fcampelo / ec-bestiary Goto Github PK

I came across another metaphor based method "butterfly optimization
algorithm" which I think can be added to the list. I am aware that "Monarch
butterfly optimization" is also in the list and inspired from butterfly
behaviour, however, these are different algorithms (Like different 'novel'
fish algorithms. Rest it's up to you. I just want to let you know.

Link to Butterfly Optimization Algorithm can be found here (
https://link.springer.com/article/10.1007/s00500-018-3102-4)

Dr. Rahul Roy

Maybe this is of any interest:

https://ieeexplore.ieee.org/document/7482120

Kind regards
Tony Wauters

Category: Birds > Feeding

Reference :
Lamy JB. Artificial Feeding Birds (AFB): a new metaheuristic inspired by the behavior of pigeons. Advances in nature-inspired computing and applications 2019;43-60, Springer
http://www.lesfleursdunormal.fr/_downloads/article_afb_2018.pdf

From
Jean-Baptiste Lamy, Université Paris (paper author)

The Golden Ball Algorithm: http://dl.acm.org/citation.cfm?id=2636011

Gotta check what exact metaphor that is...

http://www.worldscientific.com/doi/abs/10.1142/S021962201450031X

By Eduardo Hauck

Kaizen Japanese methodology:
http://dl.acm.org/citation.cfm?id=2598264

Sand piles:
https://books.google.pt/books?id=GDz2nCAJpJ4C&pg=PA378&lpg=PA378&dq=sand+pile+mutation&source=bl&ots=3V0C2kbO6Y&sig=GNGWFlJum1u4ingo3Fd9jA_uy2M&hl=en&sa=X&ved=0ahUKEwjQwJSXgMbTAhUFOxQKHeqkDC0Q6AEISzAK#v=onepage&q=sand%20pile%20mutation&f=false

Algae:
S.A. Uymaz, G. Tezel, E.Yel, Artificial Algae Algorithm (AAA) For Nonlinear Global Optimization, Applied Soft Computing, Volume 31, June 2015, Pages 153-171.

https://link.springer.com/chapter/10.1007%2F11536444_12

@inproceedings{greensmith2005introducing,
title={Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection},
author={Greensmith, Julie and Aickelin, Uwe and Cayzer, Steve},
booktitle={International Conference on Artificial Immune Systems},
pages={153--167},
year={2005},
organization={Springer}
}

I am reporting some missing algorithms from bestiary:

1. Golden ball algorithm (GB)
   E. Osaba, F. Diaz, and E. Onieva. Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Applied Intelligence, 41(1):145–166, 2014

2. Bison Algorithm
   Kazikova, A., Pluhacek, M., Senkerik R., Viktorin, A.: Proposal of a new swarm optimization method inspired in Bison behavior. In: Matousek, R. (ed.) Recent Advances in Soft Computing (Mendel 2017). Advances in Intelligent Systems and Computing. Springer, Heidelberg (2017, in press)
   Anezka Kazikova, Michal Pluhacek, Adam Viktorin, Roman Senkerik, New Running Technique for the Bison Algorithm, International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018: Artificial Intelligence and Soft Computing pp 417-426, 2018

Yours faithfully,
Krystian Łapa

1. Migrating birds

Duman et al., Migrating Birds Optimization: A new metaheuristic approach and its
performance on quadratic assignment problem, http://dx.doi.org/10.1016/j.ins.
2012.06.032

2. Hunting search

Naderi, etal. Mathematical models and a hunting search algorithm for the no-wait
flowshop scheduling with parallel machines, Int. J. Prod. Res., 52(9), 2014, http://
dx.doi.org/10.1080/00207543.2013.871389

3. African wild dogs

C. Subramanian , A.S.S. Sekar and K. Subramanian, "A New Engineering
Optimization Method: African Wild Dog Algorithm", 10.3923/ijscomp.2013.163.170,
International Journal of Soft Computing 8(3), 2013, http://medwelljournals.com/abstract/?
doi=ijscomp.2013.163.170, (DOI http://dx.doi.org/10.3923/ijscomp.2013.163.170 does not
seem to work)

4. Ant-inspired bacterial foraging

Ravibabu, "A novel metaheuristics to solve mixed shop scheduling problems", Int. J. in
Found. Comp. Sci. & Techn., 3(2), 2013, http://wireilla.com/papers/ijfcst/
V3N2/3213ijfcst04.pdf (DOI dx.doi.org/10.5121/ijfcst.2013.3204 does not seem to work)

