Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. SI systems consist typically of a population of individuals interacting in network with one another and with their environment. The inspiration often comes from nature, especially biological systems. The individuals in Complex Adaptive Systems follow
- (Individual Behaviour) the individuals in a swarm have individual experiences, are localate at different location in a swarm, perceive different features in their environment.
- (Swarm Interaction) swarm interaction is determined by an input of information from the swarm and output information to the swarm the community of swarm indviduals listen/respond to.
- (Environment Interaction) Furthermore the behaviour of individuals is determined the interaction with the environment. Together with the with the interaction with the neighbours individual perceptions are propagated in the swarm as network of individuals.
All together the individueal behaviour is determined by individual perceptions in the swarm, the information from other individuals (like warnings about predators or information about accessible food). The swara intelligence arises from without a centralized control structure dictating how individuals in the swarm 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.
- 1 Swarm - Communities
- 2 Swarm Properties
- 3 Modelling of Swarm Behaviour
- 4 Swarm Intelligence in Human Population
- 5 Learning Task
- 6 From Learner to Author
- 7 ToDo for Learning Resource
- 8 Notable researchers
- 9 See also
- 10 References
- 11 Further reading
- 12 External links
- 13 Wikipedia Links
- 14 Wikipedia Categories
Swarm - Communities
- Swarm of Birds
- Ant Colony and Network Communication with Pheromones
- Slime Mold: Swarm behaviour of Unicellular Organisms
- Individual vs. Systems Performance: Swarm intelligence combines independent behaviour of individuals with a connect swarm behaviour. This subtopic explores that concept as introduction to Systems Thinking by application of the concept of performance indicators.
- algorithms, that apply the concept of swarm intelligence
- Swarm Intelligence and Innovation
- Neural Networks
Modelling of Swarm Behaviour
Swarm Intelligence in Human Population
- Connection of teams,
- Diversity of knowledge of individuals,
- sacrifice individual preferences and performance to swarm performance
Swarm Ignorance vs. Swarm Intelligence
In this learning task we introduce the notion of swarm ignorance. It is not official defined scientific term. It is artificial to contrast with swarm intelligence
- Assume lemmings that behave similar to the next member of the swarm and run jointly towards the edge of the cliff and do not change the swarm behaviour if the first lemmings fall down. Explain missing swarm behaviour or try to define the notion of "swarm ingnorance".
- Can you find examples in history (financial market, stock exchange, dictators, ...) that show aspects of swarm intelligence and swarm ignorance?
- (Psychology) Can you approach the "swarm ignorance" and "swarm intelligence" topic from the psychological perspective? What are drivers for swarm ignorance according to your definition?
Education and Capacity Building
- Explain the role of education and capacity building in the context of swarm intelligence and a human population/communities!
Sacrifice Specialities to defend the colony
Insects form colonies and specialize in certain areas:
- feeding the larvae,
- collecting food,
- soldier insects defend predators,
Try to find scientific evidence and examples in which insects sacrifice their specialities and act coherent in a swarm to defend the colony or respond jointly to a risk, the colony is exposed to!
Disruptive Forces for Swarm Performance
Swarms of individuals are generated because they seem to have a evolutionary benefit in comparision loose collection of independent individuals. This is a Systems Thinking task.
- Teams for Development and Innvoation
- Problem Solving teams,
- States with Democracy
- Teams in sports, that compete with other teams.
Disruptive forces for the swarm performance can come
- from outside, e.g.
- virus or communicable disease,
- competing football team,
- competing companies in a global market,
- from inside, e.g.
- developers, that push only their own agenda for personal benefit,
- offence players, that prefers to score a goal instead providing an opportunity for other team players in more successful position to score the goal,
- people, that commit a crime in a society.
The examples coming from different domains (research and development teams, sports, society, economics). All swarms have a certain vulnerability. All examples are NOT pure swarm intelligence applications. You might identify centralized control structures. Explain and compare the different approaches of a
- centralized response and
- swarm response
to disruptive forces for the community. How do the work together? What are the problems and challenges in the response methods?
- Look for ancient high developed cultures (e.g. Egypt, ...). Where did they evolve (access to water, rivers)? When did they evolve, e.g. when there was a high availability of water or when there were limited water resources and the communities need to develop intelligence strategies to distribute and share the resources? Derive your conclusions for the emergence of swarm intelligence in human society!
