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Author Guidelines

Authors are invited to make a submission to this journal. All submissions will be assessed by an editor to determine whether they meet the aims and scope of this journal. Those considered to be a good fit will be sent for peer review before determining whether they will be accepted or rejected.

Before making a submission, authors are responsible for obtaining permission to publish any material included with the submission, such as photos, documents and datasets. All authors identified on the submission must consent to be identified as an author. Where appropriate, research should be approved by an appropriate ethics committee in accordance with the legal requirements of the study's country.

An editor may desk reject a submission if it does not meet minimum standards of quality. Before submitting, please ensure that the study design and research argument are structured and articulated properly. The title should be concise and the abstract should be able to stand on its own. This will increase the likelihood of reviewers agreeing to review the paper. When you're satisfied that your submission meets this standard, please follow the checklist below to prepare your submission.

Submission Preparation Checklist

All submissions must meet the following requirements.

  • This submission meets the requirements outlined in the Author Guidelines.
  • This submission has not been previously published, nor is it before another journal for consideration.
  • All references have been checked for accuracy and completeness.
  • All tables and figures have been numbered and labeled.
  • Permission has been obtained to publish all photos, datasets and other material provided with this submission.

Special Session 1：Hybrid Intelligence-Driven Optimization and Decision-Making for Complex Systems

Topics include but are not limited to:

• Formal Theories and Modeling Frameworks for Hybrid Intelligence

• Unified Decision Theories Integrating Logic, Probability, and Learning

• Human-in-the-Loop Optimization and Interactive Machine Learning

• Neuro-Symbolic Reasoning and Explainable Hybrid Decision Models

• Deep Fusion Architectures Integrating Symbolic Rules and Neural Networks

• Multi-Agent Collaboration, Game Theory, and Swarm Intelligence Decision-Making

• Digital Twins and Dynamic Simulation-Deduction for Complex Systems

• Integration of Large Models (LLM/MLLM) with Optimization and Decision-Making

• Innovative Applications of Hybrid Intelligence Optimization in Smart Manufacturing, Energy and Power Systems, and Other Fields

• Architectures, Platforms, and Evaluation Methods for Hybrid Intelligent Decision Systems

Special Session 2：Special Session on Analysis, Modeling and Control with Complex Data

Topics include but are not limited to:

• Data mining

• Pattern recognition

• Data modeling and optimization

• Data driven control

• Decision making system

• Knowledge discovery

• Theory for security and cybernetics

• Applications related to the above topics

 

Special Session 3：Biomedical and Health Informatics

Topics include but are not limited to:

• Data Mining, Machine Learning, and Artificial Intelligence

• Big Data Analytics

• Information Retrieval, Ontologies, Natural Language Processing, and Text Miningn

• Biomedical Image Analysis

• Healthcare Knowledge Representation & Reasoning

• Data Visualization

• Data Interoperability and Health Information Exchange

• Human-computer Interaction and Human Factors

• Clinical and Health Information Systems

• Consumer Informatics and Personal Health Records

• Electronic Medical/Health Records and Standards

• Mobile Health

• Clinical Decision Support

Special Session 4：LLM-Enhanced and Data-Driven Dynamic Optimization and Its Applications

Topics include but are not limited to:

• Data-Driven and/or LLM-Enhanced Dynamic Optimization

• Data-Driven and/or LLM-Enhanced Dynamic Multi-Objective Optimization

• Data-Driven and/or LLM-Enhanced Dynamic Constrained Optimization

• Data-Driven and/or LLM-Enhanced Dynamic Constrained Multi-Objective Optimization

• LLM-Enhanced Knowledge Integration for Dynamic Optimization

• Data-Driven and/or LLM-Enhanced Architecture for Large-Scale Dynamic Optimization

• Data-Driven and/or LLM-Enhanced Optimization Framework for Dynamic Route Planning

• LLM-Based Dynamic Constrained Optimization in Resource Allocation

• Industrial Applications of LLM-Enhanced Dynamic Optimization

• Dynamic Multi-Objective Optimization in Smart Grids, Robotics, and Autonomous Systems with LLM Enhancement

• Data-Driven and/or LLM-Enhanced Dynamic Optimization of Transportation Systems

• LLM-Enhanced Uncertainty Handling in Dynamic Optimization

• Real-Time Data Processing for LLM-Enhanced Dynamic Optimization

 

Special Session 5：Data-Driven Evolutionary Neural Architecture Search and Generative Model Optimization for Complex Systems

Topics include but are not limited to:

• Data-Driven Evolutionary Neural Architecture Search

• Evolutionary Optimization for Deep Neural Network Design

• Multi-Objective Neural Architecture Search

• Bayesian Optimization and Surrogate-Assisted NAS

• Evolutionary NAS for Generative Adversarial Networks

• Diffusion Model Architecture Search and Optimization

• Diffusion Models for Optimization and Model Design

• LLM-Assisted Neural Architecture Search and Optimization

• Data-Driven Decision Paradigms for Neural and Generative Systems

 

Special Session 6：AI-driven Scheduling Optimization, Supply Chain Management and Industrial Internet

Topics include but are not limited to:

• Scheduling Algorithms and Optimization Techniques

• Supply Chain Network Design and Optimization

• Logistics Planning and Route Optimization

• Resource Allocation in Industrial Systems

• Inventory Management and Optimization

• Demand Forecasting and Supply Planning

• Production Scheduling and Job Shop Optimization

• Green Logistics and Sustainable Supply Chain Management

• Real-time Monitoring and Supply Chain Visibility

• Integration of IIoT with Supply Chain Decision Support

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