5. Chaotic local search based bacterial foraging

A chaotic local search based bacterial foraging algorithm and its application to a
permutation flow-shop scheduling problem, INt. J. Comp. Integr. Manuf., 29(9), 2016,
http://dx.doi.org/10.1080/0951192X.2015.1130240

• Human seekers: https://doi.org/10.1109/TIE.2009.2031194
• Particle collisions: "A New Stochastic Optimization Algorithm based on a Particle Collision Metaheuristic"
• Molecular dynamics: https://doi.org/10.1016/j.knosys.2018.08.030
• Selfish herds (!): http://dx.doi.org/10.1016/j.biosystems.2017.07.010
• Mouth Brooding Fish: https://doi.org/10.1016/j.asoc.2017.09.035
• Saplings Growing Up: "Thinking Capability of Saplings Growing Up Algorithm"
• Bees Collecting Pollen: "A Novel Global Convergence Algorithm: Bee Collecting Pollen Algorithm"
• Yellow Saddle Goldfish: https://doi.org/10.1016/j.biosystems.2018.09.007
• Galatic Swarms: http://dx.doi.org/10.1016/j.asoc.2015.10.034

Andrea Serani, Dolphin Pod Optimization, ICSI 2017 -- by Self Recommendation.

• Check for Eligibility
• Check for Duplicates
• Add to Bestiary

Teaching–learning-based optimization
http://dx.doi.org/10.1016/j.cad.2010.12.015

Crisscross optimization algorithm
http://dx.doi.org/10.1016/j.knosys.2014.05.004

Binary Bat Algorithm
http://link.springer.com/article/10.1007%2Fs00521-013-1525-5
http://dx.doi.org/10.1007/s00521-013-1525-5

Plant intelligence (really...)

http://link.springer.com/article/10.1007/s10462-016-9486-6

Camel algorithm.

http://ijeee.org/volums/volume12/IJEEE12PDF/paper1218.pdf

it would be great to have similar for code

Fabio sent a contribution to the bestiary! All the usual steps

• check for suitability
• check for duplication and date
• add to the bestiary

Olague G., Puente C. (2006) The Honeybee Search Algorithm for Three-Dimensional Reconstruction. In: Rothlauf F. et al. (eds) Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg

Here’s the publication link:
http://link.springer.com/chapter/10.1007%2F11732242_38 http://link.springer.com/chapter/10.1007/11732242_38

Fabio Daolio
University of Stirling
Computing Science and Mathematics

1 - Laying chickens (contributed by Marc Sevaux)

Hosseini E (2017) Laying Chicken Algorithm: A New Meta-Heuristic Approach to Solve Continuous Programming Problems. J Appl Computat Math 6:344. doi: 10.4172/2168-9679.1000344

https://www.omicsonline.org/open-access/laying-chicken-algorithm-a-new-metaheuristic-approach-to-solve-continuous-programming-problems-2168-9679-1000344.php?aid=86925

• Check inclusion criteria
• Add to cage

• Thermal exchange optimization: http://www.sciencedirect.com/science/article/pii/S0965997817300066
• Vibrating particles system: http://doi.org/10.1007/s00707-016-1725-z
• Tug-of-war: A. Kaveh, A. Zolghadr "A novel meta-heuristic algorithm: tug of war optimization" - Asian J Civil Eng, 6 (4) (2016), pp. 469–492

Mine blast algorithm for optimization of truss structures with discrete variable

https://doi.org/10.1109/CEC.2014.6900366

Carlos Fonseca

• Immune systems (AIS)
• Chromosomes (GA)
• Metals cooling down (SA)

Entry repeated twice

States of Matter: Cuevas E, Echavarr'\ia A and Ram'\irez-Ortegón MA (2013). “An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation.” Applied Intelligence, 40(2), pp. 256-272. doi: 10.1007/s10489-013-0458-0

States of Matter: Cuevas E, Echavarr'\ia A and Ram'\irez-Ortegón MA (2013). “An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation.” Applied Intelligence, 40(2), pp. 256-272. doi: 10.1007/s10489-013-0458-0

Iztok Fister suggested that the following survey paper may contain heuristics not listed in the bestiary:

http://www.iztok-jr-fister.eu/static/publications/21.pdf

Check the paper for any eligible heuristics to add to the Zoo.

