Parallel Direct Search Methods

Abstract

Mostly minimization or maximization of a function is very expensive. Since function evaluation of the objective function requires a considerable time. Hence, our objective in this work is the development of parallel algorithms for minimization of objective functions evaluation takes long computing time. The base of the developed parallel algorithms is the evaluation of the objective function at various points in same time (i.e. simultaneously). We consider in this work the parallelization of the direct search methods, as these methods are non-sensitive for noise and globally convergent. We have developed two algorithms mainly they are dependent on the Hock & Jeff method in unconstrtrained optimization. The developed parallel algorithm are suitable for running on MIMD machine which are consisting of several processors operating independently, each processor has it's own memory and communicating with each other through a suitable network.

Keywords

• nonlinear optimization
• unconstrained optimization
• multidirectional search
• parallel direct search
• parallel computing
• mimd computers