Abstract
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 problems (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 massively 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 solution sets of better or equivalent quality to state-of-the-art methods.
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Rice, O., Smith, R.E., Nyman, R. (2013). Parallel Multi-Objective Genetic Algorithm. In: Dediu, AH., Martín-Vide, C., Truthe, B., Vega-Rodríguez, M.A. (eds) Theory and Practice of Natural Computing. TPNC 2013. Lecture Notes in Computer Science, vol 8273. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45008-2_18
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DOI: https://doi.org/10.1007/978-3-642-45008-2_18
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