Misplaced Pages

Latest revision as of 14:47, 6 November 2024

This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. Find sources: "Swarm intelligence" – news · newspapers · books · scholar · JSTOR (March 2023) (Learn how and when to remove this message)

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.

SI systems consist typically of a population of simple agents or boids interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Examples of swarm intelligence in natural systems include ant colonies, bee colonies, bird flocking, hawks hunting, animal herding, bacterial growth, fish schooling and microbial intelligence.

The application of swarm principles to robots is called swarm robotics while swarm intelligence refers to the more general set of algorithms. Swarm prediction has been used in the context of forecasting problems. Similar approaches to those proposed for swarm robotics are considered for genetically modified organisms in synthetic collective intelligence.

Models of swarm behavior

Boids (Reynolds 1987)

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

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.

Self-propelled particles (Vicsek et al. 1995)

Self-propelled particles (SPP), also referred to as the Vicsek model, was introduced in 1995 by Vicsek et al. 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. SPP models predict that swarming animals share certain properties at the group level, regardless of the type of animals in the swarm. 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.

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. 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. For algorithms published since that time, see List of metaphor-based metaheuristics.

Metaheuristics lack a confidence in a solution. 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. 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.

Ant colony optimization (Dorigo 1992)

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.

Particle swarm optimization (Kennedy, Eberhart & Shi 1995)

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. 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.

Artificial bee colony algorithm (Karaboga 2005)

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 (2015)

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 ASI has been used for a wide range of applications, from enabling business teams to generate highly accurate financial forecasts to enabling sports fans to outperform Vegas betting markets. 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.

Applications

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. Conversely al-Rifaie and Aber have used stochastic diffusion search to help locate tumours. Swarm intelligence (SI) is increasingly applied in Internet of Things (IoT) systems, and by association to Intent-Based Networking (IBN), due to its ability to handle complex, distributed tasks through decentralized, self-organizing algorithms. Swarm intelligence has also been applied for data mining and cluster analysis. Ant-based models are further subject of modern management theory.

Ant-based routing

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.

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.

Crowd simulation

Artists are using swarm technology as a means of creating complex interactive systems or simulating crowds.

Instances

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.

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

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).

Human swarming

Networks of distributed users can be organized into "human swarms" through the implementation of real-time closed-loop control systems. 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. The collective intelligence of the group often exceeds the abilities of any one member of the group.

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.

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.

Swarm grammars

Swarm grammars are swarms of stochastic grammars that can be evolved to describe complex properties such as found in art and architecture. 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.

Swarmic art

In a series of works, al-Rifaie et al. 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.

A more recent work of al-Rifaie et al., "Swarmic Sketches and Attention Mechanism", 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", 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 in 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.

