New Advances in Combinatorial Multi-Objective Optimization and Computational Intelligence

Dear Colleagues,

Multi-objective combinatorial optimization (MOCO) involves resolving problems characterized by multiple conflicting objectives within a finite discrete solution space. MOCO is prevalent in various domains such as logistics, telecommunications, finance, and engineering. However, finding all the exact Pareto-optimal solutions for a MOCO problem is highly challenging. Consequently, computational intelligence methods have been developed to identify a manageable number of approximated Pareto solutions within a reasonable computational budget. Despite the advancements achieved, these methods may still struggle with the complexity and trade-offs inherent in MOCO, particularly in complex real-world applications. Therefore, innovative algorithms and methodologies for efficiently addressing MOCO are crucial.
The title of this Special Issue not only reflects the subject of the Special Issue itself but also provides a direct link to the 2025 IEEE Congress on Evolutionary Computation (IEEE CEC)(https://www.cec2025.org/index/page.html?id=1391), held in Hangzhou, China, Jun 8-12, 2025. The IEEE CEC conference is a central event in the field of evolutionary computation.

This Special Issue invites submissions from conference participants. Authors of papers presented at the conference—especially those from the "Multimodal Data-Driven Optimization (MMDD)" workshop—are encouraged to submit extended or enhanced versions of their work.
The publications resulting from the event are usually turned into one or more Special Issues in selected journals known for post-conference publications, such as this very Special Issue. We also welcome submissions from researchers worldwide, regardless of conference attendance. Topics of interest include (but are not limited to) the following:

• Multi-objective optimization;
• Computational intelligence;
• Combinatorial optimization;
• Evolutionary computation;
• Optimization methods and algorithms;
• Trade-off analysis;
• Algorithm design and analysis;
• Optimization models and algorithms in real-world applications;
• Multimodal optimization and clustering;
• Data-driven calibration and wearable technologies;
• Evolutionary computation and optimization;
• Large language models in industry applications;
• Mathematical methods in image and signal processing.

Prof. Dr. Han Huang
Dr. Yi Xiang
Guest Editors

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