Evolving many‐objective water management to exploit exascale computing

Abstract

AbstractThis study contributes one of the largest parallel scalability experiments ever attempted within the water resources literature to date, encompassing 2000 years of computational time. A severely challenging multiobjective benchmark problem focusing on urban water portfolio planning under uncertainty in the Lower Rio Grande Valley (LRGV) is used to demonstrate that a multimaster variant of the Borg multiobjective evolutionary algorithm (MOEA) can be used efficiently on more than 524,288 compute cores. The scalability of the multimaster Borg MOEA enables users to compress up to 20 years of computational work into 20 min of actual wall‐clock time. Beyond these temporal efficiency gains, metric‐based statistical assessments of solution quality show that the multimaster Borg MOEA dramatically enhances the effectiveness and reliability of the algorithm's autoadaptive search features. Theoretical algorithmic analysis shows that the multimaster Borg MOEA could maintain high levels of parallel scalability on future exascale computing platforms (i.e., millions of compute cores). These results mark a fundamental expansion of the scope, computational demands, and difficulties that can be addressed in multiobjective water resources applications.