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 ------- :download:`Available here<../../LICENSE.txt>` Supporting institutions ----------------------- Faculté Polytech Mons, Université de Mons, Belgique .. image:: ../logos/logo_polytech.jpeg :scale: 75% :align: center .. image:: ../logos/logo_umons.png :scale: 75% :align: center Université de Lille, CNRS CRIStAL, Inria Lille, France .. image:: ../logos/logo_lille.png :scale: 50% :align: center .. image:: ../logos/logo_cnrs.png :scale: 30% :align: center .. image:: ../logos/logo_cristal.png :scale: 40% :align: center .. image:: ../logos/logo_inria.png :scale: 10% :align: center Collaborators ------------- School of Public Health and Preventive Medicine, Monash University, Australia .. image:: ../logos/logo_monash.png :scale: 30% :align: center