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Conference Papers Year : 2015

Parallel Evolutionary Algorithms Performing Pairwise Comparisons

Abstract

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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Dates and versions

hal-01077626 , version 1 (03-11-2014)

Identifiers

  • HAL Id : hal-01077626 , version 1

Cite

Marie-Liesse Cauwet, Olivier Teytaud, Shih-Yuan Chiu, Kuo-Min Lin, Shi-Jim Yen, et al.. Parallel Evolutionary Algorithms Performing Pairwise Comparisons. Foundations of Genetic Algorithms, 2015, Aberythswyth, United Kingdom. pp.99-113. ⟨hal-01077626⟩
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