Introduction
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GloMPO (**Glo**bally **M**anaged **P**arallel **O**ptimization) 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.

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