Suggested by Joaquin Antonio Pacheco, from ubu.es

• Grasshopper Optimisation
  Shahrzad Saremi, Seyedali Mirjalili, and Andrew Lewis "Grasshopper Optimisation Algorithm: Theory and application" Advances in Engineering Software, Volume 105, March 2017, Pages 30-47

• Sin-Cosine
  Seyedali Mirjalili "SCA: A Sine Cosine Algorithm for solving optimization problems"
  Knowledge-Based Systems, Volume 96, 15 March 2016, Pages 120-133

Tasks:

• [ ] Check the above papers for duplicates in the bestiary
• [ ] Check for the eligibility criteria (Metaphor, In Press)
• [ ] Add to the bestiary

Checked on 2018-03-31 by Felipe. Not added (Grasshopper already in, Sine-Cosine not a metaphor (dubious science, but not metaphorically so)

Marc Sevaux suggested the following papers to be added to the Bestiary.

• Check for eligibility
• Check for duplicates
• Add to bestiary

Sperm Whale Algorithm
A. Ebrahimi, E. Khamehchi, “Sperm Whale Algorithm: an Effective Metaheuristic Algorithm for Production Optimization Problems.” Journal of Natural Gas Science & Engineering (2016), http://dx.doi.org/10.1016/j.jngse.2016.01.001

Virus colony Search
M. D. Li, H. Zhao, X. W. Weng, T. Han, “A novel nature-inspired algorithm for optimization: Virus colony search.” Advances in Engineering Software, 92, (2016), 65–88.

Sine Cosine Algorithm
S. Mirjalili, “SCA: A Sine Cosine Algorithm for Solving Optimization Problems.” Knowledge-Based Systems, (2016), http://dx.doi.org/10.1016/j.knosys.2015.12.022

Multiverse optimizer
S. Mirjalili, S. M. Mirjalili, A. Hatamlou, “Multi-Verse Optimizer: a nature-inspired algorithm for global optimization.” Neural Computing & Applications, (2015), 1-19. http://dx.doi.org/10.1007/s00521-015-1870-7

Exchange Market Algorithm
N. Ghorbani, E. Babaei, “Exchange market algorithm.” Applied Soft Computing, 19, (2014), 177–187. http://dx.doi.org/10.1016/j.asoc.2014.02.006

Keshtel feeding algorithm
M. Hajiaghaei-Keshteli, M. Aminnayeri, “Solving the integrated scheduling of production rail transportation problem by Keshtel algorithm.” Applied Soft Computing, 25, (2014), 184–203. http://dx.doi.org/10.1016/j.asoc.2014.09.034

Differential search algorithm
P. Civicioglu, “Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm.” Computers & Geosciences, 46, (2012), 229–247
http://dx.doi.org/10.1016/j.cageo.2011.12.011

Teaching-learning-based optimization
R. V. Rao, V. J. Savsani, D. P. Vakharia, “Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems.” Computer-Aided Design, 43, (3), (2011), 303–315.http://dx.doi.org/10.1016/j.cad.2010.12.015

League Championship Algorithm
http://dx.doi.org/10.1016/j.asoc.2013.12.005
Chaotic League Championship Algorithms*
*doi:10.1007/s13369-016-2200-9

Imp**erialist competitive algorithm
E. Atashpaz-Gargari, C. Lucas, “Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition.” in: IEEE Congress on Evolutionary Computation, Singapore (2007), 4661–4667.http://dx.doi.org/10.1016/j.gsf.2014.11.005
Paper published recently: http://dx.doi.org/10.1016/j.gsf.2014.11.005

Amaral, L.R.; Hruschka, E.R. "Transgenic: An evolutionary algorithm operator", Neurocomputing 127, 104-113, 2014. DOI: https://doi.org/10.1016/j.neucom.2013.08.037

See-See Partridge Chicks Optimization

http://ieeexplore.ieee.org/document/7429421/ (Mexican AI conference, 2015).

Details on the publication are also at http://dblp.uni-trier.de/rec/bibtex/conf/micai/OmidvarPR15

Suggested by Thomas Stützle.

L

• Lion - Maziar Yazdani and Fariborz Jolai. "Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm." Journal of Computational Design and Engineering 3(1):24-36, 2016. DOI

M

• Monkey - R. Zhaoand W. Tang."Monkey Algorithm for Global Numerical Optimization." Journal of Uncertain Systems 2(3):165-176,2008. URL

W

• Water Evaporation - A. Kaveh and T. Bakhshpoori. "Water Evaporation Optimization: A novel physically inspired optimization algorithm." Computers & Structures 167:69-85, 2016. DOI

“Research on Permutation Flow-shop Scheduling Problem based on Improved Genetic Immune Algorithm with vaccinated offspring” [link]

By Federico Pagnozzi from ulb.ac.be

Added by Felipe on 2018-03-31

• Check for eligibility
• Check for duplicates
• Add to bestiary

http://www.inderscience.com/offer.php?id=81473
Sperm motility algorithm: a novel metaheuristic approach for global optimisation
by Osama Abdel Raouf; Ibrahim M. Hezam
International Journal of Operational Research (IJOR), Vol. 28, No. 2, 2017

Sawko, Robert, and Grzegorz Skorupa. "A new approach to global
optimization: sheep optimization." Prace Naukowe Politechniki
Warszawskiej. Elektronika 165 (2008): 181-188.

