Parallel Nonlinear Optimization on a Multiprocessor System with Distributed Memory

Abstract

This paper deals with the solution of practical nonlinear optimization problems which arise especially in the fields of engineering design and production planning. In their mathematical description these problems frequently consist of highly nonlinear objective functions and constraints whose evaluation may lead to time-consuming simulation runs. In these cases assumptions about unimodality, convexity, and smoothness of well-known solution methods in nonlinear optimization are mostly invalid. An intuitive approach to overcoming these problems would be to apply a simultaneous combination of different optimization algorithms. In this situation the basis for a more reliable and even faster solution is the controlled information exchange on the continuing solution progress between the participating and parallel running methods. This idea has been implemented on a multiprocessor system with distributed memory for the solution of nonlinear optimization problems. Based on previous experiences with different algorithms, a coarse-grained parallelization approach under asynchronous control has been developed.