Applied Soft Computing最新文献_第5页

Expansion-Trajectory Optimization (ETO): A dual-operator metaheuristic for balanced global and local search 扩展轨迹优化（ETO）：一种平衡全局和局部搜索的双算子元启发式算法

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-07-19 DOI: 10.1016/j.asoc.2025.113642

Erik Cuevas , Oscar A. González-Sánchez , Francisco Orozco- Jiménez , Daniel Zaldívar , Alma Rodríguez-Vazquez , Ram Sarkar

{"title":"Expansion-Trajectory Optimization (ETO): A dual-operator metaheuristic for balanced global and local search","authors":"Erik Cuevas , Oscar A. González-Sánchez , Francisco Orozco- Jiménez , Daniel Zaldívar , Alma Rodríguez-Vazquez , Ram Sarkar","doi":"10.1016/j.asoc.2025.113642","DOIUrl":"10.1016/j.asoc.2025.113642","url":null,"abstract":"<div><div>Premature convergence remains a critical limitation in many metaheuristic algorithms, and is often caused by a rapid loss of population diversity as individuals become overly similar early in the search process. To address this challenge, this paper proposes a new metaphor-free algorithm called Expansion-Trajectory Optimization (ETO), which introduces a dual-operator framework designed to maintain diversity and enhance search performance. The ETO algorithm combines two complementary mechanisms: the expansion operator, which leverages collective information from multiple individuals to identify and explore promising regions in the search space; and the trajectory operator, which conducts a guided search following a Fibonacci spiral. This spiral-based path enables a smooth transition from broad exploration to focused exploitation, thereby ensuring a balanced and adaptive search process. The proposed approach was rigorously evaluated against several state-of-the-art metaheuristic algorithms, using a diverse set of benchmark functions. The experimental results confirm that ETO achieves superior performance in terms of both accuracy and robustness, demonstrating its effectiveness in overcoming early convergence and enhancing optimization outcomes.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"183 ","pages":"Article 113642"},"PeriodicalIF":7.2,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703045","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}

引用次数: 0

Kidney image segmentation from CT for disease diagnosis based on deep extreme cut and NASNet-Bi-LSTM model using generative AI for improved resolution 基于深度极值切割和基于生成式AI的NASNet-Bi-LSTM模型的CT肾脏图像分割用于疾病诊断

IF 6.6 1区计算机科学

Applied Soft Computing Pub Date : 2025-07-19 DOI: 10.1016/j.asoc.2025.113641

C. Girija , P. Ganesh Kumar

{"title":"Kidney image segmentation from CT for disease diagnosis based on deep extreme cut and NASNet-Bi-LSTM model using generative AI for improved resolution","authors":"C. Girija , P. Ganesh Kumar","doi":"10.1016/j.asoc.2025.113641","DOIUrl":"10.1016/j.asoc.2025.113641","url":null,"abstract":"<div><div>Kidney disease technically referred to as nephropathy, which is a broad term used to describe a variety of disorders that affect the structure and function of the kidneys. Even a slight deviation in kidney function and structure measurements are linked to a higher chance of death more frequent than kidney failure. The patient's kidney condition doesn't appear severe in its initial stages, but recovery becomes difficult as the illness advances. To preserve the patient's life, doctors must be able to diagnose the illness early. Several machine learning algorithms are some of the commonly used automated models to predict for diagnosing various diseases. But achieving accurate illness prediction with a low error probability is difficult due to inadequate data training, poor image quality, and incorrect segmentation. So, a hybrid deep learning system is created to detect kidney illness based on CT scans in order to allay these worries. The input images of the kidney stone, cysts, normal and tumor are collected and pre-processed using a modified Gen AI enabled super resolution conversion algorithm to replace the distorted pixels in the input image. Then for enhancing the contrast level of the super resolution image, Dandelion based CLAHE algorithm is developed. At last, hybrid NASNet-BiLSTM is utilized for detecting the kidney disease whether it is normal, stone, cysts and tumor. The suggested method provides 94 % precision, 93 % specificity, and 96 % accuracy. Consequently, by employing this automated approach for detecting the kidney disease diagnosis can be facilitated and treatment can be started early to reduce the death rate.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"183 ","pages":"Article 113641"},"PeriodicalIF":6.6,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144723205","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}