Notable researchers

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

See also

• 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

References

1. Beni, G.; Wang, J. (1993). "Swarm Intelligence in Cellular Robotic Systems". Proceed. NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June 26–30 (1989). Berlin, Heidelberg: Springer. pp. 703–712. doi:10.1007/978-3-642-58069-7_38. ISBN 978-3-642-63461-1.
2. Beni, G. (1989). "The concept of cellular robotic system". Proceedings IEEE International Symposium on Intelligent Control 1988. IEEE. pp. 57–62. doi:10.1109/ISIC.1988.65405. ISBN 978-0-8186-2012-6.
3. Hu, J.; Turgut, A.; Krajnik, T.; Lennox, B.; Arvin, F., "Occlusion-Based Coordination Protocol Design for Autonomous Robotic Shepherding Tasks" IEEE Transactions on Cognitive and Developmental Systems, 2020.
4. Gad, Ahmed G. (2022-08-01). "Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review". Archives of Computational Methods in Engineering. 29 (5): 2531–2561. doi:10.1007/s11831-021-09694-4. ISSN 1886-1784.
5. Hu, J.; Bhowmick, P.; Jang, I.; Arvin, F.; Lanzon, A., "A Decentralized Cluster Formation Containment Framework for Multirobot Systems" IEEE Transactions on Robotics, 2021.
6. Solé R, Rodriguez-Amor D, Duran-Nebreda S, Conde-Pueyo N, Carbonell-Ballestero M, Montañez R (October 2016). "Synthetic Collective Intelligence". BioSystems. 148: 47–61. Bibcode:2016BiSys.148...47S. doi:10.1016/j.biosystems.2016.01.002. hdl:10630/32279. PMID 26868302.
7. ^ Reynolds, Craig (1987). "Flocks, herds and schools: A distributed behavioral model". Proceedings of the 14th annual conference on Computer graphics and interactive techniques. Association for Computing Machinery. pp. 25–34. CiteSeerX 10.1.1.103.7187. doi:10.1145/37401.37406. ISBN 978-0-89791-227-3. S2CID 546350.
8. Banks, Alec; Vincent, Jonathan; Anyakoha, Chukwudi (July 2007). "A review of particle swarm optimization. Part I: background and development". Natural Computing. 6 (4): 467–484. CiteSeerX 10.1.1.605.5879. doi:10.1007/s11047-007-9049-5. S2CID 2344624.
9. Vicsek, T.; Czirok, A.; Ben-Jacob, E.; Cohen, I.; Shochet, O. (1995). "Novel type of phase transition in a system of self-driven particles". Physical Review Letters. 75 (6): 1226–1229. arXiv:cond-mat/0611743. Bibcode:1995PhRvL..75.1226V. doi:10.1103/PhysRevLett.75.1226. PMID 10060237. S2CID 15918052.
10. Czirók, A.; Vicsek, T. (2006). "Collective behavior of interacting self-propelled particles". Physica A. 281 (1): 17–29. arXiv:cond-mat/0611742. Bibcode:2000PhyA..281...17C. doi:10.1016/S0378-4371(00)00013-3. S2CID 14211016.
11. Buhl, J.; Sumpter, D.J.T.; Couzin, D.; Hale, J.J.; Despland, E.; Miller, E.R.; Simpson, S.J.; et al. (2006). "From disorder to order in marching locusts" (PDF). Science. 312 (5778): 1402–1406. Bibcode:2006Sci...312.1402B. doi:10.1126/science.1125142. PMID 16741126. S2CID 359329. Archived from the original (PDF) on 2011-09-29. Retrieved 2011-10-07.
12. Toner, J.; Tu, Y.; Ramaswamy, S. (2005). "Hydrodynamics and phases of flocks" (PDF). Annals of Physics. 318 (1): 170–244. Bibcode:2005AnPhy.318..170T. doi:10.1016/j.aop.2005.04.011. Archived from the original (PDF) on 2011-07-18. Retrieved 2011-10-07.
13. Bertin, E.; Droz, M.; Grégoire, G. (2009). "Hydrodynamic equations for self-propelled particles: microscopic derivation and stability analysis". J. Phys. A. 42 (44): 445001. arXiv:0907.4688. Bibcode:2009JPhA...42R5001B. doi:10.1088/1751-8113/42/44/445001. S2CID 17686543.
14. Li, Y.X.; Lukeman, R.; Edelstein-Keshet, L.; et al. (2007). "Minimal mechanisms for school formation in self-propelled particles" (PDF). Physica D: Nonlinear Phenomena. 237 (5): 699–720. Bibcode:2008PhyD..237..699L. doi:10.1016/j.physd.2007.10.009. Archived from the original (PDF) on 2011-10-01.
