Comparing Large-Scale Global Optimization Competition winners in a real-world problem

Molina, Daniel and Nesterenko, Arthur R. and Latorre De La Fuente, Antonio (2019). Comparing Large-Scale Global Optimization Competition winners in a real-world problem. In: "IEEE Congress on Evolutionary Computation (CEC 2019)", 10-13 Jun 2019, Wellington, Nueva Zelanda. pp. 359-365.


Title: Comparing Large-Scale Global Optimization Competition winners in a real-world problem
  • Molina, Daniel
  • Nesterenko, Arthur R.
  • Latorre De La Fuente, Antonio
Item Type: Presentation at Congress or Conference (Article)
Event Title: IEEE Congress on Evolutionary Computation (CEC 2019)
Event Dates: 10-13 Jun 2019
Event Location: Wellington, Nueva Zelanda
Title of Book: 2019 IEEE Congress on Evolutionary Computation (CEC)
Date: 2019
Freetext Keywords: Large-scale global optimization, LSGO, Bench-marking, BComp
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Arquitectura y Tecnología de Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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The optimization of thousands of variables, Large-Scale Global Optimization, is a research topic that is obtainingmore and more attention by its applications in engineering andmedical problems. In order to design evolutionary algorithms forthese problems, several specific competitions have been organized,using benchmarks such as the ones proposed in CEC’2010 andCEC’2013, trying to simulate realistic features of real-worldproblems. Several algorithms have been proposed, some of thembeing very competitive on these benchmarks, especially duringthe last years. However, all of them were tested only on thoseartificial benchmarks, so there are no guarantees that theywould obtain good performance in more realistic problems. Inthis paper, we select the best algorithms in these competitionsto optimize a real-world problem, an electroencephalography(EEG) optimization problem. The new benchmark contains noisyproblems and an increasing number of variables (up to 5000)compared to synthetic benchmarks (limited to 1000 variables).Results show that, although thefitness obtained by the majorityof the algorithms is the same, the processing time stronglydepends on the algorithm under consideration. The optimizationtime for afixed number offitness evaluations varies, in themost complex problems, from 3 hours to around 18 minutes,being MOS-2013 the fastest algorithm. However, if we focus ourattention on the time needed to reach the best-known solution,SHADEILS becomes the fastest algorithm (with a maximum ofthree minutes). In our opinion, this should encourage researchersto continue working in more scalable and efficient algorithms forlarge-scale global optimization.

Funding Projects

Government of SpainTIN2014-57481-C2-2-RUnspecifiedUniversidad Politécnica de MadridNuevos avances en visualización analítica
Government of SpainTIN2016-8113-RUnspecifiedUnspecifiedDesde el procesamiento de series temporales en BigData hasta el mantenimiento inteligente del ferrocarril
Government of SpainTIN2017-83132-C2-2-RUnspecifiedUniversidad Politécnica de MadridVisualización analítica aplicada
Government of SpainTIN2017-89517-PUnspecifiedUniversidad de GranadaSMART-DASCI: Modelos de ciencia de datos e inteligencia computacional: tendiendo el puente entre Big Data y Smart Data
Universidad Politécnica de MadridPINV-18-XEOGHQ-19-4QTEBPUnspecifiedUnspecifiedUnspecified

More information

Item ID: 67063
DC Identifier:
OAI Identifier:
DOI: 10.1109/CEC.2019.8789943
Official URL:
Deposited by: Memoria Investigacion
Deposited on: 13 May 2021 09:53
Last Modified: 17 May 2021 05:50
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