Information and Communication Technology
In biological swarms we can observe the coherence of swarm by the spatial connectivity. If we consider information and communication technology, we can consider swarm behaviour by application of mobile devices. Where do we find constructive swarm behaviour and the emergence of swarm intelligence? Where do we find disruptive aspects of swarm behaviour in collaborative networks? Yearly flu epidemics in a country has an impact on the performance of a company because the worker in team are sick? What are similarities and differences from biological and computer viruses in terms of their impact on the economic performance of a company? How does society and economy respond to that risk and how can swarm intelligence emerge with capacity building in the area of Risk Literacy?
From Learner to Author
Learning resources can guide the learner to become an author (see Learner2Author. Generate a quiz in Wikiversity for self-assessment is a task to answer the questions for yourself as learner and then create a quiz for it.
ToDo for Learning Resource
- decompose in submodule
- create a learning path, because digging into every single topic will create to much workload and the learner will loose the big picture of the topic
- create a quiz in Wikiversity for self-assessment.
- Bernstein, Jeremy. "Project Swarm". Report on technology inspired by swarms in nature.
- Bonabeau, Eric; Dorigo, Marco; Theraulaz, Guy (1999). Swarm Intelligence: From Natural to Artificial Systems. ISBN 0-19-513159-2. Complete bibliography
- Engelbrecht, Andries. Fundamentals of Computational Swarm Intelligence. Wiley & Sons. ISBN 0-470-09191-6.
- Fisher, L. (2009). The Perfect Swarm : The Science of Complexity in Everyday Life. Basic Books.
- Fister I, XS Yang, I Fister, J Brest and D Fister (2013) "A Brief Review of Nature-Inspired Algorithms for Optimization" Elektrotehniski Vestnik, 80 (3): 1–7.
- Horn, Eva; Gisi, Lucas (Ed.) Marco (2009). Schwärme – Kollektive ohne Zentrum. Eine Wissensgeschichte zwischen Leben und Information. Bielefeld. ISBN 978-3-8376-1133-5.
- Kaiser, Carolin; Kröckel, Johannes; Bodendorf, Freimut (2010). "Swarm Intelligence for Analyzing Opinions in Online Communities". Proceedings of the 43rd Hawaii International Conference on System Sciences. pp. 1–9.
- Kennedy, James; Eberhart, Russell C.. Swarm Intelligence. ISBN 1-55860-595-9.
- Miller, Peter (July 2007), "Swarm Theory", National Geographic Magazine, http://www7.nationalgeographic.com/ngm/0707/feature5/
- Resnick, Mitchel. Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds. ISBN 0-262-18162-2.
- Ridge, E.; Curry, E. (2007). "A roadmap of nature-inspired systems research and development". Multiagent and Grid Systems 3 (1): 3–8. CiteSeerx: 10.1.1.67.1030.
- Ridge, E.; Kudenko, D.; Kazakov, D.; Curry, E. (2005). "Moving Nature-Inspired Algorithms to Parallel, Asynchronous and Decentralised Environments". Self-Organization and Autonomic Informatics (I) 135: 35–49. CiteSeerx: 10.1.1.64.3403.
- Swarm Intelligence (journal). Chief Editor: Marco Dorigo. Springer New York. ISSN 1935-3812 (Print) 1935-3820 (Online) 
- Waldner, Jean-Baptiste (2007). Nanocomputers and Swarm Intelligence. ISTE. ISBN 978-1-84704-002-2.
- Yang, Xin-She (2011). "Metaheuristic Optimization". Scholarpedia 6 (8): 11472. doi:10.4249/scholarpedia.11472. http://www.scholarpedia.org/article/Metaheuristic_Optimization.
- Zimmer, Carl (November 13, 2007). "From Ants to People: an Instinct to Swarm". The New York Times.
- Detailed theory and software
- ABC http://mf.erciyes.edu.tr/abc/
- ACO http://iridia.ulb.ac.be/~mdorigo/ACO/aco-code/public-software.html
- DSA http://www.pinarcivicioglu.com/ds.html
- PSO http://www.particleswarm.info/Programs.html
- Wikipedia:Animal cognition
- Wikipedia:Collective animal behaviour
- Wikipedia:Optimization algorithms
- Wikipedia:Mastermind group