October 2018 - September 2021
Team
Doctoral Members: Ana Luísa Custódio (PI), Pedro Medeiros (co-PI), Maria do Carmo Brás, Rohollah Garmanjani, and Vítor Duarte
Students: Aboozar Mohammadi, Everton Silva, Sérgio Tavares,Tiago Cordeiro, Nelson Santos, and Bruno Baptista
Consultants: Milagros Loreto (University of Washington Bothell) and Luís Nunes Vicente (Lehigh University)
Inspired by successful approaches used in single objective local Derivative-free Optimization, and resourcing to parallel/cloud computing, new numerical algorithms will be proposed and analyzed. As result, an integrated toolbox for solving multi/single objective, global/local Derivative-free Optimization problems will be available, taking advantage of parallelization and cloud computing, providing an easy access to several efficient and robust algorithms, and allowing to tackle harder Derivative-free Optimization problems.
Related publications
Papers
Theses
Computational toolbox and codes
BoostDFO toolbox comprises solvers for global/local single/multi objective Derivative-free Optimization, allowing to a non-experienced user to take advantage of a suite of robust and efficient solvers, without the burden of mastering all the algorithmic options. Parallel implementations are already available for some of the solvers.
Solvers included: BoostDMS, BoostSID_PSM, BoostGLODS, and BoostMultiGLODS
Version 0.2, December 2020 (written in Matlab; request by sending an e-mail)
BoostDMS is a multiobjective optimization solver which does not use any derivatives of the objective function components.The algorithm defines a search step for Direct Multisearch (DMS) based on the minimization of quadratic polynomial models, built for the different components of the objective function.
Problem class: derivative-free multiobjective optimization problems with (or without) any type of constraints.
Version 0.3, December 2020 (written in Matlab; includes parallelization of function evaluation, with possibility of simultaneously selecting more than one poll center; request by sending an e-mail)
BoostSID_PSM is a single objective optimization solver which does not use any derivatives of the objective function. The algorithm defines a search step based on the minimization of quadratic polynomial models and uses an efficient order of the poll directions.
Problem class: derivative-free single objective optimization problems with (or without) any type of constraints
Version 0.2, September 2020 (written in Matlab; includes parallelization of function evaluations; request by sending an e-mail)
BoostGLODS is a single objective global optimization solver which does not use any derivatives of the objective function. The algorithm uses a clever multistart strategy, where new searches are initialized but not always conducted until the end. Local optimization is based on directional direct search.
Problem class: global single objective derivative-free optimization problems with bound constraints
Version 0.1, September 2020 (written in Matlab; request by sending an e-mail)
BoostMultiGLODS is a global multiobjective optimization solver which does not use any derivatives of the objective function components. The algorithm uses a clever multistart strategy, where new searches are initialized but not always conducted until the end. Local optimization is based on direct multisearch.
Problem class: global multiobjective derivative-free optimization problems with bound constraints
Version 0.1, September 2020 (written in Matlab; includes parallelization of function evaluations and new strategies for the selection of the poll center; request by sending an e-mail)