  Multi-start algorithms are a common and effective tool for metaheuristic
searches. In this paper we amplify multi-start capabilities by employing the
parallel processing power of the graphics processer unit (GPU) to quickly
generate a diverse starting set of solutions for the Unconstrained Binary
Quadratic Optimization Problem which are evaluated and used to implement
screening methods to select solutions for further optimization. This method is
implemented as an initial high quality solution generation phase prior to a
secondary steepest ascent search and a comparison of results to best known
approaches on benchmark unconstrained binary quadratic problems demonstrates
that GPU-enabled diversified multi-start with screening quickly yields very
good results.
