Complex & Intelligent Systems最新文献_第6页

Assessment of air purifiers for improving the air quality index using circular intuitionistic fuzzy Heronian means 用循环直觉模糊赫氏法评价空气净化器改善空气质量指标

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-22 DOI: 10.1007/s40747-025-01813-z

Fengyu Guo, Raiha Imran, Shi Yin, Kifayat Ullah, Maria Akram, Dragan Pamucar, Mustafa Elashiry

{"title":"Assessment of air purifiers for improving the air quality index using circular intuitionistic fuzzy Heronian means","authors":"Fengyu Guo, Raiha Imran, Shi Yin, Kifayat Ullah, Maria Akram, Dragan Pamucar, Mustafa Elashiry","doi":"10.1007/s40747-025-01813-z","DOIUrl":"https://doi.org/10.1007/s40747-025-01813-z","url":null,"abstract":"The impact of airborne pollutants present in the environment, entering the body through breathing, can cause significant risks of respiratory and heart-related health problems for individuals. For this, different air purifiers are commonly used to eliminate delicate particulate matter PM2.5, and various studies have examined their effectiveness. This paper aims to analyze airborne pollutants and, by considering them, assess the performance of air purifiers in reducing the concentration of air pollutants in the environment. The aggregation operator (AO) plays a significant role in aggregating the multiple criteria and gives us a result in a singleton set, which assists us in decision-making (DM). So, considering this, the Heronian mean (HM) operator and its special cases such as averaging and geometric operators have been used in this paper. Moreover, circular intuitionistic fuzzy (C-IF) theory is more efficient and comprehensive than the intuitionistic fuzzy set (IFS), as the standard IFS cannot cope with the problems within a circular environment. So, the HM operator under the C-IF environment, such as circular intuitionistic fuzzy Heronian mean (C-IFHM) and it’s averaging and geometric operator, has been presented. Further, some prevalent and crucial properties and theorems have been defined. Then, an algorithm was developed to solve a multicriteria decision-making (MCDM) problem by using the proposed operators within the circular environment. To validate the effectiveness and priority, this paper presents a numerical example of MCDM, and a comparison analysis was conducted to verify the practicality of the proposed approach.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"25 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 segmented differential evolution with enhanced diversity and semi-adaptive parameter control 一种具有增强多样性和半自适应参数控制的分段差分进化算法

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-19 DOI: 10.1007/s40747-025-01883-z

Huarong Xu, Zhiyu Zhang, Qianwei Deng, Shengke Lin

{"title":"A segmented differential evolution with enhanced diversity and semi-adaptive parameter control","authors":"Huarong Xu, Zhiyu Zhang, Qianwei Deng, Shengke Lin","doi":"10.1007/s40747-025-01883-z","DOIUrl":"https://doi.org/10.1007/s40747-025-01883-z","url":null,"abstract":"Differential evolution (DE) is widely recognized as one of the most potent optimization algorithms, capable of effectively addressing a broad spectrum of optimization challenges. Nevertheless, even the most advanced variants of DE share some common challenges. This paper introduces a novel multi-stage semi-adaptive DE algorithm with enhanced diversity (MSA-DE), offering several key contributions: first, the algorithm is structured into three distinct stages, each employing a unique new mutation strategy and designed a new evolutionary scheme based on this segmentation, to better balance exploration and development at all stages of the process. Secondly, building on the idea of parameter restriction in the evolutionary stage of delineation, a semi-adaptive parameter control method based on the fitness of the irrelevant function is proposed which effectively solves the instability problem of excessive fluctuations in the convergence of adaptive parameters. Thirdly, new diversity maintenance mechanisms are proposed, including population initialization, shrinkage, and updating, which better ameliorated the conflicting issues of search range and search rate that existed at all stages of the DE variant. Finally, comprehensive experiments were conducted on the CEC2013, CEC2014, and CEC2017 benchmark test suites to rigorously assess the accuracy, convergence rate, and overall effectiveness of each module. The results show that MSA-DE exhibits strong competitiveness in single-objective optimisation problems. In addition, the experimental results demonstrate the superiority of the algorithm for real-world engineering problems.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"28 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Resource state adaptive collaboration mechanism based on resource modeling and multi-agent system 基于资源建模和多代理系统的资源状态自适应协作机制

