Black-box optimization benchmarking for noiseless function testbed using artificial bee colony algorithm

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Scopus citations

Abstract

This paper benchmarks the Artificial Bee Colony (ABC) algorithm using the noise-free BBOB 2010 testbed. The results show how this algorithm is highly successful in the separable and weak structured functions.

Original languageEnglish
Title of host publicationProceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication
Pages1719-1724
Number of pages6
DOIs
StatePublished - 2010
Event12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010 - Portland, OR, United States
Duration: 7 Jul 201011 Jul 2010

Publication series

NameProceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication

Conference

Conference12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010
Country/TerritoryUnited States
CityPortland, OR
Period7/07/1011/07/10

Keywords

  • Benchmarking
  • Black-box optimization

Fingerprint

Dive into the research topics of 'Black-box optimization benchmarking for noiseless function testbed using artificial bee colony algorithm'. Together they form a unique fingerprint.

Cite this