Swarm intelligence

{{See also|Swarm behaviour}}

{{main|Boids}}

Boids is an artificial life program, developed by Craig Reynolds in 1986, which simulates flocking. It was published in 1987 in the proceedings of the ACM SIGGRAPH conference.{{Cite book

| last1=Reynolds

| first1=Craig

| title=Proceedings of the 14th annual conference on Computer graphics and interactive techniques

| date=1987

| chapter=Flocks, herds and schools: A distributed behavioral model

| s2cid=546350

| author1-link=Craig Reynolds (computer graphics)

| publisher=Association for Computing Machinery

| pages=25–34

| isbn=978-0-89791-227-3

| doi=10.1145/37401.37406

| citeseerx=10.1.1.103.7187

}}

The name "boid" corresponds to a shortened version of "bird-oid object", which refers to a bird-like object.{{Cite journal

| last1=Banks

| first1=Alec

| last2=Vincent

| first2=Jonathan

| last3=Anyakoha

| first3=Chukwudi

| s2cid=2344624

| title=A review of particle swarm optimization. Part I: background and development

|date=July 2007

| volume=6

| issue=4

| pages=467–484

| journal=Natural Computing

| doi=10.1007/s11047-007-9049-5

| citeseerx=10.1.1.605.5879

}}

As with most artificial life simulations, Boids is an example of emergent behavior; that is, the complexity of Boids arises from the interaction of individual agents (the boids, in this case) adhering to a set of simple rules. The rules applied in the simplest Boids world are as follows:

• separation: steer to avoid crowding local flockmates
• alignment: steer towards the average heading of local flockmates
• cohesion: steer to move toward the average position (center of mass) of local flockmates

More complex rules can be added, such as obstacle avoidance and goal seeking.

{{main|Self-propelled particles}}

Self-propelled particles (SPP), also referred to as the Vicsek model, was introduced in 1995 by Vicsek et al.{{cite journal | last1 = Vicsek | first1 = T. |author-link1=Tamás Vicsek| last2 = Czirok | first2 = A. | last3 = Ben-Jacob | first3 = E. | last4 = Cohen | first4 = I. | last5 = Shochet | first5 = O. | s2cid = 15918052 | year = 1995 | arxiv = cond-mat/0611743 | title = Novel type of phase transition in a system of self-driven particles | journal = Physical Review Letters | volume = 75 | issue = 6 | pages = 1226–1229 | doi = 10.1103/PhysRevLett.75.1226 | pmid=10060237|bibcode = 1995PhRvL..75.1226V }} as a special case of the boids model introduced in 1986 by Reynolds. A swarm is modelled in SPP by a collection of particles that move with a constant speed but respond to a random perturbation by adopting at each time increment the average direction of motion of the other particles in their local neighbourhood.{{cite journal | last1 = Czirók | first1 = A. | last2 = Vicsek | first2 = T. | s2cid = 14211016 | year = 2006 | arxiv = cond-mat/0611742 | title = Collective behavior of interacting self-propelled particles | journal = Physica A | volume = 281 | issue = 1 | pages = 17–29 | doi = 10.1016/S0378-4371(00)00013-3 | bibcode=2000PhyA..281...17C}} SPP models predict that swarming animals share certain properties at the group level, regardless of the type of animals in the swarm.{{cite journal | last1 = Buhl | first1 = J. | last2 = Sumpter | first2 = D.J.T. | last3 = Couzin | first3 = D. | last4 = Hale | first4 = J.J. | last5 = Despland | first5 = E. | last6 = Miller | first6 = E.R. | last7 = Simpson | first7 = S.J. | s2cid = 359329 | year = 2006 | title = From disorder to order in marching locusts | url = http://webscript.princeton.edu/~icouzin/website/wp-content/plugins/bib2html/data/papers/buhl06.pdf | journal = Science | volume = 312 | issue = 5778 | pages = 1402–1406 | doi = 10.1126/science.1125142 | pmid = 16741126 | bibcode = 2006Sci...312.1402B | display-authors = etal | access-date = 2011-10-07 | archive-date = 2011-09-29 | archive-url = https://web.archive.org/web/20110929220754/http://webscript.princeton.edu/~icouzin/website/wp-content/plugins/bib2html/data/papers/buhl06.pdf | url-status = dead }} Swarming systems give rise to emergent behaviours which occur at many different scales, some of which are turning out to be both universal and robust. It has become a challenge in theoretical physics to find minimal statistical models that capture these behaviours.{{cite journal | last1 = Toner | first1 = J. | last2 = Tu | first2 = Y. | last3 = Ramaswamy | first3 = S. | year = 2005 | title = Hydrodynamics and phases of flocks | url = http://eprints.iisc.ernet.in/3397/1/A89.pdf | journal = Annals of Physics | volume = 318 | issue = 1 | pages = 170–244 | bibcode = 2005AnPhy.318..170T | doi = 10.1016/j.aop.2005.04.011 | access-date = 2011-10-07 | archive-date = 2011-07-18 | archive-url = https://web.archive.org/web/20110718172510/http://eprints.iisc.ernet.in/3397/1/A89.pdf | url-status = dead }}{{cite journal | last1 = Bertin | first1 = E. | last2 = Droz | first2 = M. | last3 = Grégoire | first3 = G. | s2cid = 17686543 | year = 2009 | arxiv = 0907.4688 | title = Hydrodynamic equations for self-propelled particles: microscopic derivation and stability analysis | journal = J. Phys. A | volume = 42 | issue = 44 | page = 445001 | doi = 10.1088/1751-8113/42/44/445001 |bibcode = 2009JPhA...42R5001B }}{{cite journal | last1 = Li | first1 = Y.X. | last2 = Lukeman | first2 = R. | last3 = Edelstein-Keshet | first3 = L. | year = 2007 | title = Minimal mechanisms for school formation in self-propelled particles | url = http://www.iam.ubc.ca/~lukeman/fish_school_f.pdf | archive-url = https://web.archive.org/web/20111001032730/http://www.iam.ubc.ca/~lukeman/fish_school_f.pdf | url-status = dead | archive-date = 2011-10-01 | journal = Physica D: Nonlinear Phenomena | volume = 237 | issue = 5 | pages = 699–720 | doi = 10.1016/j.physd.2007.10.009 | bibcode = 2008PhyD..237..699L | display-authors = etal }}