15. Lones, Michael A. (2014). "Metaheuristics in nature-inspired algorithms". Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation (PDF). pp. 1419–1422. CiteSeerX 10.1.1.699.1825. doi:10.1145/2598394.2609841. ISBN 9781450328814. S2CID 14997975.
16. ^ Silberholz, John; Golden, Bruce; Gupta, Swati; Wang, Xingyin (2019), Gendreau, Michel; Potvin, Jean-Yves (eds.), "Computational Comparison of Metaheuristics", Handbook of Metaheuristics, International Series in Operations Research & Management Science, Cham: Springer International Publishing, pp. 581–604, doi:10.1007/978-3-319-91086-4_18, ISBN 978-3-319-91086-4, S2CID 70030182
17. Burke, Edmund; De Causmaecker, Patrick; Petrovic, Sanja; Berghe, Greet Vanden (2004), Resende, Mauricio G. C.; de Sousa, Jorge Pinho (eds.), "Variable Neighborhood Search for Nurse Rostering Problems", Metaheuristics: Computer Decision-Making, Applied Optimization, Boston, MA: Springer US, pp. 153–172, doi:10.1007/978-1-4757-4137-7_7, ISBN 978-1-4757-4137-7
18. Fu, Michael C. (2002-08-01). "Feature Article: Optimization for simulation: Theory vs. Practice". INFORMS Journal on Computing. 14 (3): 192–215. doi:10.1287/ijoc.14.3.192.113. ISSN 1091-9856.
19. Dorigo, Marco; Birattari, Mauro; Stutzle, Thomas (November 2006). "Ant colony optimization". IEEE Computational Intelligence Magazine. 1 (4): 28–39. doi:10.1109/MCI.2006.329691. ISSN 1556-603X.
20. Hayes-RothFrederick (1975-08-01). "Review of "Adaptation in Natural and Artificial Systems by John H. Holland", The U. of Michigan Press, 1975". ACM SIGART Bulletin (53): 15. doi:10.1145/1216504.1216510. S2CID 14985677.
21. Resende, Mauricio G.C.; Ribeiro, Celso C. (2010), Gendreau, Michel; Potvin, Jean-Yves (eds.), "Greedy Randomized Adaptive Search Procedures: Advances, Hybridizations, and Applications", Handbook of Metaheuristics, International Series in Operations Research & Management Science, Boston, MA: Springer US, pp. 283–319, doi:10.1007/978-1-4419-1665-5_10, ISBN 978-1-4419-1665-5
22. Kudelić, Robert; Ivković, Nikola (2019-05-15). "Ant inspired Monte Carlo algorithm for minimum feedback arc set". Expert Systems with Applications. 122: 108–117. doi:10.1016/j.eswa.2018.12.021. ISSN 0957-4174. S2CID 68071710.
23. Ant Colony Optimization by Marco Dorigo and Thomas Stützle, MIT Press, 2004. ISBN 0-262-04219-3
24. Parsopoulos, K. E.; Vrahatis, M. N. (2002). "Recent Approaches to Global Optimization Problems Through Particle Swarm Optimization". Natural Computing. 1 (2–3): 235–306. doi:10.1023/A:1016568309421. S2CID 4021089.
25. Particle Swarm Optimization by Maurice Clerc, ISTE, ISBN 1-905209-04-5, 2006.
26. Rosenberg, Louis (2015-07-20). "Human Swarms, a real-time method for collective intelligence". 07/20/2015-07/24/2015. Vol. 27. pp. 658–659. doi:10.7551/978-0-262-33027-5-ch117. ISBN 9780262330275.
27. Rosenberg, Louis; Willcox, Gregg (2020). "Artificial Swarm Intelligence". In Bi, Yaxin; Bhatia, Rahul; Kapoor, Supriya (eds.). Intelligent Systems and Applications. Advances in Intelligent Systems and Computing. Vol. 1037. Springer International Publishing. pp. 1054–1070. doi:10.1007/978-3-030-29516-5_79. ISBN 9783030295165. S2CID 195258629.
28. Metcalf, Lynn; Askay, David A.; Rosenberg, Louis B. (2019). "Keeping Humans in the Loop: Pooling Knowledge through Artificial Swarm Intelligence to Improve Business Decision Making". California Management Review. 61 (4): 84–109. doi:10.1177/0008125619862256. ISSN 0008-1256. S2CID 202323483.
