Introduction

GloMPO (Globally Managed Parallel Optimization) is an optimization framework that supervises and controls traditional optimization routines in real time using customizable heuristics. By monitoring the performance of each of these optimizers in real time, the GloMPO manager is able to make decisions to terminate and start new optimizers in better locations.

GloMPO is designed to be used on high-dimensional, expensive, multimodal, black-box optimization problems but simpler problems are not precluded.

The three main advantages of optimization in this way:

1. Optimizers are pushed out of local minima, thus more and better solutions are more likely to be found;

2. Through terminations of optimizers stuck in local minima, function evaluations can be used more efficiently;

3. The use of multiple optimizers allows multiple competitive/equivalent solutions to be found.