{{See also|List of metaphor-based metaheuristics}}

Evolutionary algorithms (EA), particle swarm optimization (PSO), differential evolution (DE), ant colony optimization (ACO) and their variants dominate the field of nature-inspired metaheuristics.{{cite book|first=Michael A.|last=Lones|title=Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation |chapter=Metaheuristics in nature-inspired algorithms |date=2014 |s2cid=14997975|pages=1419–1422|url=http://www.macs.hw.ac.uk/~ml355/common/papers/lones-gecco2014-metaheuristics.pdf|doi=10.1145/2598394.2609841|isbn=9781450328814|citeseerx=10.1.1.699.1825}} This list includes algorithms published up to circa the year 2000. A large number of more recent metaphor-inspired metaheuristics have started to attract criticism in the research community for hiding their lack of novelty behind an elaborate metaphor.{{Cite journal |last=Sörensen |first=Kenneth |date=January 2015 |title=Metaheuristics—the metaphor exposed |url=https://onlinelibrary.wiley.com/doi/10.1111/itor.12001 |journal=International Transactions in Operational Research |language=en |volume=22 |issue=1 |pages=3–18 |doi=10.1111/itor.12001 |issn=0969-6016}}{{Cite journal |last1=Glover |first1=Fred |last2=Sörensen |first2=Kenneth |date=2015 |title=Metaheuristics |journal=Scholarpedia |language=en |volume=10 |issue=4 |pages=6532 |doi=10.4249/scholarpedia.6532 |doi-access=free |issn=1941-6016}}{{cite web |last1=Swan |first1=Jerry |last2=Adriaensen |first2=Steven |last3=Bishr |first3=Mohamed |last4=Burke |first4=Edmund K. |last5=Clark |first5=John A. |last6=De Causmaecke |first6=Patrick |last7=Durillo |first7=Juan José |last8=Hammond |first8=Kevin |last9=Hart |first9=Emma |last10=Johnson |first10=Colin G. |last11=Kocsis |first11=Zoltan A. |last12=Kovitz |first12=Ben |last13=Krawiec |first13=Krzysztof |last14=Martin |first14=Simon |last15=Merelo |first15=Juan J. |date=2015 |title=A Research Agenda for Metaheuristic Standardization |url=http://www.cs.nott.ac.uk/~exo/docs/publications/research-agenda-metaheuristic.pdf |access-date=2025-03-03 |website=Semantic Scholar |first16=Leandro L. |last16=Minku |first17=Ender |last17=Özcan |first18=Gisele Lobo |last18=Pappa |first19=Erwin |last19=Pesch |first20=Pablo |last20=García-Sánchez |first21=Andrea |last21=Schaerf |first22=Kevin |last22=Sim |first23=Jim |last23=Smith |first24=Thomas |last24=Stützle |first25=Stefan |last25=Wagner |s2cid=63728283}} For algorithms published since that time, see List of metaphor-based metaheuristics.

