Excited to share that our NWO OTP project DESIRE has been accepted!

Together with ASML, University of Amsterdam, Leiden University, and TNO-ESI, we are starting a new research project on design-space exploration for complex distributed cyber-physical systems. Thank you ASML and TNO-ESI for contributing to the project, and to Philips, Canon Production Printing, Thales, Vanderlande, Eindhoven University of Technology, and University of Twente for joining the user committee.

Design-space Exploration for Complex Distributed Cyber-Physical Systems (DESIRE) builds on our earlier work in DSE 2.0, extending it toward an advanced holistic and automated approach to exploring alternative hardware platforms, software changes, and mappings — helping engineers answer critical what-if questions on performance and cost in increasingly complex systems. In particular, we will focus on:
1) Capturing realistic system behavior from traces while scaling to industrial systems with partial observability and complex environments
2) Bridging software and hardware characterization to enable model-based exploration of performance across heterogeneous platforms.
3) Handling extremely large, heterogeneous, multi-objective design spaces.

At the same time, it’s great to see how earlier results are already being picked up, matured, and experimented with in practice by TNO-ESI and ASML, closing the loop between academic research and industrial impact.

Looking forward to this next step in the collaboration!

The announcement of the grant from NWO is available here.

Hyperheuristic Optimization in Cyber-Physical System Design

Today, we proudly celebrate Lars van der Water’s successful defense of his Master’s thesis, Exploring Vast Design Spaces with Hyperheuristics: Theoretical Foundations and Autotuning Implementation, at the University of Amsterdam. This work has been conducted in connection with the DSE2.0 project, a research collaboration between University of Amsterdam, Leiden University, and ASML, co-funded by NWO and TNO-ESI as a part of the Mastering Complexity (MasCot) Program.

Lars’ thesis addresses the growing complexity in designing Distributed Cyber-Physical Systems, which are increasingly vital to infrastructure and industry. Traditional Design Space Exploration methods struggle with scalability, algorithm selection, and parameter tuning, creating a bottleneck in efficient exploration of system designs. To overcome this, this work explores hyperheuristics (HHs) as a higher-level domain-agnostic approach to automate the selection and tuning of metaheuristics.  Key contributions include a modular framework for integrating HH strategies, and empirical insights into the trade-offs between performance, effort, and computational cost in autotuning. Experiments show promising results for auto-tuning of simpler meta-heuristic search algorithms like Gravitational Search and Particle Swarm Optimization, but revealing limitations with more complex ones like Genetic Algorithms.

We sincerely thank Lars for the excellent collaboration and wish him all the best in the next chapter of his career!