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Memetic Computing
Erschienen in: Memetic Computing 1/2014

01.03.2014 | Regular research paper

Spider Monkey Optimization algorithm for numerical optimization

verfasst von: Jagdish Chand Bansal, Harish Sharma, Shimpi Singh Jadon, Maurice Clerc

Erschienen in: Memetic Computing | Ausgabe 1/2014

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Abstract

Swarm intelligence is one of the most promising area for the researchers in the field of numerical optimization. Researchers have developed many algorithms by simulating the swarming behavior of various creatures like ants, honey bees, fish, birds and the findings are very motivating. In this paper, a new approach for numerical optimization is proposed by modeling the foraging behavior of spider monkeys. Spider monkeys have been categorized as fission–fusion social structure based animals. The animals which follow fission–fusion social systems, split themselves from large to smaller groups and vice-versa based on the scarcity or availability of food. The proposed swarm intelligence approach is named as Spider Monkey Optimization (SMO) algorithm and can broadly be classified as an algorithm inspired by intelligent foraging behavior of fission–fusion social structure based animals.
Vorheriger Artikel A bare-bones ant colony optimization algorithm that performs competitively on the sequential ordering problem
Nächster Artikel Fuzzy optimization approach to component selection of fault-tolerant software system

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Metadaten
Titel
Spider Monkey Optimization algorithm for numerical optimization
verfasst von
Jagdish Chand Bansal
Harish Sharma
Shimpi Singh Jadon
Maurice Clerc
Publikationsdatum
01.03.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Memetic Computing / Ausgabe 1/2014
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-013-0128-0

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