Metaheuristics lack a confidence in a solution.{{Citation| last1=Silberholz| first1=John| title=Computational Comparison of Metaheuristics|date=2019|work=Handbook of Metaheuristics|pages=581–604|editor-last=Gendreau|editor-first=Michel|series=International Series in Operations Research & Management Science|place=Cham|publisher=Springer International Publishing|language=en|doi=10.1007/978-3-319-91086-4_18|isbn=978-3-319-91086-4|last2=Golden|first2=Bruce|last3=Gupta|first3=Swati|last4=Wang|first4=Xingyin| s2cid=70030182|editor2-last=Potvin|editor2-first=Jean-Yves}} When appropriate parameters are determined, and when sufficient convergence stage is achieved, they often find a solution that is optimal, or near close to optimum – nevertheless, if one does not know optimal solution in advance, a quality of a solution is not known. In spite of this obvious drawback it has been shown that these types of algorithms work well in practice, and have been extensively researched, and developed.{{Citation|last1=Burke|first1=Edmund|title=Variable Neighborhood Search for Nurse Rostering Problems|date=2004|work=Metaheuristics: Computer Decision-Making|pages=153–172|editor-last=Resende|editor-first=Mauricio G. C.|series=Applied Optimization|place=Boston, MA|publisher=Springer US|language=en|doi=10.1007/978-1-4757-4137-7_7|isbn=978-1-4757-4137-7|last2=De Causmaecker|first2=Patrick|last3=Petrovic|first3=Sanja|last4=Berghe|first4=Greet Vanden|editor2-last=de Sousa|editor2-first=Jorge Pinho}}{{Cite journal|last=Fu|first=Michael C.|date=2002-08-01|title=Feature Article: Optimization for simulation: Theory vs. Practice|journal=INFORMS Journal on Computing|volume=14|issue=3|pages=192–215|doi=10.1287/ijoc.14.3.192.113|issn=1091-9856}}{{Cite journal|last1=Dorigo|first1=Marco|last2=Birattari|first2=Mauro|last3=Stutzle|first3=Thomas|date=November 2006|title=Ant colony optimization|journal=IEEE Computational Intelligence Magazine|volume=1|issue=4|pages=28–39|doi=10.1109/MCI.2006.329691|issn=1556-603X}}{{Cite journal|last=Hayes-RothFrederick|date=1975-08-01|title=Review of "Adaptation in Natural and Artificial Systems by John H. Holland", The U. of Michigan Press, 1975|journal=ACM SIGART Bulletin|issue=53|page=15|language=EN|doi=10.1145/1216504.1216510|s2cid=14985677}}{{Citation|last1=Resende|first1=Mauricio G.C.|title=Greedy Randomized Adaptive Search Procedures: Advances, Hybridizations, and Applications|date=2010|work=Handbook of Metaheuristics|pages=283–319|editor-last=Gendreau|editor-first=Michel|series=International Series in Operations Research & Management Science|place=Boston, MA|publisher=Springer US|language=en|doi=10.1007/978-1-4419-1665-5_10|isbn=978-1-4419-1665-5|last2=Ribeiro|first2=Celso C.|editor2-last=Potvin|editor2-first=Jean-Yves}} On the other hand, it is possible to avoid this drawback by calculating solution quality for a special case where such calculation is possible, and after such run it is known that every solution that is at least as good as the solution a special case had, has at least a solution confidence a special case had. One such instance is Ant-inspired Monte Carlo algorithm for Minimum Feedback Arc Set where this has been achieved probabilistically via hybridization of Monte Carlo algorithm with Ant Colony Optimization technique.{{Cite journal|last1=Kudelić|first1=Robert|last2=Ivković|first2=Nikola|date=2019-05-15|title=Ant inspired Monte Carlo algorithm for minimum feedback arc set|url=http://www.sciencedirect.com/science/article/pii/S0957417418307899|journal=Expert Systems with Applications|language=en|volume=122|pages=108–117|doi=10.1016/j.eswa.2018.12.021|s2cid=68071710|issn=0957-4174|url-access=subscription}}

{{main|Ant colony optimization}}

Ant colony optimization (ACO), introduced by Dorigo in his doctoral dissertation, is a class of optimization algorithms modeled on the actions of an ant colony. ACO is a probabilistic technique useful in problems that deal with finding better paths through graphs. Artificial 'ants'—simulation agents—locate optimal solutions by moving through a parameter space representing all possible solutions. Natural ants lay down pheromones directing each other to resources while exploring their environment. The simulated 'ants' similarly record their positions and the quality of their solutions, so that in later simulation iterations more ants locate for better solutions.Ant Colony Optimization by Marco Dorigo and Thomas Stützle, MIT Press, 2004. {{ISBN|0-262-04219-3}}

{{main|Particle swarm optimization}}

Particle swarm optimization (PSO) is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles.{{cite journal |doi=10.1023/A:1016568309421 |title=Recent Approaches to Global Optimization Problems Through Particle Swarm Optimization |last1=Parsopoulos |first1=K. E. |last2=Vrahatis |first2=M. N. |s2cid=4021089 |journal=Natural Computing |volume=1 |issue=2–3 |pages=235–306 |year=2002 }}[http://www.iste.co.uk/?searchtext=clerc&ACTION=Search&cat=&ACTION=Search Particle Swarm Optimization] by Maurice Clerc, ISTE, {{ISBN|1-905209-04-5}}, 2006. Particles then move through the solution space, and are evaluated according to some fitness criterion after each timestep. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large number of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.

{{Main|Artificial bee colony algorithm}}

Karaboga introduced ABC metaheuristic in 2005 as an answer to optimize numerical problems. Inspired by honey bee foraging behavior, Karaboga's model had three components. The employed, onlooker, and scout. In practice, the artificial scout bee would expose all food source positions (solutions) good or bad. The employed bee would search for the shortest route to each position to extract the food amount (quality) of the source. If the food was depleted from the source, the employed bee would become a scout and randomly search for other food sources. Each source that became abandoned created negative feedback meaning, the answers found were poor solutions. The onlooker bees wait for employed bees to either abandon a source or give information that the source has a large quantity of food and is worth sending additional resources to. The more an onlooker bee is recruited, the more positive the feedback is meaning that the answer is likely a good solution.