29. Schumann, Hans; Willcox, Gregg; Rosenberg, Louis; Pescetelli, Niccolo (2019). ""Human Swarming" Amplifies Accuracy and ROI when Forecasting Financial Markets". 2019 IEEE International Conference on Humanized Computing and Communication (HCC). pp. 77–82. doi:10.1109/HCC46620.2019.00019. ISBN 978-1-7281-4125-1. S2CID 209496644.
30. Bayern, Macy (September 4, 2018). "How AI systems beat Vegas oddsmakers in sports forecasting accuracy". TechRepublic. Retrieved 2018-09-10.
31. ^ Scudellari, Megan (2018-09-13). "AI-Human "Hive Mind" Diagnoses Pneumonia". IEEE Spectrum: Technology, Engineering, and Science News. Retrieved 2019-07-20.
32. ^ Rosenberg, Louis; Lungren, Matthew; Halabi, Safwan; Willcox, Gregg; Baltaxe, David; Lyons, Mimi (November 2018). "Artificial Swarm Intelligence employed to Amplify Diagnostic Accuracy in Radiology". 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). Vancouver, BC: IEEE. pp. 1186–1191. doi:10.1109/IEMCON.2018.8614883. ISBN 9781538672662. S2CID 58675679.
33. Rosenberg, Louis (October 13, 2021). "Swarm intelligence: AI inspired by honeybees can help us make better decisions". Big Think.
34. Lewis, M. Anthony; Bekey, George A. "The Behavioral Self-Organization of Nanorobots Using Local Rules". Proceedings of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems.
35. al-Rifaie, M.M.; Aber, A. "Identifying metastasis in bone scans with Stochastic Diffusion Search". Proc. IEEE Information Technology in Medicine and Education, ITME. 2012: 519–523.
36. al-Rifaie, Mohammad Majid, Ahmed Aber, and Ahmed Majid Oudah. "Utilising Stochastic Diffusion Search to identify metastasis in bone scans and microcalcifications on mammographs." In Bioinformatics and Biomedicine Workshops (BIBMW), 2012 IEEE International Conference on, pp. 280-287. IEEE, 2012.
37. Sun, Weifeng; Tang, Min; Zhang, Lijun; Huo, Zhiqiang; Shu, Lei (January 2020). "A Survey of Using Swarm Intelligence Algorithms in IoT". Sensors. 20 (5): 1420. Bibcode:2020Senso..20.1420S. doi:10.3390/s20051420. ISSN 1424-8220. PMC 7085620. PMID 32150912.
38. Abualigah, Laith; Falcone, Deborah; Forestiero, Agostino (2023-05-29). "Swarm Intelligence to Face IoT Challenges". Computational Intelligence and Neuroscience. 2023: 4254194. doi:10.1155/2023/4254194. ISSN 1687-5265. PMC 10241578. PMID 37284052.
39. "Intent-Based Networking for the Internet of Things | Frontiers Research Topic". www.frontiersin.org. Retrieved 2024-08-14.
40. Martens, D.; Baesens, B.; Fawcett, T. (2011). "Editorial Survey: Swarm Intelligence for Data Mining". Machine Learning. 82 (1): 1–42. doi:10.1007/s10994-010-5216-5.
41. Thrun, M.; Ultsch, A. (2021). "Swarm Intelligence for Self-Organized Clustering". Artificial Intelligence. 290: 103237. arXiv:2106.05521. doi:10.1016/j.artint.2020.103237. S2CID 213923899.
42. Fladerer, Johannes-Paul; Kurzmann, Ernst (November 2019). THE WISDOM OF THE MANY : how to create self -organisation and how to use collective... intelligence in companies and in society from mana. BOOKS ON DEMAND. ISBN 9783750422421.
43. Whitaker, R.M., Hurley, S.. An agent based approach to site selection for wireless networks. Proc ACM Symposium on Applied Computing, pp. 574–577, (2002).
44. "Planes, Trains and Ant Hills: Computer scientists simulate activity of ants to reduce airline delays". Science Daily. April 1, 2008. Archived from the original on November 24, 2010. Retrieved December 1, 2010.
45. Mahant, Manish; Singh Rathore, Kalyani; Kesharwani, Abhishek; Choudhary, Bharat (2012). "A Profound Survey on Swarm Intelligence". International Journal of Advanced Computer Research. 2 (1). Retrieved 3 October 2022.
46. Miller, Peter (2010). The Smart Swarm: How understanding flocks, schools, and colonies can make us better at communicating, decision making, and getting things done. New York: Avery. ISBN 978-1-58333-390-7.
