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Applied Soft Computing

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インパクトファクター:
7.8
出版社:
Elsevier
ISSN:
1568-4946
閲覧:
48170
フォロー:
42

論文募集

Applied Soft Computing is an academic journal published by Elsevier. (ISSN 1568-4946, impact factor 7.8).

Aims & Scope The Official Journal of the World Federation on Soft Computing (WFSC) http://www.softcomputing.org Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems. Soft computing is a collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The focus is to publish the highest quality research in application, advance and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Swarm Intelligence and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short. Major Topics: The scope of this journal covers the following soft computing and related techniques, interactions between several soft computing techniques, and their industrial applications: Evolutionary Computing Fuzzy Computing Hybrid Methods Immunological Computing Neuro Computing Swarm Intelligence Machine and Deep Learning Rough Sets The application areas of interest include but are not limited to applications of soft computing to: Agricultural Machinery, Smart Farming Autonomous Reasoning Big Data, IoT, Edge Computing Combinatorial Optimization Data Mining Decision Support Engineering Design Optimization Fault Diagnosis Finance Human-Machine Interface Intelligent Agents Manufacturing Systems Power Electronics Multi-objective Optimization Power and Energy Process and System Control Robotics Security Sensor Systems Signal or Image Processing Software Engineering Supply Chain Economy System Identification and Modelling Telecommunications Time Series Prediction Extended Reality, Metaverse, Digital Twins Vision or Pattern Recognition Authors are welcome to submit letters promoting original soft computing research to Applied Soft Computing's open access companion title, Systems and Soft Computing.
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特集号