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-19 DOI: 10.1007/s40747-025-01882-0

Zhengzuo Li, Chengxi Piao, Dianhui Chu, Zhiying Tu, Xin Hu, Deqiong Ding

{"title":"Resource state adaptive collaboration mechanism based on resource modeling and multi-agent system","authors":"Zhengzuo Li, Chengxi Piao, Dianhui Chu, Zhiying Tu, Xin Hu, Deqiong Ding","doi":"10.1007/s40747-025-01882-0","DOIUrl":"https://doi.org/10.1007/s40747-025-01882-0","url":null,"abstract":"The management of complex, dynamic, and cross-domain resources in cyber-physical-human systems (CPHS) faces significant challenges under spatiotemporal dynamics, particularly resource state conflicts caused by rapid environmental changes and interdependent resource interactions. To address these challenges, this study proposes an integrated framework combining resource modeling and resource state adaptive collaboration mechanism. First, a resource modeling framework for state coordination (RMFS) is developed to unify the representation of heterogeneous resources, their functionalities, and collaborative relationships through hybrid structural and semantic modeling. A resource state adaptive collaboration mechanism (RSACM) integrates multi-agent systems with knowledge graph to achieve real-time state synchronization. Agents utilize the collaborative relationships in the graph to make adaptive collaborative decisions on resource states, in order to perform state transitions and alleviate resource availability conflicts. Further, a meta-path-based resource inference (MPRI) method enables efficient resource retrieval and applies to simulation experiments by leveraging conceptual-instance meta-paths and large language model (LLM)-augmented substitution strategies to resolve resource unavailability. Experimental validation across emergency healthcare scenario demonstrates the framework’s effectiveness. An extension study was conducted on RMFS and RSACM through two cases from different fields. The proposed approach advances CPHS resource management by addressing heterogeneity, availability, and cooperativity in dynamic environments, offering theoretical and practical insights for complex system collaboration under spatiotemporal constraints.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"91 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Hierarchical reinforcement learning based on macro actions 基于宏观行为的分层强化学习

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-19 DOI: 10.1007/s40747-025-01895-9

Hao Jiang, Gongju Wang, Shengze Li, Jieyuan Zhang, Long Yan, Xinhai Xu

{"title":"Hierarchical reinforcement learning based on macro actions","authors":"Hao Jiang, Gongju Wang, Shengze Li, Jieyuan Zhang, Long Yan, Xinhai Xu","doi":"10.1007/s40747-025-01895-9","DOIUrl":"https://doi.org/10.1007/s40747-025-01895-9","url":null,"abstract":"The large action space is a key challenge in reinforcement learning. Although hierarchical methods have been proven to be effective in addressing this issue, they are not fully explored. This paper combines domain knowledge with hierarchical concepts to propose a novel Hierarchical Reinforcement Learning framework based on macro actions (HRL-MA). This framework includes a macro action mapping model that abstracts sequences of micro actions into macro actions, thereby simplifying the decision-making process. Macro actions are divided into two categories: combat macro actions (CMA) and non-combat macro actions (NO-CMA). NO-CMA are driven by decision tree-based logical rules and provide conditions for the execution of CMA. CMA form the action space of the reinforcement learning algorithm, which dynamically selects actions based on the current state. Comprehensive tests on the StarCraft II maps Simple64 and AbyssalReefLE demonstrate that the HRL-MA framework exhibits superior performance, achieving higher win rates compared to baseline algorithms. Furthermore, in mini-game scenarios, HRL-MA consistently outperforms baseline algorithms in terms of reward scores. The findings highlight the effectiveness of integrating hierarchical structures and macro actions in reinforcement learning to manage complex decision-making tasks in environments with large action spaces.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"17 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Fleet formation identification and analyzing method based on disposition feature for remote sensing 基于遥感布局特征的舰队编队识别与分析方法

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-19 DOI: 10.1007/s40747-025-01863-3

