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Numerical Global Optimization Competition on GNBG-II generated Test Suite

Deadline: 2025-06-29
Webpage: https://dsmlossf.github.io/GNBG-Competition-2025/

Description

This competition invites researchers to test the mettle of their global optimization algorithms against a meticulously curated set of 24 problem instances from the Generalized Numerical Benchmark Generator (GNBG). This test suite spans a wide array of problem terrains, from smooth unimodal landscapes to intricately rugged multimodal realms. The suite encompasses:

  • Unimodal instances (f_1 to f_6),
  • Single-component multimodal instances (f_7 to f_15), and
  • Multi-component multimodal instances (f_16 to f_24).


With challenges that include various degrees of modality, ruggedness, asymmetry, conditioning, variable interactions, basin linearity, and deceptiveness, the competition provides a robust assessment of algorithmic capabilities. But this competition is not just about finding optimal solutions. It is about understanding the journey to these solutions. Participants will decipher how algorithms navigate deceptive terrains, traverse valleys, and adapt to the unique challenges posed by each instance. In essence, it is a quest for deeper insights into optimization within complex numerical landscapes. We warmly invite researchers to partake in this competition and subject their global optimization algorithms to this rigorous test.


Organizers

Amir H Gandomi
Amir H. Gandomi is a Professor of Data Science and an ARC DECRA Fellow at the Faculty of Engineering & Information Technology, University of Technology Sydney. He is also affiliated with Obuda University, Budapest, as a Distinguished Professor. Prior to joining UTS, Prof. Gandomi was an Assistant Professor at the Stevens Institute of Technology and a distinguished research fellow at BEACON Center, Michigan State University. Prof. Gandomi has published over three hundred journal papers and 12 books, which have collectively been cited 59,000+ times. He has been named as one of the most influential scientific minds and received the Highly Cited Researcher award (top 1% publications and 0.1% researchers) from Web of Science for six years. In a recent study at Stanford University, released by Elsevier, Prof Amir H Gandomi is ranked 24th most impactful researcher in the AI and Image Processing subfield in 2022! He also ranked 18th in GP bibliography among more than 17,000 researchers. He has received multiple prestigious awards for his research excellence and impact, such as the 2023 Achenbach Medal and the 2022 Walter L. Huber Prize, the highest-level mid-career research award in all areas of civil engineering. He has served as associate editor, editor, and guest editor in several prestigious journals, such as AE of IEEE Networks and IEEE IoTJ. Prof Gandomi is active in delivering keynotes and invited talks. His research interests are global optimisation and (big) data analytics using machine learning and evolutionary computations in particular.


Kalyanmoy Deb
Kalyanmoy Deb is Koenig Endowed Chair Professor at Department of Electrical and Computer Engineering in Michigan State University, USA. Prof. Deb's research interests are in evolutionary optimization and their application in multi-criterion optimization, modeling, and machine learning. He has been a visiting professor at various universities across the world including University of Skövde in Sweden, Aalto University in Finland, Nanyang Technological University in Singapore, and IITs in India. He was awarded IEEE Evolutionary Computation Pioneer Award for his sustained work in EMO, Infosys Prize, TWAS Prize in Engineering Sciences, CajAstur Mamdani Prize, Distinguished Alumni Award from IIT Kharagpur, Edgeworth-Pareto award, Bhatnagar Prize in Engineering Sciences, and Bessel Research award from Germany. He is fellow of IEEE, ASME, and three Indian science and engineering academies. He has published over 548 research papers with Google Scholar citation of over 149,000 with h-index 123. He is in the editorial board on 18 major international journals. More information about his research contribution can be found from https://www.coin-lab.org.


 
Rohit Salgotra

Rohit Salgotra is an Adjunct Researcher at the AGH University of Krakow in Poland. He specializes in Nature-Inspired Computing and has authored over 40 Science Citation Indexed (SCI) publications. Dr. Salgotra has been listed among Stanford University’s Top 2% Most Influential Scientists for the years 2021–2022, within the category of Indian researchers.
Before joining AGH, Dr. Salgotra was a Research Officer at Swansea University, where he conducted studies on the socio-economic aspects of the COVID-19 pandemic. Dr. Salgotra is an Academic Editor for "Mathematical Problems in Engineering" and a reviewer for several journals, including "IEEE Transactions on Evolutionary Computing" and "Swarm and Evolutionary Computing," among more than twenty other SCI journals.