Artificial Swarm Intelligence (ASI) is method of amplifying the collective intelligence of networked human groups using control algorithms modeled after natural swarms. Sometimes referred to as Human Swarming or Swarm AI, the technology connects groups of human participants into real-time systems that deliberate and converge on solutions as dynamic swarms when simultaneously presented with a question{{Cite book|last=Rosenberg|first=Louis|date=2015-07-20|chapter=Human Swarms, a real-time method for collective intelligence|chapter-url=https://www.mitpressjournals.org/doi/abs/10.1162/978-0-262-33027-5-ch117|volume=27|pages=658–659|doi=10.7551/978-0-262-33027-5-ch117|isbn=9780262330275|title=07/20/2015-07/24/2015}}{{Cite book|last1=Rosenberg|first1=Louis|last2=Willcox|first2=Gregg|title=Intelligent Systems and Applications |chapter=Artificial Swarm Intelligence |date=2020|editor-last=Bi|editor-first=Yaxin|editor2-last=Bhatia|editor2-first=Rahul|editor3-last=Kapoor|editor3-first=Supriya|series=Advances in Intelligent Systems and Computing|language=en|publisher=Springer International Publishing|volume=1037|pages=1054–1070|doi=10.1007/978-3-030-29516-5_79|isbn=9783030295165|s2cid=195258629}}{{Cite journal|last1=Metcalf|first1=Lynn|last2=Askay|first2=David A.|last3=Rosenberg|first3=Louis B.|s2cid=202323483|date=2019|title=Keeping Humans in the Loop: Pooling Knowledge through Artificial Swarm Intelligence to Improve Business Decision Making|journal=California Management Review|language=en|volume=61|issue=4|pages=84–109|doi=10.1177/0008125619862256|issn=0008-1256|url=https://digitalcommons.calpoly.edu/cgi/viewcontent.cgi?article=1105&context=it_fac|url-access=subscription}} ASI has been used for a wide range of applications, from enabling business teams to generate highly accurate financial forecasts{{Cite book |doi = 10.1109/HCC46620.2019.00019|chapter = "Human Swarming" Amplifies Accuracy and ROI when Forecasting Financial Markets|title = 2019 IEEE International Conference on Humanized Computing and Communication (HCC)|pages = 77–82|year = 2019|last1 = Schumann|first1 = Hans|last2 = Willcox|first2 = Gregg|last3 = Rosenberg|first3 = Louis|last4 = Pescetelli|first4 = Niccolo|s2cid = 209496644|isbn = 978-1-7281-4125-1}} to enabling sports fans to outperform Vegas betting markets.{{Cite news|title=How AI systems beat Vegas oddsmakers in sports forecasting accuracy|work=TechRepublic|url=https://www.techrepublic.com/article/how-ai-systems-beat-vegas-oddsmakers-in-sports-forecasting-accuracy/ |first=Macy |last=Bayern |date=September 4, 2018|access-date=2018-09-10}} ASI has also been used to enable groups of doctors to generate diagnoses with significantly higher accuracy than traditional methods. ASI has been used by the Food and Agriculture Organization (FAO) of the United Nations to help forecast famines in hotspots around the world.{{Cite web|last=Rosenberg|first= Louis |date=October 13, 2021

|title=Swarm intelligence: AI inspired by honeybees can help us make better decisions|url=https://bigthink.com/the-future/swarm-intelligence-ai-honeybees/|access-date=|website=Big Think|language=en-US}}{{Better source needed|reason=Citation is the written by company's owner, need secondary or tertiary source to confirm.|date=December 2021}}

