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10.03.2020 | Methodologies and Application

Red deer algorithm (RDA): a new nature-inspired meta-heuristic

verfasst von: Amir Mohammad Fathollahi-Fard, Mostafa Hajiaghaei-Keshteli, Reza Tavakkoli-Moghaddam

Erschienen in: Soft Computing | Ausgabe 19/2020

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Abstract

Nature has been considered as an inspiration of several recent meta-heuristic algorithms. This paper firstly studies and mimics the behavior of Scottish red deer in order to develop a new nature-inspired algorithm. The main inspiration of this meta-heuristic algorithm is to originate from an unusual mating behavior of Scottish red deer in a breading season. Similar to other population-based meta-heuristics, the red deer algorithm (RDA) starts with an initial population called red deers (RDs). They are divided into two types: hinds and male RDs. Besides, a harem is a group of female RDs. The general steps of this evolutionary algorithm are considered by the competition of male RDs to get the harem with more hinds via roaring and fighting behaviors. By solving 12 benchmark functions and important engineering as well as multi-objective optimization problems, the superiority of the proposed RDA shows in comparison with other well-known and recent meta-heuristics.

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Vorheriger Artikel Stability analysis of dynamic nonlinear interval type-2 TSK fuzzy control systems based on describing function

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Titel: Red deer algorithm (RDA): a new nature-inspired meta-heuristic
verfasst von: Amir Mohammad Fathollahi-Fard
Mostafa Hajiaghaei-Keshteli
Reza Tavakkoli-Moghaddam
Publikationsdatum: 10.03.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 19/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-020-04812-z

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