Mare Nostrum (Barcelona Supercomputing Center)

Ant Colony Optimization for Expensive Combinatorial Optimization 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

Multi-Objective Optimization

Multi-objective Permutation Flow-shop Scheduling

Travelling Salesman Problem with Time Windows

Pump Scheduling in Water Distribution Networks