Swarm Intelligence-based techniques can be used in a number of applications. The U.S. military is investigating swarm techniques for controlling unmanned vehicles. The European Space Agency is thinking about an orbital swarm for self-assembly and interferometry. NASA is investigating the use of swarm technology for planetary mapping. A 1992 paper by M. Anthony Lewis and George A. Bekey discusses the possibility of using swarm intelligence to control nanobots within the body for the purpose of killing cancer tumors.{{cite journal |last1=Lewis |first1=M. Anthony |last2=Bekey |first2=George A. |title=The Behavioral Self-Organization of Nanorobots Using Local Rules |journal=Proceedings of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems |url=https://www.researchgate.net/publication/3690783}} Conversely al-Rifaie and Aber have used stochastic diffusion search to help locate tumours.{{cite journal | last1 = al-Rifaie | first1 = M.M. | last2 = Aber | first2 = A. | title = Identifying metastasis in bone scans with Stochastic Diffusion Search | url =https://www.researchgate.net/publication/262223271 | journal = Proc. IEEE Information Technology in Medicine and Education, ITME | volume = 2012 | pages = 519–523 }}al-Rifaie, Mohammad Majid, Ahmed Aber, and Ahmed Majid Oudah. "[http://www.academia.edu/download/30759619/Utilising-Stochastic-Diffusion-Search.pdf Utilising Stochastic Diffusion Search to identify metastasis in bone scans and microcalcifications on mammographs]{{dead link|date=July 2022|bot=medic}}{{cbignore|bot=medic}}." In Bioinformatics and Biomedicine Workshops (BIBMW), 2012 IEEE International Conference on, pp. 280-287. IEEE, 2012. Swarm intelligence (SI) is increasingly applied in Internet of Things (IoT){{Cite journal |last1=Sun |first1=Weifeng |last2=Tang |first2=Min |last3=Zhang |first3=Lijun |last4=Huo |first4=Zhiqiang |last5=Shu |first5=Lei |date=January 2020 |title=A Survey of Using Swarm Intelligence Algorithms in IoT |journal=Sensors |language=en |volume=20 |issue=5 |pages=1420 |doi=10.3390/s20051420 |doi-access=free |pmid=32150912 |pmc=7085620 |bibcode=2020Senso..20.1420S |issn=1424-8220}}{{Cite journal |last1=Abualigah |first1=Laith |last2=Falcone |first2=Deborah |last3=Forestiero |first3=Agostino |date=2023-05-29 |title=Swarm Intelligence to Face IoT Challenges |journal=Computational Intelligence and Neuroscience |volume=2023 |pages=4254194 |doi=10.1155/2023/4254194 |doi-access=free |issn=1687-5265 |pmid=37284052|pmc=10241578 }} systems, and by association to Intent-Based Networking (IBN),{{Cite web |title=Intent-Based Networking for the Internet of Things {{!}} Frontiers Research Topic |url=https://www.frontiersin.org/research-topics/59831/intent-based-networking-for-the-internet-of-things/overview |access-date=2024-08-14 |website=www.frontiersin.org |language=en}} due to its ability to handle complex, distributed tasks through decentralized, self-organizing algorithms. Swarm intelligence has also been applied for data mining{{cite journal |first1=D. |last1=Martens |first2=B. |last2=Baesens |first3=T. |last3=Fawcett |title=Editorial Survey: Swarm Intelligence for Data Mining |journal=Machine Learning |volume=82 |issue=1 |pages=1–42 |year=2011 |doi=10.1007/s10994-010-5216-5 |doi-access=free }} and cluster analysis.{{cite journal |first1=M. |last1=Thrun |first2=A. |last2=Ultsch |title= Swarm Intelligence for Self-Organized Clustering |journal=Artificial Intelligence |volume=290 |pages= 103237 |year=2021 |doi=10.1016/j.artint.2020.103237|s2cid=213923899 |arxiv=2106.05521 }} Ant-based models are further subject of modern management theory.{{cite book |last1=Fladerer |first1=Johannes-Paul |last2=Kurzmann |first2=Ernst |title=THE WISDOM OF THE MANY : how to create self -organisation and how to use collective... intelligence in companies and in society from mana. |date=November 2019 |publisher=BOOKS ON DEMAND |isbn=9783750422421}}

The use of swarm intelligence in telecommunication networks has also been researched, in the form of ant-based routing. This was pioneered separately by Dorigo et al. and Hewlett-Packard in the mid-1990s, with a number of variants existing. Basically, this uses a probabilistic routing table rewarding/reinforcing the route successfully traversed by each "ant" (a small control packet) which flood the network. Reinforcement of the route in the forwards, reverse direction and both simultaneously have been researched: backwards reinforcement requires a symmetric network and couples the two directions together; forwards reinforcement rewards a route before the outcome is known (but then one would pay for the cinema before one knows how good the film is). As the system behaves stochastically and is therefore lacking repeatability, there are large hurdles to commercial deployment. Mobile media and new technologies have the potential to change the threshold for collective action due to swarm intelligence (Rheingold: 2002, P175).

The location of transmission infrastructure for wireless communication networks is an important engineering problem involving competing objectives. A minimal selection of locations (or sites) are required subject to providing adequate area coverage for users. A very different, ant-inspired swarm intelligence algorithm, stochastic diffusion search (SDS), has been successfully used to provide a general model for this problem, related to circle packing and set covering. It has been shown that the SDS can be applied to identify suitable solutions even for large problem instances.Whitaker, R.M., Hurley, S.. [https://dl.acm.org/citation.cfm?id=508902 An agent based approach to site selection for wireless networks]. Proc ACM Symposium on Applied Computing, pp. 574–577, (2002).

Airlines have also used ant-based routing in assigning aircraft arrivals to airport gates. At Southwest Airlines a software program uses swarm theory, or swarm intelligence—the idea that a colony of ants works better than one alone. Each pilot acts like an ant searching for the best airport gate. "The pilot learns from his experience what's the best for him, and it turns out that that's the best solution for the airline," Douglas A. Lawson explains. As a result, the "colony" of pilots always go to gates they can arrive at and depart from quickly. The program can even alert a pilot of plane back-ups before they happen. "We can anticipate that it's going to happen, so we'll have a gate available," Lawson says.{{cite news |work=Science Daily |date=April 1, 2008 |title=Planes, Trains and Ant Hills: Computer scientists simulate activity of ants to reduce airline delays |url=https://www.sciencedaily.com/videos/2008/0406-planes_trains_and_ant_hills.htm |access-date=December 1, 2010 |url-status=dead |archive-url=https://web.archive.org/web/20101124132227/https://www.sciencedaily.com/videos/2008/0406-planes_trains_and_ant_hills.htm |archive-date=November 24, 2010 }}

Artists are using swarm technology as a means of creating complex interactive systems or simulating crowds.{{Citation needed|reason=How?, direct mathematical use? 'Poetic' use? and if so, abstract or concrete usage?|date=May 2021}}

The Lord of the Rings film trilogy made use of similar technology, known as Massive (software), during battle scenes. Swarm technology is particularly attractive because it is cheap, robust, and simple.

