BAA 2025 will be held on ZOOM, 12-13 November 2025
BAA 2025 will be held on ZOOM, 12-13 November 2025
09:10 - 14:30 GMT
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Free event
Free event
Registration link:
Call for Contributions
We invite chapters in all areas of engineering that use the Bees Algorithm, including but not limited to:
• Bioengineering
• Chemical Engineering
• Civil Engineering
• Computer Science
• Electrical Engineering
• Environmental Engineering
• Industrial Engineering
• Informatics
• Mechanical Engineering
• Operations Research
Authors of accepted chapters will present their work on 12-13 November 2025 and answer questions from attendees.
Following the workshop, the chapters included in a peer-reviewed book edited by D T Pham and N Hartono
If you have further queries, please contact Natalia at this email address:
Important Dates:
Manuscript Submission opens: 01 April 2025
Submission deadline: 10 October 2025
Presentation: 12 - 13 Nov 2025
Keynote Speakers:
Prof. Adil Baykasoğlu
Dokuz Eylül University, Türkiye
Adil Baykasoğlu received his B.Sc., M.Sc. and Ph.D. degrees from Mechanical & Industrial Engineering areas in Turkey (Gaziantep) and United Kingdom (Nottingham). He is presently a full Professor at the Industrial Engineering Department at the Dokuz Eylül University. He has organized many academic conferences, published numerous academic papers, three books and organized/edited several conference books on operational research, computational intelligence, engineering management, and manufacturing systems design. He is an active editor and editorial board member for many scientific journals. He is also an active member of many academic and professional institutions including the International Society of Agile Manufacturing, Turkish Chamber of Mechanical Engineers, Turkish Operational Research Association, etc. He has received awards from Turkish Academy of Sciences (TUBA), Scientific and Technological Research Council of Türkiye (TUBITAK), Middle East Technical University (METU), and several other institutions for his scientific contributions. ORCID: 0000-0002-4952-7239, Web: https://web.deu.edu.tr/baykasoglu
Keynote 1: Metaheuristics Revisited: Challenges, Innovations and Future Trajectories
Metaheuristics are versatile, stochastic search algorithms that operate independently of specific problem domains and have demonstrated success across a wide range of scientific disciplines. Their widespread application has contributed to the development of more than 250 algorithms in the literature, often introduced under the premise of being innovative, high-performing, or promising. However, this proliferation has also sparked substantial criticism within the research community. Common concerns include the lack of genuine novelty, many metaheuristic algorithms exhibit structurally similar frameworks; inadequate empirical validation, which may conceal limited practical effectiveness; and the reliance on metaphor-driven nomenclature that often lacks alignment with established optimization theory.
Despite these critiques, there remains a persistent tendency among researchers to draw inspiration from new natural phenomena rather than focus on advancing the theoretical foundations of optimization or improving existing metaheuristic frameworks. This inclination may, in part, be influenced by the No Free Lunch Theorem, which asserts that all metaheuristics perform equivalently when averaged across all possible problems. Consequently, the theorem may unintentionally incentivize the continual development of novel algorithms aimed at outperforming others in specific problem instances.
Nevertheless, significant opportunities remain to strengthen and refine existing metaheuristic methods including Bees Algorithm and others. Areas of high potential include the design of intelligent step-size adaptation strategies, enhanced search direction mechanisms, robust constraint-handling techniques, and effective encoding schemes for complex discrete optimization problems. Further research is also needed in the domains of stochastic and fuzzy optimization, dynamic optimization, hybridization with machine learning algorithms, efficient memory utilization and the parallelization of population-based methods. Finally, the integration of metaheuristics with mathematical programming techniques presents a promising avenue for advancing both the theoretical and practical capabilities of optimization approaches.
Prof. Dr. Mete Kalyoncu
Konya Technical University, Türkiye
Prof. Dr. Mete KALYONCU is a faculty member in the Department of Mechanical Engineering at Konya Technical University. His research spans mechanical vibrations, system dynamics, mechanism design, control systems, robotics, and artificial intelligence-based optimization methods. He earned his Ph.D. in Mechanical Engineering from Selçuk University in 1998 and later completed postdoctoral research at Cardiff University, UK.
Prof. KALYONCU has published extensively in national and international journals, supervised numerous graduate theses, and led or contributed to more than 50 publicly funded R&D projects. He served on the board of TÜBİTAK TEYDEB MAKITEG, where he evaluated over 1,200 industrial R&D proposals, and has advised several companies in establishing R&D centres.
In addition to his academic and industrial contributions, Prof. KALYONCU has played a leading role in the Chamber of Mechanical Engineers (TMMOB), serving multiple terms as Chair of the Konya Branch. Through organizing technical events and editing reference publications, he has significantly advanced the profession. His career reflects a strong integration of academic research, industrial innovation, and professional service in mechanical engineering.