引用次数: 0

3D CAD model dynamic clustering based on inertial feature encoder 基于惯性特征编码器的三维CAD模型动态聚类

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-07-18 DOI: 10.1016/j.asoc.2025.113627

Fangwei Ning , Zirui Li , Jiaxing Lu , Yixuan Wang , Yanxia Niu , Yan Shi

{"title":"3D CAD model dynamic clustering based on inertial feature encoder","authors":"Fangwei Ning , Zirui Li , Jiaxing Lu , Yixuan Wang , Yanxia Niu , Yan Shi","doi":"10.1016/j.asoc.2025.113627","DOIUrl":"10.1016/j.asoc.2025.113627","url":null,"abstract":"<div><div>The number of three-dimensional (3D) computer-aided design (CAD) models of mechanical parts in cyber manufacturing has experienced explosive growth. Classified CAD model shape knowledge based on induction is conducive to model retrieval, design reuse, and machining reuse. However, 3D CAD feature extraction primarily utilizes projected views, point clouds, voxels, and meshes for dimensionality reduction. Nonetheless, complex processes and high computational costs impede effective shape analysis. Traditional distance measures in data spaces or shallow linear embedded spaces are susceptible to errors when assessing similarity in data clusters. Furthermore, as the size of the database increases, data distribution may change in dynamic clustering, leading to data drift. This paper proposes an automatic unsupervised learning shape classification method based on deep embedding for 3D mechanical part CAD models. First, an inertial feature descriptor that effectively represents shape characteristics was established to extract the multidimensional moment of inertia of the 3D CAD model. Second, the inertial feature data space was nonlinearly mapped to a low-dimensional feature space, and the clustering accuracy was improved through the joint training of the encoder and clustering layers. Simultaneously, we revealed the influence of <em>eps</em> and <em>min samples</em> of Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm on the clustering distribution of the CAD models. Third, adding new data can effectively achieve dynamic clustering based on the original clustering results. This paper explains the potential problems of fuzzy clustering boundaries that may arise from adding new data. Experimental data showed that the silhouette coefficient calculated by the proposed method is 0.78, and the normalized mutual information is 0.82, which has an excellent automatic classification effect.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"182 ","pages":"Article 113627"},"PeriodicalIF":7.2,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686230","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}

引用次数: 0

Hypergraph representation learning based on fuzzy attention network 基于模糊注意网络的超图表示学习

IF 6.6 1区计算机科学

Applied Soft Computing Pub Date : 2025-07-18 DOI: 10.1016/j.asoc.2025.113602

Liyan Zhang , Yifan Li , Xinghui Hao , Qinyu Zhang , Aimin Yang , Jingfeng Guo , Lei Zhang

{"title":"Hypergraph representation learning based on fuzzy attention network","authors":"Liyan Zhang , Yifan Li , Xinghui Hao , Qinyu Zhang , Aimin Yang , Jingfeng Guo , Lei Zhang","doi":"10.1016/j.asoc.2025.113602","DOIUrl":"10.1016/j.asoc.2025.113602","url":null,"abstract":"<div><div>Since different nodes in hypergraph have different contributions to hyperedge learning, it is a hot topic in the field of graph representation learning to introduce attention mechanism into hypergraph neural network to represent the importance of different nodes or hyperedges. However, the existing hypergraph attention network methods are mostly based on the assumption that node attributes and topology are perfectly given. In practice, node links and attributes are usually fuzzy concepts, which will lead to the uncertainty of feature learning and then affect the effectiveness of node representation. Based on this, inspired by the attention mechanism and fuzzy logic, this paper proposes a new fuzzy attention hypergraph neural network (HFATN), which is used to quantify the contribution ability of different vertices and hyperedges to better learn the vector representation of nodes in the hypergraph. HFATN consists of two modules: fuzzy attention vertex convolution and fuzzy attention hyperedge convolution. In the process of node convolution and hyperedge convolution, the attention mechanism based on hyperedge and node uncertainty characteristics is introduced respectively. By fuzzing the node set and hyperedge set on the hypergraph, the membership degree of nodes and hyperedges is calculated, which is used to extract effective features to deal with the uncertainty in the hypergraph data. Finally, we conduct experiments on three benchmark datasets for hypergraph node classification. The results show that compared with the latest TDHGNN model, the classification accuracy of FHATN on the three datasets is improved by 2.28 %, 8.99 % and 1.85 % respectively.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"183 ","pages":"Article 113602"},"PeriodicalIF":6.6,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144723362","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}

