Handling constraints with Derivative-Free
Optimization
X.Fu, A. R. Almomani, M. Minick, K.R.Fowler
Department of mathematics and computer science
Derivative free optimization (DFO) methods are designed to solve optimization
problems whose gradient information is unavailable or unreliable and thus the
minimization is guided solely by function values. Nelder-Mead, Genetic
Algorithm, Implicit Filtering and Particle Swarm Optimization are some of the
commonly used DFO methods. On the other hand, there is little guidance for DFO
methods to solve problems with linear and non-linear constraints which define the
feasible search region. Typical methods to handle constraints include the penalty
and the barrier methods. In this study, we seek to identify the best-performing pair
of DFO and constrained methods based on numerical experiments using a suite of
constrained optimization test problems. Our current focus is on collecting
performance measures to assess algorithm behavior based on properties of
objective functions such as non-smoothness, local minima, low amplitude noise
and discontinuous domains.
{2011}, {Applied Mathematics}, {Honors Program}, {K.R.Fowler}