Rummaging conduct of social animals has consistently involved investigation for the improvement of enhancement calculations. Creepy crawly Monkey Optimization is a worldwide streamlining calculation motivated by Fission-Fusion social development of insect monkey throughout their looking out conduct. Spider Monkey Optimization stunningly portrays two crucial ideas of multitude insight: self-association and division of work. Spider Monkey Optimization has acquired notoriety as of late as a large number perception based mostly calculation and is utilized to many more designing streamlining points. In this part it presents the Spider Monkey Optimization calculation intimately. A mathematical illustration of Spider Monkey Optimization methodology has moreover given for a superior comprehension of its functioning.
Topics
Bioacoustics of mammals
REFERENCES
1.
S. I.
Ayon
, M. A. H.
Akhand
, S. A.
Shahriyar
, and N.
Siddique
, “Spider Monkey Optimization to Solve Traveling Salesman Problem
,” 2nd Int. Conf. Electr. Comput. Commun. Eng. ECCE 2019, Apr.
2019
, doi: .2.
J. C.
Bansal
, H.
Sharma
, S. S.
Jadon
, and M.
Clerc
, “Spider Monkey Optimization algorithm for numerical optimization
,” Memetic Comput.
, Vol. 6
, No. 1
, pp. 31
–47
, 2014
, doi: .3.
M.
Clerc
and M.
Clerc Standard
, “Standard Particle Swarm Optimisation
,” Accessed: Aug. 30
, 2021
. [Online]. Available: https://hal.archives-ouvertes.fr/hal-00764996.4.
K.
Gupta
, K.
Deep
, and J. C.
Bansal
, “Improving the Local Search Ability of Spider Monkey Optimization Algorithm Using Quadratic Approximation for Unconstrained Optimization
,” Comput. Intell.
, Vol. 33
, No. 2
, pp. 210
–240
, 2017
, doi: .5.
A.
Agrawal
, P.
Farswan
, V.
Agrawal
, D. C.
Tiwari
, and J. C.
Bansal
, “On the hybridization of spider monkey optimization and genetic algorithms
,” Adv. Intell. Syst. Comput.
, Vol. 546
, pp. 185
–196
, 2017
, doi: .6.
H. J.
Liao
, C. H.
Richard Lin
, Y. C.
Lin
, and K. Y.
Tung
, “Intrusion detection system: A comprehensive review
,” J. Netw. Comput. Appl.
, Vol. 36
, No. 1
, pp. 16
–24
, Jan. 2013
, doi: .7.
P. M.
, “Bacterial Foraging Optimization
,” Int. J. Swarm Intell. Res.
, Vol. 1
, No. 1
, pp. 1
–16
, Jan. 2010
, doi: .8.
“An Idea Based on Honey Bee Swarm for Numerical Optimization, Technical Report - TR06 | Request PDF.” https://www.researchgate.net/publication/255638348_An_Idea_Based_on_Honey_Bee_Swarm_for_Numerical_Optimization_Technical_Report_-_TR06 (accessed Aug. 30, 2021).
9.
D. S.
Kumar
and R.
Poonia
, “Self-Adaptive Spider Monkey Optimization Algorithm for Engineering Optimization Problems.” Accessed: Aug. 30, 2021. [Online]. Available: https://www.academia.edu/11564826/Self_Adaptive_Spider_Monkey_Optimization_Algorithm_for_Engineering_Optimization_Problems.10.
M.
Ehteram
, H.
Karami
, and S.
Farzin
, “Reducing Irrigation Deficiencies Based Optimizing Model for Multi-Reservoir Systems Utilizing Spider Monkey Algorithm
,” Water Resour. Manag.
, Vol. 32
, No. 7
, pp. 2315
–2334
, May 2018
, doi: .11.
C.
ID
and L.
ME
, “Fission-fusion populations
,” Curr. Biol.
, Vol. 19
, No. 15, Aug
. 2009
, doi: .12.
A. L.
Baden
, T. H.
Webster
, and J. M.
Kamilar
, “Resource seasonality and reproduction predict fission-fusion dynamics in black-and-white ruffed lemurs (Varecia variegata
),” Am. J. Primatol.
, Vol. 78
, No. 2
, pp. 256
–279
, Feb. 2016
, doi: .13.
D. N, G.
DA
, K.
AS
, and G. N, “Facial expressions of emotion states and their neuronal correlates in mice
,” Science
, Vol. 368
, No. 6486, Apr
. 2020
, doi: .14.
J.
Chand
et al, “Spider Monkey Optimization algorithm for numerical optimization
,” vol. 6
, pp. 31
–47
, 2014
, doi: .15.
N.A.
Singh
, V.
Bhardwaj
, C.
Ravi
, N.
Ramesh
, A.K.A.
Mandal
, and Z.A.
Khan
, EGCG nanoparticles attenuate aluminum chloride induced neurobehavioral deficits, beta amyloid and tau pathology in a rat model of Alzheimer’s disease
, Front. Aging Neurosci.
10
, (2018
).16.
S.
Garg
, K.
Kaur
, S.
Batra
, G.S.
Aujla
, G.
Morgan
, N.
Kumar
, A.Y.
Zomaya
, and R.
Ranjan
, En-ABC: An ensemble artificial bee colony based anomaly detection scheme for cloud environment
, J. Parallel Distrib. Comput.
135
, 219
(2020
).17.
H.
Cao
, S.
Wu
, G.S.
Aujla
, Q.
Wang
, L.
Yang
, and H.
Zhu
, Dynamic Embedding and Quality of Service-Driven Adjustment for Cloud Networks
, IEEE Trans. Ind. Informatics
16
, 1406
(2020
)18.
N.
Mittal
, U.
Singh
, R.
Salgotra
, and B.S.
Sohi
, A boolean spider monkey optimization based energy efficient clustering approach for WSNs
, Wirel. Networks
24
, 2093
(2018
)
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