Parallel Multi-Objective Genetic Algorithm GPU Accelerated Asynchronously Distributed NSGA II
Oliver Rice, Robert E. Smith, Rickard Nyman · 2013
Multi-objective optimization problems consist of numerous, often conflicting, criteria for which any solution existing on the Pareto front of criterion trade-offs is considered optimal. In this paper we present a general-purpose algorithm designed for solving multi-objective prob- lems (MOPS) on graphics processing units (GPUs). Specifically, a purely asynchronous multi-populous genetic algorithm is introduced. While this algorithm is designed to maximally utilize consumer grade nVidia GPUs, it is feasible to implement on any parallel hardware. The GPU's mas- sively parallel architecture and low latency memory result in +125 times speed-up for proposed parametrization relative to single threaded CPU implementations. The algorithm, NSGA-AD, consistently solves for so- lution sets of better or equivalent quality to state-of-the-art methods.