MRPSO: MapReduce particle swarm optimization

Authors:
Andrew W. McNabb;Christopher K. Monson;Kevin D. Seppi
Affiliations:
Brigham Young University, Provo, UT;Brigham Young University, Provo, UT;Brigham Young University, Provo, UT
Venue:
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Year:
2007

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Abstract

In optimization problems involving large amounts of data, Particle Swarm Optimization (PSO) must be parallelized because individual function evaluations may take minutes or even hours. However, large-scale parallelization is difficult because programs must communicate efficiently, balance workloads and tolerate node failures. To address these issues, we present Map Reduce Particle Swarm Optimization(MRPSO), a PSO implementation based on Google's Map Reduce parallel programming model.