Keynote Speakers

 

Prof. D T Pham
The University of Birmingham, UK

Title: The Bees Algorithm: Twenty-One Years of Development and Future Directions
Abstract: Since its introduction in 2005, the Bees Algorithm has developed into a versatile and effective optimisation method, with applications spanning a wide range of engineering problems. Originating from a simple observation of honey bee foraging behaviour, the approach was designed to provide a flexible yet robust framework for exploring complex search spaces. This presentation revisits these underlying ideas, highlighting how the core principles have contributed to the algorithm’s continued relevance.

Over the past two decades, the Bees Algorithm has been extended in a number of important directions. In particular, efforts to improve the balance between exploration and exploitation have led to more efficient and reliable performance. Hybrid variants have further broadened its scope, allowing it to be applied to increasingly complex and large-scale problems. While such developments inevitably introduce new considerations, such as parameter selection and computational cost, they have, on the whole, strengthened the algorithm’s practicality.

From the perspective of its development, one of the more encouraging aspects has been the consistency with which the Bees Algorithm has adapted to new challenges. It has proved capable of delivering competitive results across diverse problem domains, while remaining conceptually accessible and relatively straightforward to implement. These qualities have contributed to its sustained popularity within the research community.

Looking ahead, there is clear potential for further progress. Current work is beginning to explore how the algorithm can be combined with data-driven approaches, including machine learning, to solve hypercomplex multimodal optimisation problems. Such directions are not without challenges, but they offer a natural continuation of the ideas that motivated the algorithm in the first place. Taken together, these developments suggest that the Bees Algorithm remains not only relevant, but well positioned for continued evolution.

Bio: Duc-Truong Pham is the Chance Professor of Engineering at the University of Birmingham. He was Professor of Computer-Controlled Manufacture and Director of the Manufacturing Engineering Centre at Cardiff University. He has published over 700 papers and books on intelligent systems, advanced manufacturing and remanufacturing and has graduated more than 100 PhD students. His awards include five prizes from the Institution of Mechanical Engineers, a Lifetime Achievement Award from the World Automation Congress and a Distinguished International Academic Contribution Award from the IEEE. He is a Fellow of the Royal Academy of Engineering, Learned Society of Wales, SME, IET and IMechE. He is the founding editor of the Springer Series in Advanced Manufacturing and editor-in-chief of Cogent Engineering and the International Journal on Interactive Design and Manufacturing. He obtained his Bachelor's, PhD and DEng degrees from the University of Canterbury (NZ).

Prof. Weijia Jia
Beijing Normal University (Zhuhai), China

Title: Agentic Open Environment Tabular Quality QA
Abstract: The advancement of large language models (LLMs) has enhanced tabular question answering (Tabular QA), yet they struggle with open-domain queries exhibiting underspecified or uncertain expressions. To address this, we introduce Open-Loop Agent QA system that composes of agent workflows with comprehensive benchmark in edge infrastructure to tackle with open tabular for real estate: (1) a large-scale benchmark with 5K tables and 75K QA pairs; a fine-grained labeling scheme for detailed evaluation; and (2) a dynamic clarification interface that simulates user feedback for interactive assessment. We also propose a multi-agent framework that excels at detecting ambiguities, clarifying them through dialogue, and refining answers; (3) A large-scale instances featuring machine-verifiable supervision for intermediate steps, including structured intent labels, SQL queries, and API calls. We propose a hierarchical agent framework instantiating an under-stand–plan–execute architecture as a strong baseline. By orchestrating a Front-end parser, a planning Supervisor, and execution Specialists, hierarchical-agent effectively integrates heterogeneous evidence. Extensive experiments demonstrate that hierarchical-Agent constitutes a strong baseline and substantiates the necessity of hierarchical collaboration for complex, real-world reasoning tasks.

Bio: Professor Weijia Jia (Member of European Academy of Engineering, Member of National Academy of Artificial Intelligence, IEEE Fellow) is currently the Director of Institute of Artificial Intelligence and Future Networking, and Super Intelligent Computer Center, Beijing Normal University (BNU, Zhuhai); also, a Chair Professor at BNBU(UIC), Zhuhai, Guangdong, China. His contributions have been recognized for the research of edge AI, optimal network routing and deployment; vertex cover; anycast and multicast protocols; sensors networking; knowledge relation extractions; NLP and intelligent edge computing. He has over 800 publications in the prestige international journals/conferences and research books and book chapters. He has received the best product awards from the International Science & Tech. Expo (Shenzhen) in 2011/2012 and the 1st Prize of Scientific Research Awards from the Ministry of Education of China in 2017 (list 2), 1st Prize of Shanghai Science and Technology Award (2025, list 4) and top 2% World Scientists in Stanford-list (2020-2026) and many provincial science and tech awards.

Prof. Eneko Osaba Icedo
Basque Research and Technology Alliance, Spain

Title: Quantum Computing in the Real-World: Optimization, Reality and Live Execution.
Abstract: Quantum Computing is often associated with future potential, but what can it truly deliver today? This keynote presents a realistic view of quantum computing applied to real-world optimization problems, particularly in supply chain and industrial contexts. Focusing on hybrid quantum–classical approaches, the talk shows how quantum technologies can already be integrated into existing optimization pipelines to enhance performance. Through real industrial use cases, the session will include live demonstrations executed on an actual quantum computer, showcasing how these systems operate under realistic constraints and with real data. The session moves beyond hype, offering an honest perspective on current capabilities, limitations, and the role of quantum computing as a complementary tool for industrial optimization.

Bio: Dr. Eneko Osaba works at TECNALIA as principal researcher in the DIGITAL/Next area. He obtained his Ph.D. degree on Artificial Intelligence in 2015. He has participated in more than 35 research projects. He has contributed to the development of more than 190 papers, including more than 32 Q1. He has performed several stays in universities of United Kingdom, Italy, China and Malta. He has served as a member of the program and/or organizing committee in more than 60 international conferences. He has acted as guest editor in journals such as Journal of Supercomputing, Swarm and Evolutionary Computation, Engineering Applications of Artificial Intelligence and Quantum Machine Intelligence. In 2022, Eneko was recognized by the Basque Research and Technology Alliance as one of the most promising young researchers of the Basque Country, Spain. Also, Eneko is part of the Stanford/Elsevier's World’s Top 2% Scientist List since 2022.