1. Jintai Chen, Fangqing Liu, Xueming Yan, Han Huang. Regionalized Metric Framework: A Novel Approach for Evaluating Multimodal Multi-Objective Optimization Algorithms
2. Shixuan Zhou, Yi Xiang. Discriminative Feature Selection for Critical Scenario Identification In Self-Driving Cars
3. Yue Xie, Kai-Fung Chu, Xing Wang, Fumiya Iida. The Morphology-Control Trade-Off: Insights into Soft Robotic Efficiency
4. Ying Huang, Xiaojian Cao. Multimodal Multi-Objective Evolutionary Algorithm with Hierarchical Ranking and Special Crowding Distance
5. Gan-yi Tang, Gui-Fu Lu. Tensorized Multi-View Subspace Clustering by Tensor Nuclear Norm and Block Diagonal Representation
6. Pengfei Lu, Ping Zhang. LogisticsLLM: Network Freight Price Prediction Based on Large Language Models
7. Gao You, Tao Qian. Investigation on Convergence Efficiency of Two-Dimensional Adaptive Fourier Decompositions in Image Approximation
8. Yazhi Li, Hong Shen, Yuwei Cheng. A Hybrid Harmony Search for Distributed Permutation Flowshop Scheduling with Multimodal Optimization
9. Xueming Yan, Han Huang, Yaochu Jin. Neural Architecture Search Based on Self-Attention Alignment
10. Beiyin Pang, Yunyi Zhao, Shufan Yang. Data-Driven Calibration for Wearable Brain Functional Imaging Devices
11. Siying Lv, Shuwei Zhu, Fang Wei. Adaptive Surrogate-Assisted Evolutionary Multi-Objective Community Detection Algorithm with Core Node Learning

With rapid advancements in data science and artificial intelligence, multimodal data-driven optimization, including evolutionary optimization, swarm optimization and neural optimization, is becoming increasingly critical across a wide range of applications. So far, most data-driven optimization algorithms can make use of numerical data only, while in the real world, many other modalities of data are available. By integrating diverse data types — such as text, images, audio, and structured data, we expect that we can significantly improve the optimization performance in the presence of data paucity.

Additionally, the emergence of Large Language Models (LLMs) and diffusion optimization techniques offers new opportunities to further enhance these methodologies. This workshop aims to bring together researchers, practitioners, and industry experts to explore the latest developments, challenges, and applications in multimodal data-driven optimization, with a particular focus on graph neural networks, diffusion models and large language models, and the integration of these techniques with evolutionary and swarm optimization algorithms.

Topics include but are not limited to:

Multimodal Data-Driven Optimization Algorithms and Frameworks Evolutionary and Swarm Optimization with Multimodal Data Neural Combinatorial Optimization with Multimodal Data Large Language Models (LLMs) / Diffusion Models for Optimization Federated Learning Optimization in Multimodal Contexts Reinforcement Learning Approaches in Multimodal Optimization Transfer Learning and Domain Adaptation in Multimodal Optimization Causal Inference and Reasoning in Multimodal Optimization Explainable AI and Interpretability in Multimodal Optimization Models Benchmarking and Evaluation Metrics for Multimodal Optimization Methods Real-World Applications of Multimodal Optimization in Medicine, Finance, Manufacturing, Robotics, ect.

Hangzhou International Expo Center

Each submission can be up to 8 pages of contents plus up to 2 additional pages of references and acknowledgements. The submitted papers must be written in English and in PDF format according to the CEC2025 template. All submitted papers will be under a single-blind peer review for their novelty, technical quality and impact. The submissions can contain author details. Submission will be accepted via the OpenReview submission website.

Based on the requirement from MMDO@CEC2025, at least one author of each accepted paper must travel to the MMDO@CEC2025 venue in person. In addition, multiple submissions of the same paper to more than one MMDO@CEC2025 workshop are forbidden.

OpenReview submission site: https://openreview.net/group?id=IEEE.org/CEC/2025/Workshop/MMDO

For inquiries, please email to: yanxm@gdufs.edu.cn