Scalability Analysis of the Asynchronous, Master-Slave Borg Multiobjective Evolutionary Algorithm

The Borg Multiobjective Evolutionary Algorithm (MOEA) is a new, efficient, and robust optimizer that outperforms competing optimization methods on numerous complex engineering problems. To date, the Borg MOEA has been successfully applied to problems ranging from aerospace applications to water resources engineering. Problems from these domains often involve expensive design evaluations that require large-scale parallel algorithms to produce results in a reasonable amount of time. This study presents the first theoretical and experimental look at parallelizing the Borg MOEA. First, we derive theoretical models for predicting speedup, efficiency, and processor count lower and upper bounds. Second, we validate these models on a simple problem, DTLZ2, and a harder, non-separable problem, UF11. Third, we examine the effects of scaling on convergence speed and solution quality. These experiments are performed on the 62, 976 core Texas Advanced Computing Center (TACC) Ranger system.