Fangli Mou, Zide Fan, Chuan’ao Jiang, Keqing Zhu, Lei Wang, Xinming Li

{"title":"Fleet formation identification and analyzing method based on disposition feature for remote sensing","authors":"Fangli Mou, Zide Fan, Chuan’ao Jiang, Keqing Zhu, Lei Wang, Xinming Li","doi":"10.1007/s40747-025-01863-3","DOIUrl":"https://doi.org/10.1007/s40747-025-01863-3","url":null,"abstract":"Fleet formation identification in remote sensing is a significant focus in maritime surveillance. However, fleet may occur with different ship dense and noisy data due to the complex background and different satellite resolution, few studies have discussed formation identification considering the limits of sensing and application. This study introduces an effective fleet formation identification and analysis method based on disposition features for remote sensing. We fully consider and analyze detection performance in remote sensing applications, such as false alarms, missed detections, ship position errors and observed ship attitudes. A hierarchical density-based spatial clustering with noise method is introduced to cluster fleet regions. The robust disposition features are designed for various kinds of formations without using training data, enabling discrete remote sensing observations. Our method is robust to ship detection results and has low computational complexity, making it highly suitable for real applications. The advantages of our method were demonstrated through extensive experiments. The experimental results show that the proposed method achieves formation identification with an accuracy over 95% within less than 0.15 s when the ship detection performance changes over a large range, leading to a performance improvement of 5–27% compared with that of other comparative methods.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"10 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Energy-based open set domain adaptation with dynamic weighted synergistic mechanism 基于能量的动态加权协同机制的开集域自适应

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-19 DOI: 10.1007/s40747-025-01857-1

Zihao Fu, Dong Liu, Shengsheng Wang, Hao Chai

{"title":"Energy-based open set domain adaptation with dynamic weighted synergistic mechanism","authors":"Zihao Fu, Dong Liu, Shengsheng Wang, Hao Chai","doi":"10.1007/s40747-025-01857-1","DOIUrl":"https://doi.org/10.1007/s40747-025-01857-1","url":null,"abstract":"Open Set Domain Adaptation (OSDA) aims to minimize domain variation while distinguishing between known and unknown samples. However, existing OSDA methods, which rely on deep neural network classifiers, often lead to overconfident predictions and fail to clearly demarcate known from unknown samples. To address this limitation, we propose the Energy-based Open Set domain adaptation (EOS) method. EOS introduces a novel two-stage approach involving a separation stage followed by an alignment stage. In the separation stage, we employ an energy-based anomaly detection strategy to identify unknown samples, transforming the traditional K-class classification task into a K+1-dimensional classifier by introducing an additional dimension to model the uncertainty of out-of-distribution samples. To further refine separation, we apply a coarse-to-fine method that iteratively improves the separation outcomes, which are integrated as weighted inputs in the alignment process to enhance feature distribution alignment. In the alignment stage, we employ a dynamic weighted synergistic mechanism, where the separation network and alignment network co-evolve through continuous alternating training. This mechanism enables the system to better adapt to invariant features across domains. We evaluate EOS on standard benchmarks, including Office-31, Office-Home, and VisDA-2017, with experimental results demonstrating that EOS consistently outperforms other state-of-the-art methods.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"24 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Jointly adaptive cross-resolution person re-identification on super-resolution 基于超分辨率的联合自适应跨分辨率人物再识别

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-19 DOI: 10.1007/s40747-025-01881-1

Caihong Yuan, Zhijie Guan, Yuanchen Xu, Xiaopan Chen, Xiaoke Zhu, Wenjuan Liang

引用次数: 0

MB-AGCL: multi-behavior adaptive graph contrast learning for recommendation MB-AGCL：推荐的多行为自适应图对比学习

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-16 DOI: 10.1007/s40747-025-01880-2