特集号：Soft Computing for Agentic AI: Advances and Applications 投稿締切日: 2026-11-15 AI is increasingly evolving toward systems that can think and act autonomously, referred to as agentic AI. These systems can make decisions, adapt to changing conditions, and solve complex problems without constant human supervision. Examples include autonomous robots, intelligent IoT networks, and smart factories, all of which require advanced computational techniques to operate effectively. Soft computing methods, including fuzzy logic, neural networks, and swarm intelligence, are well-suited for agentic AI systems. They handle uncertainty, incomplete information, and highly dynamic environments, enabling AI agents to exhibit flexible, robust, and reliable behavior. Despite significant progress in agentic AI, there remains a growing need to investigate how soft computing can further enhance autonomous decision-making, adaptive learning, and multi-agent coordination. This Special Issue aims to collect innovative research that leverages soft computing to advance agentic AI, bridging the gap between theoretical models and real-world applications. By combining soft computing techniques with agentic AI, researchers can develop systems that optimize performance under practical constraints, adapt to unforeseen scenarios, and coordinate efficiently in complex environments. Contributions may explore new algorithms, architectures, and tools that enhance agentic behavior in robotics, cyber-physical systems, IoT, edge computing, and other autonomous domains. Furthermore, this Special Issue encourages interdisciplinary research that integrates insights from AI, control systems, networking, and intelligent automation. The goal is to highlight approaches that enable AI agents to be intelligent, autonomous, reliable, trustworthy, and applicable to real-world challenges. This Special Issue aims to bring together studies that develop new methods, tools, and applications for agentic AI using soft computing, helping researchers turn advanced ideas into real-world solutions. Guest editors: Dr. Sahil Garg Canadian University, Dubai Prof. Bong Jun Choi Soongsil University, South Korea Prof. Biplab Sikdar National University of Singapore, Singapore Special issue information: The Special Issue welcomes original research, review articles, and application-oriented papers in the following areas (but not limited to): -Soft computing techniques for agentic AI systems -Hybrid intelligent systems for autonomous decision-making -Fuzzy logic-based reasoning in agentic environments -Neural networks and deep learning models for adaptive agents -Evolutionary and swarm intelligence approaches to multi-agent coordination -Self-learning and self-adaptive systems under uncertainty -Intelligent agent architectures for IoT, edge computing, and cyber-physical systems -Agentic AI for autonomous robotics and smart manufacturing -Optimization and control in agentic systems using soft computing -Trust, reliability, and ethical considerations in autonomous agent design -Applications in digital twins, extended reality, and metaverse environments -Case studies demonstrating soft computing for practical agentic AI deployment Manuscript submission information: Submission Open Date: April 15, 2026 Submission Deadline: November 15, 2026 Paper submissions for the special issue should follow the submission format and guidelines for regular papers and be submitted at Submit your manuscript | Applied Soft Computing Journal. Each submission must contribute to soft computing related methodology. Authors should select “VSI:ASOC_Agentic AI” when they reach the “Article Type” step in the submission process. The submitted papers must propose original research that has not been published nor is currently under review in other venues. Papers that either lack originality or clarity in presentation or fall outside the scope of the special issue will be desk-rejected and will not be sent for review. Authors should explain in the Cover Letter how the submission aligns with the goals and scope of the Special Issue. Authors should declare the use of generative AI and AI tools in the manuscript preparation process, and include a detailed declaration in the manuscript (e.g., which tool has been used for what purpose or which part). Please refer to the Declaration of generative AI use in Guide for authors. Keywords: Agentic AI; Soft Computing; Autonomous Systems; Adaptive Agents; Multi-Agent Coordination; Real-World AI Applications https://www.sciencedirect.com/special-issue/332257/soft-computing-for-agentic-ai-advances-and-applications
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特集号：Fault Diagnosis and Health Management Soft Computing Methodologies for Industrial 5.0 Systems 投稿締切日: 2026-11-25 As the global industry accelerates towards the 5.0 era, new industrial systems are developing and evolving at an unprecedented speed. These systems are characterized by high automation, integration, and intelligence, and deeply integrate emerging technologies such as information technology, advanced manufacturing technology, and artificial intelligence. They not only have stronger production capacity and higher efficiency, but also bring disruptive changes to traditional industrial models. However, the high complexity of new industrial systems also brings new challenges, especially in ensuring stable operation and efficient maintenance of the system. Specifically, these systems often involve the collection, storage, processing, and analysis of massive heterogeneous data, with more complex and diverse failure modes. Traditional fault diagnosis and maintenance methods based on manual experience are no longer sufficient to meet the needs. At the same time, equipment failure may lead to serious consequences such as production interruption, quality decline, safety accidents, etc., which have a huge impact on the economic benefits and social impact of the enterprise. Therefore, developing advanced fault diagnosis and intelligent operation and maintenance technologies is crucial for improving the reliability, safety, and economy of new industrial systems. The purpose of this solicitation is to gather the latest research results and innovative applications in the fields of artificial intelligence and machine learning at home and abroad, focusing on fault diagnosis and health management of industrial 5.0 systems. We sincerely invite experts, scholars, engineers, and researchers to actively submit their papers and work together to promote the development of this field. Guest editors: Assoc. Prof. Haibin Ouyang Guangzhou University, China Email: [email protected] Prof. Seyedali Mirjalili Torrens University, Australia Email: [email protected] Prof. Jiewu Leng Guangdong University of Technology, China Email: [email protected] Special issue information: Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems. Soft computing is a collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The focus is to publish the highest quality research in application, advance and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Swarm Intelligence and other similar techniques to address real world complexities. According to the requirements of the Applied Soft Computing journal, we welcome but are not limited to submissions on the following topics: 1. Fault feature extraction and classification method based on deep learning 2. Fault reasoning and diagnosis method based on knowledge graph 3. Application of Small Sample Learning and Federated Learning in Fault Diagnosis 4. Fault diagnosis method based on multi-source heterogeneous data fusion 5. Prognostics and Health