A Diversified Multi-Start Algorithm for Unconstrained Binary Quadratic Problems Leveraging the Graphics Processor Unit
This work provides an incremental improvement for researchers and practitioners in optimization by accelerating metaheuristic searches for binary quadratic problems.
The paper tackled the Unconstrained Binary Quadratic Optimization Problem by using GPU parallel processing to generate diverse starting solutions with screening, resulting in very good results quickly compared to best-known approaches on benchmark problems.
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.