Stanley and Stella in: Breaking the Ice was the first movie to make use of swarm technology for rendering, realistically depicting the movements of groups of fish and birds using the Boids system.{{Citation needed|reason='the first movie' to show X by Y while using Z system needs a citation|date=May 2021}}

Tim Burton's Batman Returns also made use of swarm technology for showing the movements of a group of bats.

{{cite journal |last1=Mahant |first1=Manish |last2=Singh Rathore |first2=Kalyani |last3=Kesharwani |first3=Abhishek |last4=Choudhary |first4=Bharat |title=A Profound Survey on Swarm Intelligence |journal=International Journal of Advanced Computer Research |date=2012 |volume=2 |issue=1 |url=http://scholar.googleusercontent.com/scholar?q=cache:-gCjl9XY6IcJ:scholar.google.com/+A+Profound+Survey+on+Swarm+Intelligence&hl=vi&as_sdt=0,5 |access-date=3 October 2022}}

Airlines have used swarm theory to simulate passengers boarding a plane. Southwest Airlines researcher Douglas A. Lawson used an ant-based computer simulation employing only six interaction rules to evaluate boarding times using various boarding methods.(Miller, 2010, xii-xviii).{{cite book |last=Miller |first=Peter |year=2010 |title=The Smart Swarm: How understanding flocks, schools, and colonies can make us better at communicating, decision making, and getting things done |publisher=Avery |location=New York |isbn=978-1-58333-390-7 |url-access=registration |url=https://archive.org/details/smartswarmhowund00mill }}

Networks of distributed users can be organized into "human swarms" through the implementation of real-time closed-loop control systems.{{Cite web|url=http://www.bbc.com/future/story/20161215-why-bees-could-be-the-secret-to-superhuman-intelligence|title=Why bees could be the secret to superhuman intelligence|last=Oxenham|first=Simon|date=15 December 2016 |access-date=2017-01-20}}{{Cite book|last1=Rosenberg|first1=L.|last2=Pescetelli|first2=N.|last3=Willcox|first3=G.|title=2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) |chapter=Artificial Swarm Intelligence amplifies accuracy when predicting financial markets |s2cid=21312426|date=October 2017|pages=58–62|doi=10.1109/UEMCON.2017.8248984|isbn=978-1-5386-1104-3}} Developed by Louis Rosenberg in 2015, human swarming, also called artificial swarm intelligence, allows the collective intelligence of interconnected groups of people online to be harnessed.{{Cite journal|url=https://www.csmonitor.com/Technology/2016/0512/Smarter-as-a-group-How-swarm-intelligence-picked-Derby-winners|title=Smarter as a group: How swarm intelligence picked Derby winners|journal=Christian Science Monitor}}{{Cite web|url=https://www.cnet.com/tech/services-and-software/swarm-ai-unu-boasts-it-can-predict-winners-using-humans/|title=AI startup taps human 'swarm' intelligence to predict winners|website=CNET}} The collective intelligence of the group often exceeds the abilities of any one member of the group.{{Cite journal|last=Rosenberg|first=Louis|date=2016-02-12|title=Artificial Swarm Intelligence, a human-in-the-loop approach to A.I.|url=https://dl.acm.org/doi/10.5555/3016387.3016604|journal=Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence|series=AAAI'16|location=Phoenix, Arizona|publisher=AAAI Press|pages=4381–4382}}

Stanford University School of Medicine published in 2018 a study showing that groups of human doctors, when connected together by real-time swarming algorithms, could diagnose medical conditions with substantially higher accuracy than individual doctors or groups of doctors working together using traditional crowd-sourcing methods. In one such study, swarms of human radiologists connected together were tasked with diagnosing chest x-rays and demonstrated a 33% reduction in diagnostic errors as compared to the traditional human methods, and a 22% improvement over traditional machine-learning.{{Cite web|url=https://spectrum.ieee.org/ai-human-hive-mind-diagnoses-pneumonia|title=AI-Human "Hive Mind" Diagnoses Pneumonia|last=Scudellari|first=Megan|date=2018-09-13|website=IEEE Spectrum: Technology, Engineering, and Science News|access-date=2019-07-20}}{{Cite web|url=https://venturebeat.com/2018/09/10/unanimous-ai-achieves-22-more-accurate-pneumonia-diagnoses/|title=Unanimous AI achieves 22% more accurate pneumonia diagnoses|date=2018-09-10|website=VentureBeat|access-date=2019-07-20}}{{Cite web|url=https://www.radiologytoday.net/archive/rt0119p12.shtml|title=A Swarm of Insight - Radiology Today Magazine|website=www.radiologytoday.net|access-date=2019-07-20}}{{Cite book|last1=Rosenberg|first1=Louis|last2=Lungren|first2=Matthew|last3=Halabi|first3=Safwan|last4=Willcox|first4=Gregg|last5=Baltaxe|first5=David|last6=Lyons|first6=Mimi|title=2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) |chapter=Artificial Swarm Intelligence employed to Amplify Diagnostic Accuracy in Radiology |s2cid=58675679|date=November 2018|location=Vancouver, BC|publisher=IEEE|pages=1186–1191|doi=10.1109/IEMCON.2018.8614883|isbn=9781538672662}}

