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Hierarchical parallel model for improving performance on differential evolution

Authors: María Laura Tardivo; Paola Caymes‐Scutari; Germán Bianchini; Miguel Méndez‐Garabetti;

Hierarchical parallel model for improving performance on differential evolution

Abstract

SummaryThis paper presents a parallel distributed model for the Differential Evolution algorithm. The proposed model, Hierarchical Island‐Based Model for Differential Evolution, follows a double‐hierarchy master‐worker scheme and offers two parallelism levels. In this proposal, the processes are associated with certain cooperation hierarchy, allowing them to explore in a more comprehensive way the search space of the problem at hand. A comparative study with other algorithms from the state of art is also presented. The results show that Hierachical Island‐Based Model for Differential Evolution is a flexible model and achieves good performance in terms of results quality and computing time.

Keywords

COMPARATIVE STUDY, ISLAND MODEL, https://purl.org/becyt/ford/1.2, https://purl.org/becyt/ford/1, HIGH PERFORMANCE COMPUTING, DIFFERENTIAL EVOLUTION, DOUBLE HIERARCHY

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Top 10%
Average
Top 10%
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