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Multi-start algorithms are a common and effective tool for metaheuristicsearches. In this paper we amplify multi-start capabilities by employing theparallel processing power of the graphics processer unit (GPU) to quicklygenerate a diverse starting set of solutions for the Unconstrained BinaryQuadratic Optimization Problem which are evaluated and used to implementscreening methods to select solutions for further optimization. This method isimplemented as an initial high quality solution generation phase prior to asecondary steepest ascent search and a comparison of results to best knownapproaches on benchmark unconstrained binary quadratic problems demonstratesthat GPU-enabled diversified multi-start with screening quickly yields verygood results.

A Diversified Multi-Start Algorithm for Unconstrained Binary Quadratic Problems Leveraging the Graphics Processor Unit