results substantiate the superior performance of the proposed approach compared to existing solutions, as measured by the metrics commonly employed to evaluate the network lifetime of protocols in the literature.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112424"},"PeriodicalIF":7.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656739","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":"Medical image segmentation network based on feature filtering with low number of parameters","authors":"Zitong Ren ,&nbsp;Zhiqing Guo ,&nbsp;Liejun Wang,&nbsp;Lianghui Xu,&nbsp;Chao Liu","doi":"10.1016/j.asoc.2024.112399","DOIUrl":"10.1016/j.asoc.2024.112399","url":null,"abstract":"<div><div>In recent years, the medical image segmentation method based on hybrid convolutional neural network (CNN) and Vision Transformer (ViT) has made great progress, but it still faces the challenge of unbalanced global and local modeling, and excessive parameters. In addition, ViT repeatedly uses the whole feature map to model the global information, thus generating irrelevant and weakly related information, which will weaken the performance of the model when facing small datasets and segmentation targets. Therefore, this paper proposes a feature screening network based on similarity, named Screening Feature (SF)-MixedNet. Specifically, this paper first proposes a new feature extractor, namely Correlation based Similarity Transformer (CSimFormer). On the basis of parameter pruning, it uses the Screening Feature Multi-head Self Attention (SF-MSA) to establish the remote dependency, and calculates the similarity between local elements through the Location-Sensitive Mechanism (LsM) to obtain the weight matrix. Then, the correlation between regional elements is mined by Region Matching and Selection (RMS) mechanism, and the obtained information is filtered according to the corresponding rules to reduce the side effects of redundant information. Extensive experiments on Synapse dataset, ACDC dataset and SegPC-2021 dataset show that the segmentation accuracy reaches 83.51%, 92.20% and 81.27% respectively. Especially in the Synapse dataset, our method is 6.31% higher than the baseline. The method proposed in this paper effectively improves the segmentation accuracy, provides more detailed information for medical diagnosis and promotes the development of medical artificial intelligence technology.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112399"},"PeriodicalIF":7.2,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578870","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":"Knowledge graph-driven mountain railway alignment optimization integrating karst hazard assessment","authors":"Hao Pu ,&nbsp;Ting Hu ,&nbsp;Taoran Song ,&nbsp;Paul Schonfeld ,&nbsp;Wei Li ,&nbsp;Lihui Peng","doi":"10.1016/j.asoc.2024.112421","DOIUrl":"10.1016/j.asoc.2024.112421","url":null,"abstract":"<div><div>Karst hazard is a considerable threat that should be considered in railway alignment design for mountainous regions with dense water systems. Nevertheless, alignment design principles in karst regions have not been systematically studied. Moreover, a quantitative karst hazard assessment model is currently lacking for automated alignment optimization. To solve the above problems, based on the analyses of karst inducing factors and hazard representation, the railway alignment design principles in karst regions are summarized through an event tree. A highly-coupled knowledge graph (called KaRAD-KG) modeling method is proposed. Then, a bi-objective alignment optimization model considering railway construction cost and karst hazard (mainly including hazard components of synclinal karst, anticlinal karst and karst depression) is constructed. To solve the optimization model, a knowledge-driven distance transform algorithm incorporating a karst hazard assessment method and a multicriteria tournament decision method is customized. Finally, the application in a real-world case indicates that the proposed method can generate an alignment which reduces construction cost by 3.39 % and karst hazard by 18.73 % compared to the best manually-designed alternative, which verifies the effectiveness of this method for assisting actual railway alignment design in a karst-dense mountainous region.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112421"},"PeriodicalIF":7.2,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578866","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":"Combined fuzzy-metaheuristic framework for bridge health monitoring using UAV-enabled rechargeable wireless sensor networks","authors":"Fakhrosadat Fanian ,&nbsp;Marjan Kuchaki Rafsanjani ,&nbsp;Mohammad Shokouhifar","doi":"10.1016/j.asoc.2024.112429","DOIUrl":"10.1016/j.asoc.2024.112429","url":null,"abstract":"<div><div>It is essential to monitor the health of important infrastructure (<em>e.g.</em>, bridges) to maintain their functions. Visual inspections have been conventionally dominant in this regard, although they are susceptible to human errors. Wireless sensor networks (WSNs) provide an automated, convenient, and low-cost option for developing bridge health monitoring (BHM) networks. However, the constant use of WSNs for monitoring the structural and environmental health of bridges can pose a serious challenge due to the limited lifetimes of these networks that depend on the battery lifetimes of sensor nodes. This paper proposes a combined fuzzy-metaheuristic framework to maintain the BHM stability by using rechargeable sensors. This framework benefits from metaheuristic methods and the fuzzy logic to match the sensor network configuration management to the specific conditions of each bridge, recognizing that different bridges share very few common characteristics. Every new bridge is unique; hence, it is difficult to design a BHM paradigm that fits the conditions of all bridges. The proposed framework manages the current network configuration concerning the conditions of each bridge. This framework manages the network topology formation, information relay, and recharge by using a multipurpose objective, tuning control parameters, and controlling network activities in an optimization process. Moreover, unmanned aerial vehicles (UAVs) are employed to recharge sensor nodes under the proposed framework strategies to overcome the energy limitation of sensor nodes. The proposed framework is evaluated on three bridge scenarios: Hardanger Bridge, Bergsøysund Bridge, and New Carquinez Suspension Bridge. Compared to common WSN methods, it demonstrated superior performance under various conditions, including the rate of active and inactive nodes, energy efficiency, survival rate, stability, recharge delay, average node energy, recharge requests, and total packets received. The evaluation results demonstrate that the proposed framework significantly surpasses existing methods in terms of WSN performance metrics. The results show that the proposed framework outperforms existing methods by an average of 32.8 % for the Hardanger Bridge, 53.2 % for the Bergsøysund Bridge, and 31.2 % for the New Carquinez Suspension Bridge.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112429"},"PeriodicalIF":7.2,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703613","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}