The University of California San Francisco (UCSF) School of Medicine released a preprint in 2021 about the diagnosis of MRI images by small groups of collaborating doctors. The study showed a 23% increase in diagnostic accuracy when using Artificial Swarm Intelligence (ASI) technology compared to majority voting.{{cite arXiv|last1=Shah|first1=Rutwik|last2=Astuto|first2=Bruno|last3=Gleason|first3=Tyler|last4=Fletcher|first4=Will|last5=Banaga|first5=Justin|last6=Sweetwood|first6=Kevin|last7=Ye|first7=Allen|last8=Patel|first8=Rina|last9=McGill|first9=Kevin|last10=Link|first10=Thomas|last11=Crane|first11=Jason|date=2021-09-06|title=Utilizing a digital swarm intelligence platform to improve consensus among radiologists and exploring its applications|class=cs.HC|eprint=2107.07341}}{{Cite journal|title=Utilizing a Digital Swarm Intelligence Platform to Improve Consensus Among Radiologists and Exploring Its Applications|first1=Rutwik|last1=Shah|first2=Bruno|last2=Astuto Arouche Nunes|first3=Tyler|last3=Gleason|first4=Will|last4=Fletcher|first5=Justin|last5=Banaga|first6=Kevin|last6=Sweetwood|first7=Allen|last7=Ye|first8=Rina|last8=Patel|first9=Kevin|last9=McGill|first10=Thomas|last10=Link|first11=Jason|last11=Crane|first12=Valentina|last12=Pedoia|first13=Sharmila|last13=Majumdar|date=April 4, 2023|journal=Journal of Digital Imaging|volume=36|issue=2|pages=401–413|doi=10.1007/s10278-022-00662-3|pmid=36414832|pmc=10039189 }}

Swarm grammars are swarms of stochastic grammars that can be evolved to describe complex properties such as found in art and architecture.{{cite journal|last1=vonMammen|first1=Sebastian|last2=Jacob|first2=Christian|s2cid=17882213|title=The evolution of swarm grammars -- growing trees, crafting art and bottom-up design|journal= IEEE Computational Intelligence Magazine|volume=4|issue=3|pages=10–19|date=2009 |doi=10.1109/MCI.2009.933096|citeseerx=10.1.1.384.9486}} These grammars interact as agents behaving according to rules of swarm intelligence. Such behavior can also suggest deep learning algorithms, in particular when mapping of such swarms to neural circuits is considered.{{cite journal|last1=du Castel|first1=Bertrand|title=Pattern Activation/Recognition Theory of Mind|journal=Frontiers in Computational Neuroscience|volume=9|issue=90|pages=90|date = 15 July 2015|doi=10.3389/fncom.2015.00090|pmid=26236228|pmc=4502584|ref=neuroscience|doi-access=free}}

In a series of works, al-Rifaie et al.{{cite journal | last1 = al-Rifaie | first1 = MM | last2 = Bishop | first2 = J.M. | last3 = Caines | first3 = S. | s2cid = 942335 | year = 2012 | title = Creativity and Autonomy in Swarm Intelligence Systems | url = http://research.gold.ac.uk/17273/1/2012_CC_updated.pdf| journal = Cognitive Computation | volume = 4 | issue = 3| pages = 320–331 | doi=10.1007/s12559-012-9130-y}} have successfully used two swarm intelligence algorithms—one mimicking the behaviour of one species of ants (Leptothorax acervorum) foraging (stochastic diffusion search, SDS) and the other algorithm mimicking the behaviour of birds flocking (particle swarm optimization, PSO)—to describe a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour. The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploiting an artistic tension between the local behaviour of the 'birds flocking'—as they seek to follow the input sketch—and the global behaviour of the "ants foraging"—as they seek to encourage the flock to explore novel regions of the canvas. The "creativity" of this hybrid swarm system has been analysed under the philosophical light of the "rhizome" in the context of Deleuze's "Orchid and Wasp" metaphor.Deleuze G, Guattari F, Massumi B. A thousand plateaus. Minneapolis: University of Minnesota Press; 2004.

A more recent work of al-Rifaie et al., "Swarmic Sketches and Attention Mechanism",{{Cite book | doi=10.1007/978-3-642-36955-1_8|chapter = Swarmic Sketches and Attention Mechanism|title = Evolutionary and Biologically Inspired Music, Sound, Art and Design| volume=7834| pages=85–96|series = Lecture Notes in Computer Science|year = 2013|last1 = Al-Rifaie|first1 = Mohammad Majid| last2=Bishop| first2=John Mark| isbn=978-3-642-36954-4|url = https://research.gold.ac.uk/17268/1/2013_EvoMUSART_sketches_April%283-5%29.pdf| chapter-url=http://research.gold.ac.uk/17268/1/2013_EvoMUSART_sketches_April%283-5%29.pdf}} introduces a novel approach deploying the mechanism of 'attention' by adapting SDS to selectively attend to detailed areas of a digital canvas. Once the attention of the swarm is drawn to a certain line within the canvas, the capability of PSO is used to produce a 'swarmic sketch' of the attended line. The swarms move throughout the digital canvas in an attempt to satisfy their dynamic roles—attention to areas with more details—associated with them via their fitness function. Having associated the rendering process with the concepts of attention, the performance of the participating swarms creates a unique, non-identical sketch each time the 'artist' swarms embark on interpreting the input line drawings. In other works, while PSO is responsible for the sketching process, SDS controls the attention of the swarm.