47. Oxenham, Simon. "Why bees could be the secret to superhuman intelligence". Retrieved 2017-01-20.
48. Rosenberg, L.; Pescetelli, N.; Willcox, G. (October 2017). "Artificial Swarm Intelligence amplifies accuracy when predicting financial markets". 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON). pp. 58–62. doi:10.1109/UEMCON.2017.8248984. ISBN 978-1-5386-1104-3. S2CID 21312426.
49. "Smarter as a group: How swarm intelligence picked Derby winners". Christian Science Monitor.
50. "AI startup taps human 'swarm' intelligence to predict winners". CNET.
51. Rosenberg, Louis (2016-02-12). "Artificial Swarm Intelligence, a human-in-the-loop approach to A.I." Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. AAAI'16. Phoenix, Arizona: AAAI Press: 4381–4382.
52. "Unanimous AI achieves 22% more accurate pneumonia diagnoses". VentureBeat. 2018-09-10. Retrieved 2019-07-20.
53. "A Swarm of Insight - Radiology Today Magazine". www.radiologytoday.net. Retrieved 2019-07-20.
54. Shah, Rutwik; Astuto, Bruno; Gleason, Tyler; Fletcher, Will; Banaga, Justin; Sweetwood, Kevin; Ye, Allen; Patel, Rina; McGill, Kevin; Link, Thomas; Crane, Jason (2021-09-06). "Utilizing a digital swarm intelligence platform to improve consensus among radiologists and exploring its applications". arXiv:2107.07341 .
55. Shah, Rutwik; Astuto Arouche Nunes, Bruno; Gleason, Tyler; Fletcher, Will; Banaga, Justin; Sweetwood, Kevin; Ye, Allen; Patel, Rina; McGill, Kevin; Link, Thomas; Crane, Jason; Pedoia, Valentina; Majumdar, Sharmila (April 4, 2023). "Utilizing a Digital Swarm Intelligence Platform to Improve Consensus Among Radiologists and Exploring Its Applications". Journal of Digital Imaging. 36 (2): 401–413. doi:10.1007/s10278-022-00662-3. PMC 10039189. PMID 36414832.
56. vonMammen, Sebastian; Jacob, Christian (2009). "The evolution of swarm grammars -- growing trees, crafting art and bottom-up design". IEEE Computational Intelligence Magazine. 4 (3): 10–19. CiteSeerX 10.1.1.384.9486. doi:10.1109/MCI.2009.933096. S2CID 17882213.
57. du Castel, Bertrand (15 July 2015). "Pattern Activation/Recognition Theory of Mind". Frontiers in Computational Neuroscience. 9 (90): 90. doi:10.3389/fncom.2015.00090. PMC 4502584. PMID 26236228.
58. ^ al-Rifaie, MM; Bishop, J.M.; Caines, S. (2012). "Creativity and Autonomy in Swarm Intelligence Systems" (PDF). Cognitive Computation. 4 (3): 320–331. doi:10.1007/s12559-012-9130-y. S2CID 942335.
59. Deleuze G, Guattari F, Massumi B. A thousand plateaus. Minneapolis: University of Minnesota Press; 2004.
60. Al-Rifaie, Mohammad Majid; Bishop, John Mark (2013). "Swarmic Sketches and Attention Mechanism" (PDF). Evolutionary and Biologically Inspired Music, Sound, Art and Design (PDF). Lecture Notes in Computer Science. Vol. 7834. pp. 85–96. doi:10.1007/978-3-642-36955-1_8. ISBN 978-3-642-36954-4.
61. Al-Rifaie, Mohammad Majid; Bishop, John Mark (2013). "Swarmic Paintings and Colour Attention" (PDF). Evolutionary and Biologically Inspired Music, Sound, Art and Design (PDF). Lecture Notes in Computer Science. Vol. 7834. pp. 97–108. doi:10.1007/978-3-642-36955-1_9. ISBN 978-3-642-36954-4.
62. al-Rifaie, Mohammad Majid, Mark JM Bishop, and Ahmed Aber. "Creative or Not? Birds and Ants Draw with Muscle." Proceedings of AISB'11 Computing and Philosophy (2011): 23-30.
63. al-Rifaie MM, Bishop M (2013) Swarm intelligence and weak artificial creativity Archived 2019-08-11 at the Wayback Machine. In: The Association for the Advancement of Artificial Intelligence (AAAI) 2013: Spring Symposium, Stanford University, Palo Alto, California, U.S.A., pp 14–19
64. "Correll lab". Correll lab.