Management (PHM) technology based on machine learning for predicting the remaining lifespan of devices 6. Remote Monitoring and Diagnostic System Based on Internet of Things (IoT) 7. Fault diagnosis and operation and maintenance technology for intelligent manufacturing equipment 8. Intelligent monitoring and fault prediction methods for power systems, rail transit system, chemical processes, energy equipment 9. Optimization Algorithm Design and Application for Industrial fault diagnosis 10. Standardization research on fault diagnosis and intelligent operation and maintenance 11. Fault diagnosis and prediction based on edge intelligence 12. Application of Cross disciplinary Knowledge Transfer in Fault Diagnosis Manuscript submission information: Important Dates: Submission Open Date: Mar 25, 2026 Submission Deadline: Nov 25, 2026 Paper submissions for the special issue should follow the submission format and guidelines for regular papers and be submitted at Submit your manuscript | Applied Soft Computing Journal. All the papers will be peer-reviewed following Applied Soft Computing reviewing procedures. Guest editors will make an initial assessment of the suitability and scope of all submissions. Papers will be evaluated based on their originality, presentation, relevance, and contributions, as well as their suitability to the special issue. Each submission must contribute to soft computing related methodology. Papers that either lack originality or clarity in presentation or fall outside the scope of the special issue will be desk-rejected and will not be sent for review. Authors should select “VSI:ASOC_SC for Machine Health” when they reach the “Article Type” step in the submission process. The submitted papers must propose original research that has not been published nor is currently under review in other venues. Keywords: FAULT DIAGNOSIS; Health Management; Soft Computing https://www.sciencedirect.com/special-issue/330127/special-issue-on-fault-diagnosis-and-health-management-soft-computing-methodologies-for-industrial-50-systems
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特集号：Incremental Learning: Algorithms & Applications 投稿締切日: 2026-12-31 Incremental Learning (IL), also known as continual or lifelong learning, represents a fundamental paradigm in machine learning where models learn continuously from a stream of data, accumulating knowledge over time without catastrophically forgetting previously acquired information. This capability is crucial for real-world intelligent systems that operate in non-stationary environments, such as autonomous agents, personalized recommender systems, and IoT devices, where data arrives sequentially and the underlying data distribution may evolve. IL has also been identified as one of the main areas where soft computing techniques, such as neuro computing and evolutionary algorithms, may be an enabling factor. Despite the remarkable success of deep learning and other advanced algorithms in static batch learning settings, their direct application to dynamic, ever-changing data streams remains a\ significant challenge. The primary obstacles include catastrophic forgetting, concept drift, and the stability-plasticity dilemma. Overcoming these challenges is key to building adaptive, efficient, and sustainable AI systems that can learn throughout their operational lifetime without the need for frequent and costly retraining from scratch. This field has recently gained significant attention as systems move from isolated laboratory environment to open-ended worlds. Soft Computing is exceptionally well-positioned to provide powerful solutions to these challenges. Unlike traditional "hard" computing which seeks precise, deterministic answers, soft computing techniques are tolerant of imprecision, uncertainty, and partial truth, making them inherently suited for dynamic and evolving problem domains. This special issue seeks to gather cutting-edge research that advances the theory, methodologies, and applications of Incremental Learning, with particular emphasis on approaches based on Soft Computing. Guest editors: Assoc. Prof. Catarina Moreira University of Technology Sydney, Australia [email protected] Dr. Sahraoui Dhelim Dublin City University, Ireland [email protected] Prof. Xin Ning Institute of Semiconductors Chinese Academy of Sciences, China [email protected] Assoc. Prof. Wenbin Zhang Florida International University, United States [email protected] Special issue information: We invite high-quality submissions on topics including, but not limited to: 1. Theoretical Foundations of Incremental Learning: Novel neural network architectures for incremental learning (e.g., dynamic networks, progressive networks). Regularization-based methods for mitigating catastrophic forgetting. Memory replay-based and experience rehearsal strategies. Meta-learning and optimization techniques for continual learning.Architectures supporting both task-incremental and class-incremental learning. 2. Algorithms and Techniques: Few-Shot and Zero-Shot Learning methods and models. Incremental and Continual Learning Algorithms for handling data streams and non-stationary environments. Cross-Domain and Cross-Modal Learning Systems that enable effective knowledge transfer. Scalable Architectures for incremental learning and knowledge accumulation from large-scale data. Transfer Learning and mechanisms for knowledge sharing across diverse tasks and domains. System design for ensuring robustness of incremental learning in adverse or complex conditions. 3. Applications and Systems: Incremental learning for robotics and autonomous systems. Incremental learning in healthcare, biomedical data analysis, and monitoring systems. Incremental learning for 3D perception and world modeling Deployment of IL systems on edge devices and embedded platforms. Case studies demonstrating the application of incremental learning in critical domains such as finance, industrial control, and smart transportation. 4. Evaluation, Robustness, and Explainability: Standardized experimental protocols and datasets for fair comparison. Robustness and security of incremental learning systems against adversarial attacks. Interpretability and explainability of continually learned models.User studies assessing the trust and usability of IL systems in real-world applications Manuscript submission information: Submission Open Date: Feb 28, 2026 Submission Deadline: Dec 31, 2026 Paper submissions for the special issue should follow the submission format and guidelines for regular papers and be submitted at Submit your manuscript | Applied Soft Computing Journal. All the papers will be peer-reviewed following Applied Soft Computing reviewing procedures. Guest editors will make an initial assessment of the suitability and scope of all submissions. Papers will be evaluated based on their originality, presentation, relevance, and contributions, as well as their suitability to the special issue. Each submission must contribute to soft computing related methodology. Papers that either lack originality or clarity in presentation or fall outside the scope of the special issue will be desk-rejected and will not be sent for review. Authors should select “VSI:ASOC_ILAA” when they reach the “Article Type” step in the submission process. The submitted papers must propose original research that has not been published nor is currently under review in other venues. Keywords: Incremental Learning; Zero-Shot Learning; Cross-Modal Learning https://www.sciencedirect.com/special-issue/330311/incremental-learning-algorithms-applications
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