Keynote 2: Real-World Industrial Applications of the Bees Algorithm for Optimisation
This keynote will present real-world industrial applications of the Bees Algorithm, a swarm intelligence-based optimization technique inspired by the foraging behaviour of honeybees. Drawing on practical case studies from manufacturing, process design, and system optimization, the talk will demonstrate how the algorithm has been successfully applied to improve efficiency, reduce costs, and solve complex engineering problems. Emphasis will be placed on lessons learned from industrial practice, highlighting both the opportunities and challenges of implementing nature-inspired algorithms in real settings. Future directions for expanding the Bees Algorithm’s role in industry will also be discussed.
Prof. Ahmed Haj Darwish
University of Aleppo, Syria
Professor Ahmed Haj Darwish is a faculty member at the University of Aleppo, Syria. Where he serves in the Department of Artificial Intelligence and Natural Languages, Faculty of Informatics Engineering. He received his Ph.D. in Systems Engineering from Cardiff University, United Kingdom, in 2009, under the supervision of Professor D.T. Pham. His doctoral research, entitled “Enhanced Bees Algorithm with Fuzzy Logic and Kalman Filtering,” proposed a hybrid optimization framework that combined swarm intelligence with fuzzy reasoning and probabilistic filtering, contributing to the advancement of adaptive computational methods.
His research interests cover a wide spectrum of fields, including robotics, intelligent optimization techniques, fuzzy and neural systems, and image processing. Much of his work emphasizes the integration of these approaches into practical applications, ranging from autonomous systems and intelligent control to advanced computational modeling.
Beyond his research, Professor Haj Darwish is deeply engaged in academic development and knowledge transfer. He has contributed to curriculum design, graduate supervision, and the promotion of interdisciplinary collaboration in artificial intelligence and engineering. His commitment lies in bridging theoretical innovation with real-world problem-solving, fostering both academic excellence and applied impact.
Keynote 3: The Evolution of the Bees Algorithm: Development, Enhancements, Applications, and Future Research Directions
Prof. Xin Yao
Lingnan University, Hong Kong SAR
Xin Yao is Vice President (Research & Innovation) and Tong Tin Sun Chair Professor of Machine Learning at Lingnan University, Hong Kong SAR. He is an IEEE Fellow and a Fellow of Hong Kong Academy of Engineering. He served as the President (2014-15) of IEEE Computational Intelligence Society (CIS) and the Editor-in-Chief (2003-08) of IEEE Transactions on Evolutionary Computation. His major research interests include evolutionary computation, machine learning and trustworthy AI. His work won the 2001 IEEE Donald G. Fink Prize Paper Award; 2010, 2016 and 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Awards; 2011 IEEE Transactions on Neural Networks Outstanding Paper Award; 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist); and other best paper awards at conferences. He received the 2012 Royal Society Wolfson Research Merit Award, 2013 IEEE CIS Evolutionary Computation Pioneer Award and 2020 IEEE Frank Rosenblatt Award.
Keynote 4: Evolutionary Approaches to Capacitated Arc Routing Problems
Capacitated arc routing problems (CARPs) cover a wide range of real-world problems, from gritting truck routing in winter to urban waste collection, and from parcel delivery to street sweeping. Fundamentally they are hard problems, i.e., NP-hard hard, if an exact global optimum is required. Various approaches have been proposed in the literature to tackle CARPs, from exact methods for small-scale CARPs to (meta-)heuristic methods for finding near optimal solutions. This talk overviews some of the evolutionary algorithms for solving large-scale CARPs. In particular, four ideas will be presented: (a) repairing infeasible solutions, (b) combining local search into an evolutionary algorithm (i.e., a memetic algorithm); (c) extended neighbourhood search; and (d) co-evolutionary algorithms. Then this talk considers some CARP variants, e.g., multiple depots, periodic services, and multiple objectives, and their solutions using evolutionary approaches. Finally, the talk will consider dynamics in real-world CARPs and outline strategies for tackling CARPs in uncertain and dynamic environments, where road conditions and demands change over time.
Prof. Swagatam Das
Indian Statistical Institute, Kolkata, India
Swagatam Das earned his B.E. in Electronics and Telecommunications Engineering, M.E. with a specialization in Control Engineering, and Ph.D.(Engineering) degrees from Jadavpur University, India, in the years 2003, 2005, and 2009, respectively. He is currently a professor at the Electronics and Communication Sciences Unit (ECSU) of the Indian Statistical Institute, Kolkata, India. He is also serving as the Professor-in-Charge of the Computer and Communication Sciences Division (CCSD) of his Institute for the term 2024 - 26. He previously held the position of Professor and Deputy Director at the Institute for Advancing Intelligence (IAI), TCG CREST, Kolkata, India, from April 01, 2023, to March 31, 2024. His research interests encompass deep learning and non-convex optimization, and he has published over 400 research articles in peer-reviewed journals and international conferences. Dr. Das is the founding Co-Editor-in-Chief of Swarm and Evolutionary Computation, an international journal by Elsevier. He has served or is currently serving as an Associate Editor for several prominent journals, including the IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Evolutionary Computation, Pattern Recognition (Elsevier), Neurocomputing (Elsevier), Information Sciences (Elsevier), IEEE Trans. on Systems, Man, and Cybernetics: Systems, among others. He is a member of the editorial board of Information Fusion (Elsevier), Progress in Artificial Intelligence (Springer), Applied Soft Computing (Elsevier), Engineering Applications of Artificial Intelligence (Elsevier), and so on. Dr. Das has received over 36,500 Google Scholar citations and an H-index of 90 to date. He has actively participated in the program committees and organizing committees of renowned international conferences such as NeurIPS, AAAI, AISTATS, ACM Multimedia, BMVC, IEEE WCCI, GECCO, and more. He currently serves as an ACM Distinguished Speaker. He received the 2012 Young Engineer Award from the Indian National Academy of Engineering (INAE) and the 2015 Thomson Reuters Research Excellence India Citation Award for being the highest-cited researcher in Engineering and Computer Science in India between 2010 and 2014.