Binary whale optimization algorithm: a new metaheuristic approach for...
https://www.tandfonline.com/doi/full/10.1080/0305215X.2018.1463527

http://www.sciencedirect.com/science/article/pii/S0965997816305646

Chemical-Reaction-Inspired Metaheuristic for Optimization
*DOI: * 10.1109/TEVC.2009.2033580 https://doi.org/10.1109/TEVC.2009.2033580

Chemical Reactions - Bilal Alatas. "ACROA: artificial chemical reaction optimization algorithm for global optimization." Expert Systems with Applications 38(10):13170-13180, 2011. [DOI http://dx.doi.org/10.1016/j.eswa.2011.04.126] [Google Scholar https://scholar.google.com.br/scholar?cluster=4558455092048574656&hl=en&as_sdt=0,5]

(Contributed by Iago Augusto de Carvalho, UFMG)

• Salp swarm: http://www.sciencedirect.com/science/article/pii/S0965997816307736
• Kinetic energy of gas molecules (KGMO): http://www.sciencedirect.com/science/article/pii/S0020025514001327
• Artificial Physics Optimization: http://ieeexplore.ieee.org/document/5393736/?reload=true

Third one not added, there is an earlier gravitation-based method.

S. Salcedo-Sanz, Modern meta-heuristics based on nonlinear physics
processes: A review of models and design procedures, Physics Reports,
vol. 655, pp. 1-70, 2016.
https://www.sciencedirect.com/science/article/pii/S0370157316302332

https://arxiv.org/pdf/1312.4078.pdf

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4132328/

Oftadeh R, Mahjoob MJ, Shariatpanahi M. A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Computers and Mathematics with Applications. 2010;60(7):2087–2098.

http://waset.org/publications/9999515/a-new-tool-for-global-optimization-problems-cuttlefish-algorithm

Cortés P, García JM, Muñuzuri J, Onieva L. Viral systems: a new bio-inspired optimisation approach. Computers and Operations Research. 2008;35(9):2840–2860.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4052047/

https://search.proquest.com/docview/1806561886?pq-origsite=gscholar

@inproceedings{debruyne2016harris,
title={Harris's Hawk Multi-Objective Optimizer for Reference Point Problems},
author={DeBruyne, A Sandra and Kaur, B Devinder},
booktitle={Proceedings on the International Conference on Artificial Intelligence (ICAI)},
pages={287-292},
year={2016}
}

• extra laughs for the figures on Page 2.

It is getting a bit unwieldly to edit the huge readme.

Ideally we would be able to create a bib file for the bestiary, a header and footer file, and use some sort of script to knit together the whole thing as a markdown file.

Well I know everbody hates mosquitoes but this is for the sake of academia ;)
There are two inspirations from it:
1.

@article{arif2011mox,
  title={MOX: A novel global optimization algorithm inspired from Oviposition site selection and egg hatching inhibition in mosquitoes},
  author={Arif, Muhammad and others},
  journal={Applied Soft Computing},
  volume={11},
  number={8},
  pages={4614--4625},
  year={2011},
  publisher={Elsevier}
}

@INPROCEEDINGS{7754783, 
author={M. Alauddin}, 
booktitle={2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)}, 
title={Mosquito flying optimization (MFO)}, 
year={2016}, 
volume={}, 
number={}, 
pages={79-84}, 
doi={10.1109/ICEEOT.2016.7754783}, 
ISSN={}, 
month={March},}

Contributed by Iago Augusto (UFMG, Brazil):

N.Ath. Kallioras, N.D. Lagaros, D.N. Avtzis, “Pity Beetle Algorithm - A new metaheuristic inspired by the behaviour of bark beetles”, Advances in Engineering Software, Volume 121, July 2018, Pages 147-166, 2018

