BFO, a trainable derivative-free Brute Force Optimizer for nonlinear bound-constrained optimization and equilibrium computations with continuous and discrete variables

Published: 2015/07/03, Updated: 2017/07/13

(Mixed) Integer Nonlinear Programming, Bound-constrained Optimization, Nonlinear Optimization bound constraints, derivative-free optimization, direct search, mixed-integer programming, trainable algorithms Short URL: https://optimization-online.org/?p=13497

A direct-search derivative-free Matlab optimizer for bound-constrained problems is described, whose remarkable features are its ability to handle a mix of continuous and discrete variables, a versatile interface as well as a novel self-training option. Its performance compares favourably with that of NOMAD, a state-of-the art package. It is also applicable to multilevel equilibrium- or constrained-type problems. Its easy-to-use interface provides a number of user-oriented features, such as checkpointing and restart, variable scaling and early termination tools.

BFO, a trainable derivative-free Brute Force Optimizer for nonlinear bound-constrained optimization and equilibrium computations with continuous and discrete variables

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