Slime mould algorithm: A new method for stochastic optimization

Shimin Li; Huiling Chen; Mingjing Wang; Ali Asghar Heidari; Seyedali Mirjalili

doi:10.1016/j.future.2020.03.055

journal article Oct 01, 2020

Slime mould algorithm: A new method for stochastic optimization

Shimin Li Huiling Chen

Mingjing Wang Ali Asghar Heidari Seyedali Mirjalili

Future Generation Computer Systems Vol. 111 pp. 300-323 · Elsevier BV

View at Publisher Save 10.1016/j.future.2020.03.055

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References

92

[1]

Chen "An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models" Energy Convers. Manage. (2019) 10.1016/j.enconman.2019.05.057

[2]

Chen "A balanced whale optimization algorithm for constrained engineering design problems" Appl. Math. Model. (2019) 10.1016/j.apm.2019.02.004

[3]

Wang "Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis" Appl. Soft Comput. (2019)

[4]

Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses

Mingjing Wang, Huiling Chen, Bo Yang et al.

Neurocomputing 2017 10.1016/j.neucom.2017.04.060

[5]

Osher (2018)

[6]

Mirjalili "Multi-verse optimizer: a nature-inspired algorithm for global optimization" Neural Comput. Appl. (2016) 10.1007/s00521-015-1870-7

[7]

Kaveh "A novel heuristic optimization method: Charged system search" Acta Mech. (2010) 10.1007/s00707-009-0270-4

[8]

Rashedi (2009)

[9]

SCA: A Sine Cosine Algorithm for solving optimization problems

Seyedali Mirjalili

Knowledge-Based Systems 2016 10.1016/j.knosys.2015.12.022

[10]

Venkata Rao (2012)

[11]

Formato (2007)

[12]

Fogel (2009)

[13]

Booker "Classifier systems and genetic algorithms" Artificial Intelligence (1989) 10.1016/0004-3702(89)90050-7

[14]

J.R. Koza, J.P. Rice, Automatic programming of robots using genetic programming, in: Proceedings Tenth National Conference on Artificial Intelligence, 1992.

[15]

Hansen (2003)

[16]

Yao (1999)

[17]

Storn (1997)

[18]

Qiao "Nature-inspired hybrid techniques of IWO, DA, ES, GA, and ICA, validated through a k-fold validation process predicting monthly natural gas consumption" Energy Build. (2020) 10.1016/j.enbuild.2020.110023

[19]

Moayedi "Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods" Appl. Soft. Comput. (2018) 10.1016/j.asoc.2018.02.027

[20]

Moayedi "An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand" Neural Comput. Appl. (2019) 10.1007/s00521-017-2990-z

[21]

Beni (1993)

[22]

J. Kennedy, R. Eberhart, Particle swarm optimization, in: IEEE International Conference on Neural Networks - Conference Proceedings, 1995.

[23]

Yang (2010)

[24]

Mirjalili "Grey wolf optimizer" Adv. Eng. Softw. (2014) 10.1016/j.advengsoft.2013.12.007

[25]

Pan "A new fruit fly optimization algorithm: Taking the financial distress model as an example" Knowl.-Based Syst. (2012) 10.1016/j.knosys.2011.07.001

[26]

Mirjalili "Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm" Knowl.-Based Syst. (2015) 10.1016/j.knosys.2015.07.006

[27]

Dorigo "Ant colony optimization theory: A survey" Theoret. Comput. Sci. (2005) 10.1016/j.tcs.2005.05.020

[28]

Harris hawks optimization: Algorithm and applications

Ali Asghar Heidari, Seyedali Mirjalili, Hossam Faris et al.

Future Generation Computer Systems 2019 10.1016/j.future.2019.02.028

[29]

Karaboga "A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm" J. Global Optim. (2007) 10.1007/s10898-007-9149-x

[30]

Lin (2009)

[31]

Wolpert (1997)

[32]

Monismith (2008)

[33]

Li (2011)

[34]

Qian (2013)

[35]

Schmickl (2006)

[36]

Becker (2015)

[37]

Brabazon Int. J. Innovative Comput. Appl. (2020) 10.1504/ijica.2020.105316

[38]

Howard (1931)

[39]

Kessler (1982)

[40]

Camp (1936)

[41]

Kamiya (1940)

[42]

Nakagaki (2000)

[43]

Becker "On the efficiency of nature-inspired algorithms for generation of fault-tolerant graphs" (2016)

[44]

Šešum Čavić "Bio-inspired search algorithms for unstructured P2P overlay networks" Swarm Evol. Comput. (2016) 10.1016/j.swevo.2016.03.002

[45]

Daniel Yu "Bicycle pathway generation through a weighted digital slime mold algorithm via topographical analysis" (2018)

[46]

Beekman "Brainless but multi-headed: Decision making by the acellular slime mould physarum polycephalum" J. Mol. Biol. (2015) 10.1016/j.jmb.2015.07.007

[47]

Latty (2010)

[48]

Latty (2011)

[49]

Latty (2015)

[50]

Kareiva (1987)

Showing 50 of 92 references

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Details

Published: Oct 01, 2020
Vol/Issue: 111
Pages: 300-323
License: View

Authors

Obuda University

Funding

National Natural Science Foundation of China Award: U1809209

Science and Technology Plan Project of Wenzhou, China Award: 2018ZG012

Cite This Article

Shimin Li, Huiling Chen, Mingjing Wang, et al. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300-323. https://doi.org/10.1016/j.future.2020.03.055

Slime mould algorithm: A new method for stochastic optimization

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