Mare Nostrum (Barcelona Supercomputing Center)

Expensive Combinatorial Optimization Problems

With Ekhine Irurozki, we have proposed UMM, an estimation-of-distribution algorithm based on mallows models for expensive black-box permutation problems. Our initial results indicate that UMM produces similar results to GP-based approaches, but it less computationally demanding and can tackle larger problems.

With Leslie Pérez Cáceres and Thomas Stützle, we have examined the performance of different ant colony optimization (ACO) algorithms under a strongly limited budget of 1K evaluations. We compared the performance of classical ACO algorithms to an ACO algorithm that makes use of surrogate modeling of the search landscapes (which we call EGACO). We not only show that tuning algorithms for the limited budget case is of uttermost importance, but also that, surprisingly, direct search by the classical ACO algorithms keeps an edge over the EGACO variants using surrogate modeling.

Machine Decision-Makers

Anytime Behavior of Optimization Algorithms

Automatic Configuration of Optimization Algorithms

Automatic Design of Optimization Algorithms

Local Search and Local Optima in Multi-Objective Optimization

Multi-Objective Optimization

Multi-objective Permutation Flow-shop Scheduling

Travelling Salesman Problem with Time Windows

Pump Scheduling in Water Distribution Networks

IRIDIA BibTeX Repository

The IRIDIA BibTeX Repository is a collection of high-quality BibTeX files related to my research. Contributions are welcome via github.