Keynote 5: Beyond Benchmarking: Bayesian Testing and Modern Indicators for Comparing Swarm Intelligence Algorithms
Swarm intelligence and other bio-inspired optimization algorithms are increasingly applied to challenging non-convex and high-dimensional problems, yet rigorous evaluation of their performance remains unsettled. Traditional benchmarking practices—based on convergence curves, run-time distributions, or non-parametric tests such as Friedman and Wilcoxon—often provide limited or inconsistent insights. This talk surveys modern performance indicators for single-objective continuous optimization and emphasizes recent advances in Bayesian statistical testing, effect size estimation, and probabilistic performance profiling. Unlike classical rank-based approaches, Bayesian models yield richer, more interpretable comparisons of algorithm reliability and reproducibility. By integrating these perspectives, we highlight how algorithm–problem relationships can be more faithfully understood and outline a principled framework for the fair and transparent comparison of swarm intelligence methods in both research and real-world deployment.
Organising Committee
Dr. (cand) Hamid F Suluova – University of Birmingham, UK
Dr. (cand) Fatih M Eker – University of Birmingham, UK
Dr. (cand) Muhammed Ali Erdem - University of Birmingham, UK
Dr. Rongge Guo - Beijing Jiaotong University, China
Achmad Jaelani, SE, MM – SWINS, Indonesia
Dr. Lavanya Meherishi - University of Exeter
Atik Budi Paryanti, SPd, MM – SWINS, Indonesia
Dr. Winayah Purwanti, SE,MM – SWINS, Indonesia
Dr. (cand) Yishuang Wang - University of Birmingham, UK
Safrudin, S.Kom, M.Ak – SWINS, Indonesia
Salim, S.Ilkom, M.Si - SWINS, Indonesia
Dr. Solihin, SE., M.Ak - SWINS, Indonesia
Dr (cand) Hang Yang
Dr (cand) Castle - University of Birmingham, UK
Scientific Committee
Dr. Mei Choo Ang – University Kebangsaan Malaysia, Malaysia
Prof. Adil Baykasoglu - Dokuz Eylül University, Turkey
Dr. Marco Castellani – University of Birmingham, UK
Dr. Mario Caterino – University of Campania Luigi Vanvitelli, Italy
Prof. Ahmed Haj Darwish – University of Aleppo, Syria
Prof. Doriana M. D’Addona – University of Naples Federico II, Italy
Prof. Nguyen Dinh Duc – Vietnam National University, Hanoi, Vietnam
Dr. Hector de la Torre Gutiérrez – Mathematics Research Centre, Mexico
Prof. Jun Huang – Wuhan University of Technology, China
Dr. Kaiwen Jiang - Brunnel University of London, UK
Dr. Shafie Kamarrudin – International Islamic University, Malaysia
Dr. Feiying Lan - University of Birmingham, UK
Dr. Jiayi Liu – Wuhan University of Technology, China
Dr. Martino Luis - University of Exeter, UK
Dr. Massudi bin Mahmuddin - Universiti Utara Malaysia, Malaysia
Dr. Ernesto Mastrocinque – Coventry University, UK
Yusraini Muharni, S.T., M.T. - Sultan Ageng Tirtayasa University, Indonesia
Dr. Michael Packianather – Cardiff University, UK
Prof. F. Javier Ramirez – Universidad de Castilla-La Mancha, Spain
Dr. Mozafar Saadat – University of Birmingham, UK
Dr. Shahnorbanun Sahran – University Kebangsaan Malaysia, Malaysia
Dr. Yanjie Song - Dalian Maritime University, China
Prof. Voicu Ion Sucala - University of Exeter, UK
Let.Col. Nathinee Theinnoi, PhD - Chulachomklao Royal Military Academy, Thailand
Dr. Tran Duc Vi - Vietnam National University, Vietnam
Dr. Chao Wang - Changshu Institute of Technology, Suzhou, China
Dr. Yongjing Wang - University of Birmingham, UK
Prof. Wenjun Xu SME Member OYME – Wuhan University of Technology, China
Dr. Baris Yuce - University of Exeter, UK
Dr. Sultan Zeybek – Fatih Sultan Mehmet Vakif University, Turkey
Dr. Malarvizhi Kaniappan Chinnathai - University of Exeter, UK
Program Book BAA 2025
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