引用次数: 0

UL-Phys:Ultra-lightweight remote physiological measurement in facial videos based on unsupervised learning UL-Phys：基于无监督学习的面部视频中超轻量远程生理测量

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-07-17 DOI: 10.1016/j.asoc.2025.113593

Haibo Zhang , Xu Wang , Pan Dang , Chaohui Ma , Shuai Liu , Zhuang Xiong , Cheng Liu

{"title":"UL-Phys:Ultra-lightweight remote physiological measurement in facial videos based on unsupervised learning","authors":"Haibo Zhang , Xu Wang , Pan Dang , Chaohui Ma , Shuai Liu , Zhuang Xiong , Cheng Liu","doi":"10.1016/j.asoc.2025.113593","DOIUrl":"10.1016/j.asoc.2025.113593","url":null,"abstract":"<div><div>Remote photoplethysmography (rPPG) enables non-contact monitoring of vital signs using facial videos, but current supervised learning methods often rely on complex architectures and large annotated datasets, limiting their practicality in real-time and resource-constrained scenarios. This paper addresses these limitations by proposing UL-Phys, an ultra-lightweight self-supervised framework for rPPG signal estimation. From a research standpoint, we reformulate the rPPG task as a linear self-supervised reconstruction problem, introducing a novel frequency-constrained objective to extract inherent periodic information without requiring ground truth labels. The framework integrates a lightweight 3D spatiotemporal encoder-decoder network, and a neuroscience-inspired hybrid attention module to enhance pulsatile signal regions while suppressing noise. Experimental evaluations on PURE and UBFC-rPPG datasets demonstrate that UL-Phys achieves superior performance compared to existing supervised and self-supervised baselines, while significantly reducing model complexity and inference latency. Our method also shows strong generalization across datasets, highlighting the value of embedding physiological priors into lightweight, self-supervised architectures. These findings offer a promising direction for scalable and deployable rPPG systems in real-world settings.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"182 ","pages":"Article 113593"},"PeriodicalIF":7.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665518","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}

引用次数: 0

Efficient perturbation-aware distinguishing score for zero-shot neural architecture search 零点神经结构搜索的有效摄动感知区分分数

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-07-17 DOI: 10.1016/j.asoc.2025.113447

Junhao Huang , Bing Xue , Yanan Sun , Mengjie Zhang , Gary G. Yen

{"title":"Efficient perturbation-aware distinguishing score for zero-shot neural architecture search","authors":"Junhao Huang , Bing Xue , Yanan Sun , Mengjie Zhang , Gary G. Yen","doi":"10.1016/j.asoc.2025.113447","DOIUrl":"10.1016/j.asoc.2025.113447","url":null,"abstract":"<div><div>Zero-cost proxies are under the spotlight of neural architecture search (NAS) lately, thanks to their low computational cost in predicting architecture performance in a training-free manner. The NASWOT score is one of the representative proxies that measures the architecture’s ability to distinguish inputs at the activation layers. However, obtaining such a score still requires considerable calculations on a large kernel matrix about input similarity. Moreover, the NASWOT score is relatively coarse-grained and provides a rough estimation of the architecture’s ability to distinguish general inputs. In this paper, to further reduce the computational complexity, we first propose a simplified NASWOT scoring term by relaxing its original matrix-based calculation into a vector-based one. More importantly, we develop a fine-grained perturbation-aware term to measure how well the architecture can distinguish between inputs and their perturbed counterparts. We propose a layer-wise score multiplication approach to combine these two scoring terms, deriving a new proxy, named efficient perturbation-aware distinguishing score (ePADS). Experiments on various NAS spaces and datasets show that ePADS consistently outperforms other zero-cost proxies in terms of both predictive reliability and efficiency. Particularly, ePADS achieves the highest ranking correlation among the advanced competitors (e.g., Kendall’s coefficient of 0.620 on NAS-Bench-201 with ImageNet-16-120 and 0.485 on NDS-ENAS), and ePADS-based random architecture search spends only 0.018 GPU days on DARTS-CIFAR to find networks with an average error rate of 2.64%.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"182 ","pages":"Article 113447"},"PeriodicalIF":7.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663627","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}

