Recent Progress in Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes - Sheng Du; Zixin Huang; Li Jin; Xiongbo Wan (Algorithms)
This editorial discusses recent progress in data-driven intelligent modeling and optimization algorithms for industrial processes. With the advent of Industry 4.0, the amalgamation of sophisticated data analytics, machine learning, and artificial intelligence has become pivotal, unlocking new horizons in production efficiency, sustainability, and quality assurance. Contributions to this Special Issue highlight innovative research in advancements in work-sampling data analysis, data-driven process choreography discovery, intelligent ship scheduling for maritime rescue, process variability monitoring, hybrid optimization algorithms for economic emission dispatches, and intelligent controlled oscillations in smart structures. These studies collectively contribute to the body of knowledge on data-driven intelligent modeling and optimization, offering practical solutions and theoretical frameworks to address complex industrial challenges.