Xiaowen lv, Yiwei Zhao, Zhihu Zhou, Yifeng Zhang, Yourong Chen

{"title":"MB-AGCL: multi-behavior adaptive graph contrast learning for recommendation","authors":"Xiaowen lv, Yiwei Zhao, Zhihu Zhou, Yifeng Zhang, Yourong Chen","doi":"10.1007/s40747-025-01880-2","DOIUrl":"https://doi.org/10.1007/s40747-025-01880-2","url":null,"abstract":"Graph Convolutional Networks (GCNs) have achieved remarkable success in recommendation systems by leveraging higher-order neighborhoods. In recent years, multi-behavior recommendation has addressed the challenges of data sparsity and cold start problems to some extent. However, the introduction of noise from multi-behavior tasks into the user-item graph exacerbates the impact of noise from a few active users and popularity bias from popular items. To tackle these challenges, graph augmentation has emerged as a promising approach in recommendation systems. However, existing augmentation methods may generate suboptimal graph structures, and maximizing correspondence may capture information unrelated to the recommendation task. To address these issues, we propose a novel approach called the Multi-Behavior Adaptive Graph Contrastive Learning Model (MB-AGCL) for recommendation. Our approach integrates auxiliary behaviors to compensate for data sparsity and utilizes adaptive learning to determine whether to drop edges or nodes, thus obtaining an optimized graph structure that reduces the impact of noise. We then train the original and generated graphs using supervised tasks. Furthermore, we propose an efficient adaptive graph augmentation method that integrates graph augmentation with down-stream tasks to reduce the impact of popularity bias. Finally, we jointly optimize these two tasks. Through extensive experiments on public datasets, we validate the effectiveness of our recommendation model.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"218 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Optimizing AlexNet for accurate tree species classification via multi-branch architecture and mixed-domain attention 通过多分支架构和混合域关注优化AlexNet的准确树种分类

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-16 DOI: 10.1007/s40747-025-01836-6

Jianjianxian Liu, Tao Xing, Xiangyu Wang

{"title":"Optimizing AlexNet for accurate tree species classification via multi-branch architecture and mixed-domain attention","authors":"Jianjianxian Liu, Tao Xing, Xiangyu Wang","doi":"10.1007/s40747-025-01836-6","DOIUrl":"https://doi.org/10.1007/s40747-025-01836-6","url":null,"abstract":"Accurate identification of tree species is essential for effective forestry management and conservation. Simple deep-learning models, such as AlexNet and VGG16, often struggle with fine-grained texture extraction and feature distinction, especially in complex environments. While more advanced models, such as ResNet34 and deeper architectures, offer superior feature extraction capabilities, they come with the trade-off of significantly longer training times and higher computational costs. To address these challenges in tree species classification, an optimized AlexNet architecture, MMCAlexNet, is proposed to address these challenges for tree species classification. The model integrates a multi-branch convolutional module, a mixeddomain attention module, and a joint loss function to improve feature extraction and class separation. The multi-branch convolutional module extracts diverse features by processing input with branches of different kernel sizes, capturing both fine and global details. The mixeddomain attention module enhances feature representation by focusing on critical spatial and channel-wise features. The joint loss function ensures better class separation and prevents overfitting by balancing classification accuracy and feature consistency. Experimental results demonstrate that MMCAlexNet outperforms the baseline AlexNet by 7.61%–9.69% in classification accuracy. Although the introduction of optimized structures increases computational complexity, it reduces the model size by 12MB. Furthermore, MMCAlexNet decreases the model size by 306.24MB and improves accuracy by 3.94%–6.35% compared to VGG16, demonstrating a balance between improved accuracy and computational efficiency.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"37 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Formal concept analysis assisted large-scale global optimization and its application to cloud task scheduling 形式化概念分析有助于大规模全局优化及其在云任务调度中的应用

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-16 DOI: 10.1007/s40747-025-01878-w

Guo Yu, Yibo Yong, Chao Jiang, Fei Hao, Lianbo Ma

{"title":"Formal concept analysis assisted large-scale global optimization and its application to cloud task scheduling","authors":"Guo Yu, Yibo Yong, Chao Jiang, Fei Hao, Lianbo Ma","doi":"10.1007/s40747-025-01878-w","DOIUrl":"https://doi.org/10.1007/s40747-025-01878-w","url":null,"abstract":"Effective identification of interdependence information between decision variables is crucial for variable grouping in large-scale global optimization (LSGO). This paper introduces a novel approach called FCA-G (Formal Concept Analysis-Driven Grouping) to solve LSGO problems. FCA, an effective tool for data analysis, is employed in this approach. The primary contribution involves transforming decision variables into the formal context within FCA and utilizing the FCA methodology to solve LSGO problems based on a cooperative coevolution framework. Based on the formal context, a formal concept lattice is constructed, from which equivalent concepts are extracted. All variables within these concepts exhibit explicit interactions. This approach ensures a high degree of correlation among variables within subgroups and a low degree of correlation between subgroups, thereby enhancing cooperative coevolution. Experimental results indicate the significant potential of FCA-G in LSGO, as it outperforms state-of-the-art LSGO algorithms across the majority of LSGO test problems, including those with up to 1000 decision variables and a large-scale cloud task scheduling problem.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"24 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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