In the past years a great variety of nature-inspired algorithms have proven their ability to efficiently handle combinatorial optimization problems ranging from design and form finding problems to mainstream economic theory and medical diagnosis. In this study, a new metaheuristic algorithm called Pity Beetle Algorithm (PBA) is presented and its efficiency against state-of-the-art algorithms is assessed. The proposed algorithm was inspired by the aggregation behavior, searching for nest and food, of the beetle named Pityogenes chalcographus, also known as six-toothed spruce bark beetle. This beetle has the ability to locate and harvest on the bark of weakened trees into a forest, while when its population exceeds a specific threshold it can infest healthy and robust trees as well. As it was proved in this study, PBA can be applied to NP-hard optimization problems regardless of the scale, since PBA has the ability to search for possible solutions into large spaces and to find the global optimum solution overcoming local optima. In this work, PBA was applied to well-known benchmark uni-modal and multi-modal, separable and non-separable unconstrained test functions while it was also compared to other well established metaheuristic algorithms implementing also the CEC 2014 benchmark and complexity evaluation tests.

https://www.sciencedirect.com/science/article/pii/S0965997817305239

Spotted in the wild by Rafael Stubs Parpinelli (UDESC, Brazil)

• Gai-Ge Wang, Suash Deb, Leandro dos Santos Coelho:
  Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. IJBIC 12(1): 1-22 (2018)

• Juliano Pierezan, Leandro dos Santos Coelho:
  Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems. CEC 2018: 1-8.

The Camels entry, in the C cage, is attributed an incorrect doi.

That publication does not have a doi, but the one provided (10.1007/s00707-009-0270-4) points to the charged systems paper.

Here is the corrected bibtex:

@article{Camels,
  year  = {2016},
  volume = {12},
  number = {2},
  pages = {167--177},
  issn = {18145892},
  author = {M. K. Ibrahim, R. S. Ali},
  title = {Novel Optimization Algorithm Inspired by Camel Traveling Behavior},
  journal = {Iraq J. Electrical and Electronic Engineering},
}

This url points to the article, which also has a full-text link:
url = {https://www.iasj.net/iasj?func=article&aId=118375}

Two by the very same author, in the very same event!

KLEIN, CARLOS E. ; COELHO, L. S. . Meerkats-inspired algorithms for global optimization problems. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2018, Bruges. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). Louvain-la-Neuve: Ciaco, Michel Verleysen (editor), 2018. v. 1. p. 679-684.

KLEIN, CARLOS E. ; MARIANI, V. C. ; COELHO, L. S. . Cheetah based optimization algorithm: a novel swarm intelligence paradigm. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2018, Bruges. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). Louvain-la-Neuve: Ciaco, Michel Verleysen, 2018. v. 1. p. 685-690.

Also to check/include:

PIEREZAN, J. ; COELHO, LEANDRO DOS S. . Coyote Optimization algorithm: a new metaheuristic for global optimization problems. In: EEE Congress on Evolutionary Computation (IEEE CEC), 2018, Rio de Janeiro. EEE Congress on Evolutionary Computation (IEEE CEC). Piscataway,NJ: IEEE Press, 2018. v. 1. p. 1-6.

Gai-Ge Wang ; GAO, X. ; ZENGER, K. ; COELHO, L. S. . A novel metaheuristic algorithm inspired by rhino herd behavior. In: 9th Eurosim Congress on Modelling and Simulation (EUROSIM 2016), 2016, Oulu. 9th Eurosim Congress on Modelling and Simulation (EUROSIM 2016), 2016. v. 1. p. 1-6.

Work on the intro text and possibly remove the year-2000 threshold, as suggested by Christian Blum via e-mail:

"in my opinion you should (1) work on the wording of your initial paragraphs such that everyone coming across your website understands your intentions, and (2) removing this artificial threshold of the year 2000, and simply include all works on algorithms inspired by nature (also evolutionary algorithms, ant colony optimization, etc)."

I think the paper below may also be included:
Social group optimization (SGO): a new population evolutionary optimization technique
DOI 10.1007/s40747-016-0022-8

Seeven Amic
Université des Mascareignes, Pamplemousses, Mauritius

Plant intelligence...

http://download.springer.com/static/pdf/600/art%253A10.1007%252Fs10462-016-9486-6.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs10462-016-9486-6&token2=exp=1489677113~acl=%2Fstatic%2Fpdf%2F600%2Fart%25253A10.1007%25252Fs10462-016-9486-6.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Farticle%252F10.1007%252Fs10462-016-9486-6*~hmac=e5671e7ee2e3b79d37ca289c3677f09fc8836953a353f3ec15172d3e397431ec

https://link.springer.com/article/10.1007/s11063-017-9750-z