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Evolutionary Intelligence
Erschienen in: Evolutionary Intelligence 2/2019

25.02.2019 | Research Paper

Emperor Penguins Colony: a new metaheuristic algorithm for optimization

verfasst von: Sasan Harifi, Madjid Khalilian, Javad Mohammadzadeh, Sadoullah Ebrahimnejad

Erschienen in: Evolutionary Intelligence | Ausgabe 2/2019

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Abstract

A metaheuristic is a high-level problem independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms. Metaheuristic algorithms attempt to find the best solution out of all possible solutions of an optimization problem. A very active area of research is the design of nature-inspired metaheuristics. Nature acts as a source of concepts, mechanisms and principles for designing of artificial computing systems to deal with complex computational problems. In this paper, a new metaheuristic algorithm, inspired by the behavior of emperor penguins which is called Emperor Penguins Colony (EPC), is proposed. This algorithm is controlled by the body heat radiation of the penguins and their spiral-like movement in their colony. The proposed algorithm is compared with eight developed metaheuristic algorithms. Ten benchmark test functions are applied to all algorithms. The results of the experiments to find the optimal result, show that the proposed algorithm is better than other metaheuristic algorithms.
Vorheriger Artikel Cross domain association using transfer subspace learning
Nächster Artikel Integer-PSO: a discrete PSO algorithm for task scheduling in cloud computing systems

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Metadaten
Titel
Emperor Penguins Colony: a new metaheuristic algorithm for optimization
verfasst von
Sasan Harifi
Madjid Khalilian
Javad Mohammadzadeh
Sadoullah Ebrahimnejad
Publikationsdatum
25.02.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Evolutionary Intelligence / Ausgabe 2/2019
Print ISSN: 1864-5909
Elektronische ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-019-00212-x

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