About pySBO

The platform aims at facilitating the implementation of parallel surrogate-based optimization algorithms. pySBO provides re-usable algorithmic components (surrogate models, evolution controls, infill criteria, evolutionary operators) as well as the foundations to ensure the components inter-changeability. Actual implementations of sequential and parallel, surrogate-based and surrogate-free optimization algorithms are supplied as ready-to-use tools to handle very and moderately expensive single- and multi-objective problems with continuous decision variables. The MPI implementation allows to execute on distributed machines. Box-constraints are explicitly integrated while more elaborated constraints must be handled by the user.

At a glance:

• Surrogate-Assisted Evolutionary Algorithms for moderately expensive problems

• Surrogate-Driven Algorithms for very expensive problems

• Surrogate-Free Algorithms for unexpensive problems

• Single- and multi-objective

• Continuous decision variables

• Parallel evaluations of the objective function

• Parallel Acquisition Processes

• Centered on Evolutionary Algorithms

• Box-constrained problems

Notes

pySBO is organized following the one-class-per-file Java convention. Consequently, each module is nammed after the class it contains.

Support

guillaume.briffoteaux@gmail.com

Background on Surrogate-Based Optimization

G. Briffoteaux. Parallel surrogate-based algorithms for solving expensive optimization problems. Thesis. University of Mons (Belgium) and University of Lille (France). 2022.

G. Briffoteaux, N. Melab, M. Mezmaz et D. Tuyttens. Hybrid Acquisition Processes in Surrogate-based Optimization. Application to Covid-19 Contact Reduction. International Conference on Bioinspired Optimisation Methods and Their Applications, BIOMA, 2022, Maribor, Slovenia, Lecture Notes in Computer Science, vol 13627. Springer, pages 127-141

G. Briffoteaux, R. Ragonnet, P. Tomenko, M. Mezmaz, N. Melab et D. Tuyttens. Comparing Parallel Surrogate-based and Surrogate-free Multi-Objective Optimization of COVID-19 vaccines allocation. International Conference on Optimization and Learning, OLA, 2022, Syracuse, Italy, Communications in Computer and Information Science, vol 1684. Springer, pages 201-212,

G. Briffoteaux, R. Ragonnet, M. Mezmaz, N. Melab and D. Tuyttens. Evolution Control Ensemble Models for Surrogate-Assisted Evolutionary Algorithms. HPCS 2020 - International Conference on High Performance Computing and Simulation, 22-27 March 2021, Onlineconference.

G.Briffoteaux, M.Gobert, R.Ragonnet, J.Gmys, M.Mezmaz, N.Melab and D.Tuyttens. Parallel Surrogate-assisted Optimization: Batched Bayesian Neural Network-assisted GA versus q-EGO. Swarm and Evolutionary Computation, 57:100717, 2020.

G.Briffoteaux, R.Ragonnet, M.Mezmaz, N.Melab and D.Tuyttens. Evolution Control for Parallel ANN-assisted Simulation-based Optimization, Application to Tuberculosis Transmission Control. Future Generation Computer System, 113:454-467, 2020.

Author

Guillaume Briffoteaux

License

Available here

Supporting institutions

Faculté Polytech Mons, Université de Mons, Belgique

Université de Lille, CNRS CRIStAL, Inria Lille, France

Collaborators

School of Public Health and Preventive Medicine, Monash University, Australia