(Including but not limited to the following topics)
Track 1: Data-driven Optimization
Data-driven optimization algorithms
Bayesian optimization
Neural combinatorial optimization
Large-scale and multi-objective optimization
Integration of machine learning and optimization
Data-driven decision paradigm
Intelligent scheduling
Reinforcement learning for combinatorial optimization
Distributed and joint optimization
Analysis and decision-making for industrial and manufacturing systems
Optimization using diffusion models
LLM-assisted optimization Artificial Intelligence
Deep Learning
Reinforcement Learning
Transfer learning
Self-supervised learning
Distributed and joint machine learning
Trustworthy machine learning
Generative models
Large language models
Automatic verification of LLMs
Coordination of small and large models
AI for science and engineering
AI for education and society
Robust control
Intelligent control
Optimization-based control of ******
Model predictive control
Fault detection and identification
Hybrid intelligent systems
Neural control
Fuzzy logic control
Networked control
Industrial automation
Intelligent transportation systems
Track 2: Intelligent perception
Edge machine learning
Manufacturing intelligence systems
Machine vision Human-computer interaction
Industrial robots
Collaborative robots
Intelligent sensing system
Predictive maintenance and fault diagnosis
Logistics robots
Track 3: Computing
Genetic algorithm
Simulated annealing algorithm
Tabu search algorithm
Evolutionary algorithm
Heuristic algorithm
Ant colony algorithm
Artificial fish swarm algorithm
Particle swarm algorithm
Hybrid intelligent algorithm
Immune algorithm
Neural network Machine Learning