In a similar work, "Swarmic Paintings and Colour Attention",{{Cite book | doi=10.1007/978-3-642-36955-1_9| chapter=Swarmic Paintings and Colour Attention| title=Evolutionary and Biologically Inspired Music, Sound, Art and Design| volume=7834| pages=97–108| series=Lecture Notes in Computer Science| year=2013| last1=Al-Rifaie| first1=Mohammad Majid| last2=Bishop| first2=John Mark| isbn=978-3-642-36954-4| url=https://research.gold.ac.uk/17267/1/2013_EvoMUSART_paintings_April%283-5%29.pdf| chapter-url=http://research.gold.ac.uk/17267/1/2013_EvoMUSART_paintings_April%283-5%29.pdf}} non-photorealistic images are produced using SDS algorithm which, in the context of this work, is responsible for colour attention.

The "computational creativity" of the above-mentioned systems are discussed inal-Rifaie, Mohammad Majid, Mark JM Bishop, and Ahmed Aber. "[http://eprints.gold.ac.uk/6939/1/AISB_On_Creativity_of_the_swarms_ISBN%3A_978-1-908187-03-1.pdf Creative or Not? Birds and Ants Draw with Muscle]." Proceedings of AISB'11 Computing and Philosophy (2011): 23-30.al-Rifaie MM, Bishop M (2013) [https://www.aaai.org/ocs/index.php/SSS/SSS13/paper/viewFile/5728/5925 Swarm intelligence and weak artificial creativity] {{Webarchive|url=https://web.archive.org/web/20190811190817/https://www.aaai.org/ocs/index.php/SSS/SSS13/paper/viewFile/5728/5925 |date=2019-08-11 }}. In: The Association for the Advancement of Artificial Intelligence (AAAI) 2013: Spring Symposium, Stanford University, Palo Alto, California, U.S.A., pp 14–19 through the two prerequisites of creativity (i.e. freedom and constraints) within the swarm intelligence's two infamous phases of exploration and exploitation.

Michael Theodore and Nikolaus Correll use swarm intelligent art installation to explore what it takes to have engineered systems to appear lifelike.{{Cite web|url=https://www.colorado.edu/lab/correll/|title=Correll lab|website=Correll lab}}

{{div col}}

• Maurice Clerc (mathematician)
• Nikolaus Correll
• Marco Dorigo
• Russell C. Eberhart
• Luca Maria Gambardella
• James Kennedy
• Alcherio Martinoli
• Craig Reynolds
• Magnus Egerstedt
• P. N. Suganthan

{{div col end}}

{{div col}}

• Artificial immune systems
• Collaborative intelligence
• Collective effervescence
• Group mind (science fiction)
• Cellular automaton
• Complex systems
• Differential evolution
• Dispersive flies optimisation
• Distributed artificial intelligence
• Evolutionary computation
• Global brain
• Harmony search
• Language
• Multi-agent system
• Myrmecology
• Promise theory
• Quorum sensing
• Population protocol
• Reinforcement learning
• Rule 110
• Self-organized criticality
• Spiral optimization algorithm
• Stochastic optimization
• Swarm Development Group
• Swarm robotic platforms
• Swarming
• SwisTrack
• Symmetry breaking of escaping ants
• The Wisdom of Crowds
• Wisdom of the crowd

{{div col end}}

{{Reflist|30em}}

• {{cite book |title=Swarm Intelligence: From Natural to Artificial Systems |first1=Eric |last1=Bonabeau |first2=Marco |last2=Dorigo |first3=Guy |last3=Theraulaz |year=1999 |publisher=Oup USA |isbn=978-0-19-513159-8}}
• {{cite book |title=Swarm Intelligence |first1=James |last1=Kennedy |first2=Russell C. |last2=Eberhart |isbn=978-1-55860-595-4|date=2001-04-09 |publisher=Morgan Kaufmann }}
• {{cite book |title=Fundamentals of Computational Swarm Intelligence |first=Andries |last=Engelbrecht |publisher=Wiley & Sons |isbn=978-0-470-09191-3|date=2005-12-16 }}

{{Sisterlinks|Swarm Intelligence}}

• Marco Dorigo and Mauro Birattari (2007). [http://www.scholarpedia.org/article/Swarm_intelligence "Swarm intelligence"] in Scholarpedia
• Antoinette Brown. [https://web.archive.org/web/20161130043122/https://medium.com/@antoinettebromwn/swarm-intelligence-review-eeb74beddbad#.xr4gpi4c6 Swarm Intelligence]

{{animal cognition}}

{{collective animal behaviour}}

{{optimization algorithms|state=collapsed}}

{{Authority control}}

{{DEFAULTSORT:Swarm Intelligence}}

Category:Collective intelligence

Category:Intelligence by type

Category:Multi-agent systems