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BENCHMARKING ANALYSIS FOR BIOLOGICAL-BASED METAHEURISTICS

MAY 2024   -  Volume: 99 -  Pages: 296-302

DOI:

https://doi.org/10.6036/11070

Authors:

ENOL GARCÍA GONZÁLEZ
-
JOSÉ RAMÓN VILLAR FLECHA
-
JAVIER SEDANO FRANCO
-
CAMELIA CHIRA

Disciplines:

  • Computer Sciences (ARTIFICIAL INTELLIGENCE / INTELIGENCIA ARTIFICIAL )
  • INFORMATION TECHNOLOGY AND KNOWLEDGE (OPTIMIZACION Y CONTROL )
  • INFORMATION TECHNOLOGY AND KNOWLEDGE (INTELIGENCIA ARTIFICIAL Y SIMULACION )

Downloads:   8

How to cite this paper:  

Received Date :   27 July 2023

Reviewing Date :   27 July 2023

Accepted Date :   6 November 2023

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Key words:
Metaheurísticas, benchmark, problemas de optimización, metaheurísticas inspiradas en la biología, Metaheurístics, benchmark, optimization problems, biological-based metaheuristics
Article type:
ARTICULO DE INVESTIGACION / RESEARCH ARTICLE
Section:
RESEARCH ARTICLES

This article reviews the metaheuristics published in the literature, emphasizing their usefulness in solving complex optimization problems. The review highlights inspiration's relevance in the metaheuristics design, being the main classification in multiple taxonomies existing in the literature.
After reviewing the state of the art, six of the most relevant metaheuristics used to solve problems of various types (engineering, logistics, economics, data science, ...) were selected. This selection of metaheuristics will be subjected to an analysis of their performance using a set of problems selected from different authors. The problems selected for this analysis include problems with a single minimum or multiple minima, different sizes in terms of dimensions, and different types of mathematical functions such as polynomial, trigonometric, or exponential. The analysis offers a discussion of which scenarios are the best for each metaheuristic, analyzing aspects such as the ability of metaheuristics to explore and escape local minima. The article concludes by summarizing which metaheuristic is best for each type of problem.

Keywords: Metaheuristics, benchmark, optimization problems, biological-based metaheuristics

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