引用次数: 0

Cooperative co-evolutionary search for meta multigraph and graph neural architecture on heterogeneous information networks 异构信息网络上元多图和图神经结构的协同进化搜索

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-07-17 DOI: 10.1016/j.asoc.2025.113541

Yang Liu , Xiangyi Teng , Jing Liu

{"title":"Cooperative co-evolutionary search for meta multigraph and graph neural architecture on heterogeneous information networks","authors":"Yang Liu , Xiangyi Teng , Jing Liu","doi":"10.1016/j.asoc.2025.113541","DOIUrl":"10.1016/j.asoc.2025.113541","url":null,"abstract":"<div><div>To model the rich semantic information on heterogeneous information networks (HINs), heterogeneous graph neural architecture search (HGNAS) has become a research hotspot, as it offers a promising automatic search technique for heterogeneous graph neural networks (HGNNs). However, there is no method that can simultaneously solve the meta multigraph and neural architecture search, which are the two core problems of HGNAS. In addition, existing HGNAS methods can only search for the meta graph or determine the number of edge types by setting a threshold hyperparameter, which has limited expression or is difficult to determine and significantly affects performance. In this paper, a cooperative co-evolutionary meta multigraph and graph neural architecture search method (called CCMG) on HINs is proposed. Specifically, CCMG first represents the meta multigraph and neural architecture by discrete encodings, and the number of network layers is variable. Second, whether an encoding of the architecture is meaningful or not is affected by the value of the encoding taken at the corresponding meta multigraph position and their search space sizes are not imbalanced. To cope with these situations, they are cooperatively and collaboratively optimized in the form of subproblems, facilitating group collaboration and information sharing. Finally, the effectiveness and superiority of the CCMG are verified on six datasets for node classification and recommendation tasks. Over the comparison HGNAS method, CCMG improves its performance on the two tasks by an average of 2.29% and 1.21%, respectively.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"182 ","pages":"Article 113541"},"PeriodicalIF":7.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654214","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}

引用次数: 0

Dual-branch fusion network with mutual learning for 12-lead electrocardiogram signal classification 基于相互学习的双支路融合网络对12导联心电图信号进行分类

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-07-17 DOI: 10.1016/j.asoc.2025.113638

Ke Ma , Tao Zhang , Hengyuan Zhang , Wu Huang

{"title":"Dual-branch fusion network with mutual learning for 12-lead electrocardiogram signal classification","authors":"Ke Ma , Tao Zhang , Hengyuan Zhang , Wu Huang","doi":"10.1016/j.asoc.2025.113638","DOIUrl":"10.1016/j.asoc.2025.113638","url":null,"abstract":"<div><div>At present, the vast majority of methods use 12-lead electrocardiograms as a two-dimensional array as network input, and use deep neural networks to extract inter-lead correlation features. However, extracting intra-lead differential features is particularly important, as not every lead’s feature carries equal significance for classification. In this paper, we propose a dual-branch fusion network with 12-lead separation and combination, integrating the idea of mutual learning. The dual-branch network extracts differentiated and correlated features respectively and fuse them for classification. Each branch network is not only supervised by the ground truth but also referenced the learning experience of another branch network to further improve its classification ability. To address data imbalance, we introduced a category weighted binary focal loss to increase the attention of the network to the samples with few classes. We validated the proposed method on two publicly available multi-label datasets. Compared to the baseline model, our model has significantly improved in performance, demonstrating strong competitiveness and validating the effectiveness of our method. The experimental results show that our proposed method surpasses existing methods and achieves state-of-the-art performance. The method enables lightweight deployment on wearable devices, such as 12-lead ECG garments and smartwatches, facilitating real-time arrhythmia monitoring.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"182 ","pages":"Article 113638"},"PeriodicalIF":7.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685480","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}

