Parallel Processing in Sequential Approximate Optimization

The paper presents a first level of coarse-grained parallelization in a sequential approximate optimization framework. A sequential approximate optimization framework builds local approximations of the system every iteration by evaluating a set of design points around the current design. In this research the database is generated by distributing the data sampling process among several processors in a cluster. Two test problems are implemented in a 32 processor cluster. Communications and process control is performed using a message passing interface (MPI) implementation called LAM (Local area multicomputer). The MPI application sends to each processor a set of points to evaluate during the database generation step. Results demonstrate that the use of a cluster of computers to perform the optimization reduces significantly the overall computational time.