Further reading

• Bonabeau, Eric; Dorigo, Marco; Theraulaz, Guy (1999). Swarm Intelligence: From Natural to Artificial Systems. Oup USA. ISBN 978-0-19-513159-8.
• Kennedy, James; Eberhart, Russell C. (2001-04-09). Swarm Intelligence. Morgan Kaufmann. ISBN 978-1-55860-595-4.
• Engelbrecht, Andries (2005-12-16). Fundamentals of Computational Swarm Intelligence. Wiley & Sons. ISBN 978-0-470-09191-3.

External links

• Marco Dorigo and Mauro Birattari (2007). "Swarm intelligence" in Scholarpedia
• Antoinette Brown. Swarm Intelligence

v t e Swarming
Biological swarming	Agent-based model in biology Collective animal behavior Droving Flock flocking sort sol Herd herd behavior Locust Mixed-species foraging flock Mobbing behavior feeding frenzy Pack pack hunter Patterns of self-organization in ants ant mill symmetry breaking of escaping ants Shoaling and schooling bait ball Swarming behaviour Swarming (honey bee) Swarming motility
Animal migration	Animal migration altitudinal tracking history coded wire tag Bird migration flyways reverse migration Cell migration Fish migration diel vertical Lessepsian salmon run sardine run Homing natal philopatry Insect migration butterflies monarch Sea turtle migration
Swarm algorithms	Agent-based models Ant colony optimization Boids Crowd simulation Particle swarm optimization Swarm intelligence Swarm (simulation)
Collective motion	Active matter Collective motion Self-propelled particles clustering Vicsek model BIO-LGCA
Swarm robotics	Ant robotics Microbotics Nanorobotics Swarm robotics Symbrion
Related topics	Allee effect Animal navigation Collective intelligence Decentralised system Eusociality Group size measures Microbial intelligence Mutualism Predator satiation Quorum sensing Spatial organization Stigmergy Military swarming Task allocation and partitioning of social insects

v t e Optimization: Algorithms, methods, and heuristics
Unconstrained nonlinear Functions Golden-section search Powell's method Line search Nelder–Mead method Successive parabolic interpolation Gradients Convergence Trust region Wolfe conditions Quasi–Newton Berndt–Hall–Hall–Hausman Broyden–Fletcher–Goldfarb–Shanno and L-BFGS Davidon–Fletcher–Powell Symmetric rank-one (SR1) Other methods Conjugate gradient Gauss–Newton Gradient Mirror Levenberg–Marquardt Powell's dog leg method Truncated Newton Hessians Newton's method
Constrained nonlinear General Barrier methods Penalty methods Differentiable Augmented Lagrangian methods Sequential quadratic programming Successive linear programming
Convex optimization Convex minimization Cutting-plane method Reduced gradient (Frank–Wolfe) Subgradient method Linear and quadratic Interior point Affine scaling Ellipsoid algorithm of Khachiyan Projective algorithm of Karmarkar Basis-exchange Simplex algorithm of Dantzig Revised simplex algorithm Criss-cross algorithm Principal pivoting algorithm of Lemke Active-set method
Combinatorial Paradigms Approximation algorithm Dynamic programming Greedy algorithm Integer programming Branch and bound/cut Graph algorithms Minimum spanning tree Borůvka Prim Kruskal Shortest path Bellman–Ford SPFA Dijkstra Floyd–Warshall Network flows Dinic Edmonds–Karp Ford–Fulkerson Push–relabel maximum flow
Metaheuristics Evolutionary algorithm Hill climbing Local search Parallel metaheuristics Simulated annealing Spiral optimization algorithm Tabu search
Software

• Nature-inspired metaheuristics
• Collective intelligence
• Intelligence by type
• Multi-agent systems