Parallel Evolutionary Algorithms Performing Pairwise Comparisons
Résumé
We study mathematically and experimentally the conver-gence rate of differential evolution and particle swarm opti-mization for simple unimodal functions. Due to paralleliza-tion concerns, the focus is on lower bounds on the runtime, i.e upper bounds on the speed-up, as a function of the pop-ulation size. Two cases are particularly relevant: A popula-tion size of the same order of magnitude as the dimension and larger population sizes. We use the branching factor as a tool for proving bounds and get, as upper bounds, a lin-ear speed-up for a population size similar to the dimension, and a logarithmic speed-up for larger population sizes. We then propose parametrizations for differential evolution and particle swarm optimization that reach these bounds.
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