引用次数: 0

A multi-bipolar fuzzy graph and its centrality measure based decision making system for healthcare waste treatment method selection 基于多极模糊图及其中心性测度的医疗废物处理方法选择决策系统

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-07-17 DOI: 10.1016/j.asoc.2025.113564

Deva Nithyanandham, Felix Augustin

{"title":"A multi-bipolar fuzzy graph and its centrality measure based decision making system for healthcare waste treatment method selection","authors":"Deva Nithyanandham, Felix Augustin","doi":"10.1016/j.asoc.2025.113564","DOIUrl":"10.1016/j.asoc.2025.113564","url":null,"abstract":"<div><div>Fuzzy graphs help to handle various real-life uncertain problems and fuzzy preference relations have widely been utilized to deal with various decision-making problems. In reality, the multi-dimensional and counter investigations of a problem always provide an efficient outcome. However, these conventional approaches often lack consideration of counter-views and multi-dimensional perspectives in problem-solving. Therefore, the present study defines the notion of multi-bipolar fuzzy preference relation to fuse the multi-dimensional and bipolar views in handling uncertain relations in real-time problems. The notion of multi-bipolar fuzzy preference relation graph is explored to effectively study the pairwise importance in terms of multi-bipolar fuzzy relations between the multi-bipolar fuzzy sets. Additionally, the concepts of degree, in-degree and out-degree centrality measures are explored within the multi-bipolar fuzzy graph context. On the other hand, these concepts are fused to design a multi-criteria decision-making technique, where the preference relation graph helps to analyze the pairwise importance among the criteria and in-degree centrality aids to consider the importance of a criteria from other criteria. Furthermore, this fusion is implemented to address the healthcare waste treatment selection problem, where steam sterilization and chemical disinfection resulted in first and last rank, respectively. For this problem, five alternatives and their ten essential criteria are considered from the view of technical, social, environmental and economic aspects. To demonstrate the superiority and validity of the proposed technique, a comparative study is performed with existing techniques. Finally, the stability of the results is analyzed through a sensitivity performance.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"182 ","pages":"Article 113564"},"PeriodicalIF":7.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680389","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}

引用次数: 0

Dynamic multi-objective optimization for uncertain order insertion green shop production scheduling 不确定订单插入绿色车间生产调度的动态多目标优化

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-07-17 DOI: 10.1016/j.asoc.2025.113573

Linjie Wu , Tianhao Zhao , Xingjuan Cai , Zhihua Cui , Jinjun Chen

{"title":"Dynamic multi-objective optimization for uncertain order insertion green shop production scheduling","authors":"Linjie Wu , Tianhao Zhao , Xingjuan Cai , Zhihua Cui , Jinjun Chen","doi":"10.1016/j.asoc.2025.113573","DOIUrl":"10.1016/j.asoc.2025.113573","url":null,"abstract":"<div><div>Efficient production scheduling of forgings is critical to manufacturing. However, production orders in manufacturing can change dynamically, making it challenging to quickly track changes between orders and ensure productivity and environmentally friendly production in the shop. This paper presents a dynamic multi-objective green shop production scheduling optimization problem that addresses uncertain order insertion, considering the objectives of total completion time, energy consumption, and carbon emission. When a new batch of orders arrives, changes in the dimensionality of decision variables for production scheduling lead to environmental alterations. By integrating the workpiece completion rates of orders from historical environments with the workpiece quantities of orders in the new environment, the scheduling plan is dynamically adjusted and promptly responded to. Therefore, we design a discrete matrix-based dynamic multi-objective optimization algorithm (DM-DMOEA), which can measure the similarity of the orders before and after the dynamic changes, and reconstruct a high-quality scheduling solution under the new environment, which solves the problem of variable dimensionality changes due to the dynamic environment. Finally, experiments were conducted in a real case of a flange manufacturing company, and the results proved the validity of the proposed model and the superior performance of the algorithm.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"182 ","pages":"Article 113573"},"PeriodicalIF":7.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694370","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}

引用次数: 0