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Nikolaos S. Karastathis (2008). A quite short introduction to the organisational and business applications of swarm intelligence and associated evolutionary computation techniques. Self-published online. Accessed 8 January 2008. Available at: http://cosmoswiki.org/index.php/A_quite_short_introduction_to_the_organisational_and_business_applications_of_swarm_intelligence_and_associated_evolutionary_computation_techniques
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Nature of the document: This is intended as a quick introduction to the use of swarm intelligence and similar techniques in companies. It is intended to be understood by a general audience.
Document identifier: cr2c1v0.1a1
A quite short introduction to the organisational and business applications of swarm intelligence and associated evolutionary computation techniques
by Nikolaos S. Karastathis
7 January 2008
1 1: Prologue
Many problems faced by businesses and other organisations can be solved with mathematical models [1] and algorithms [2]. Recently techniques borrowed from the fields of swarm intelligence [3] and evolutionary [4] or natural computation [5] have been applied on business problems, with promising results.Swarm intelligence fits well with recent calls for decentralisation in organisations [6], and the term has started to enter the non-specialist vocabulary [7]. It has also been termed as collective intelligence (Engelbrecht [2005] 2006:2). For the purposes of this text, swarm intelligence is interchangeable with computational swarm intelligence [19].
1.1 1.1: What swarm intelligence is
The essence of swarm intelligence the management of complex systems where the interactions between the system's entities are minimal (Fleischer 2003:1), but there is no widely accepted exact definition or mathematical characterisation [8] (ibid). The concept and the term were introduced by Gerardo Beni, Suzanne Hackwood, and Jing Wang in 1989 (Beni and Wang 1989 cited in Martinoli 2001 cited in Ugur circa 2006 and MIT Press 2006).1.2 1.2: How swarm intelligence is being used
Swarm intelligence is used to improve the management of a large number of interacting entities (Fleischer 2003:1). Some applications of swarm intelligence include computer networks (ibid), satellite constellations (ibid), telephone networks (Miller 2007), robotics (ibid), the military [9] (ibid), railroad yards (Sabino et al 2006), traffic lights control (Oliveira and Bazzan 2006), simulation and modelling of criminal activity (Melo et al 2006), data mining (Abraham et al 2006), electric or hybrid vehicle battery pack state of charge [12] (Eberhart and Kennedy 2001:xviii), human performance assessment [12] (ibid), and human tremour diagnosis [12] (ibid). It is obvious that the applications are transdisciplinary, and Edelen (2003:1) observes that the 'applications of swarm intelligence have propagated beyond the fields of engineering and computer science into business, telecommunications, finance, social psychology, etc'.2 2: Applications in organisations
Until 1999 there have been very few applications of swarm intelligence (Bonabeau et al 1999:7 [10]), but this is not the case today. An overview of the current business and organisational applications is given here.2.1 2.1: Applications in logistics and supply chain management
Some problems in logistics [13], such as Facility Planning and Vehicle Routing Problems, have been found to be NP-hard [14] (Cheung 2005).2.1.1 2.1.1: Applications of ant colony optimisation in logistics
2.1.2 2.1.2: Applications of particle swarm optimisation in logistics
2.1.3 2.1.3: Applications of genetic algorithms in logistics
2.1.3.1 Location and allocation problem
The location and allocation problem is a classic problem in logistics and has been approached with various methods [15]. However, when the lengths of delivery routes are not Euclidean [16] and the total cost is not convex [17], then a method utilising genetic algorithms has to be used (Cheung et al 2001:160).3 3: A brief technical overview of swarm intelligence
In 1999 swarm intelligence was still considered hard to implement (Bonabeau et al 1999:7 [10]), but from 2001 the field has developed significantly and is now considered 'quite easy to program' [11] (Eberhart and Kennedy 2001:xix).3.1 3.1: Ant colony optimisation
Ant colony optimisation is the most successful ant algorithm (Dorigo and Stutzle 2004).3.2 3.2: Particle swarm optimisation
Particle swarm optimisation was first published in 1995 (Eberhart and Kennedy 1995a, Eberhart and Kennedy 1995b, both cited in Clerc [2005] 2006:87), but the modern particle swarm optimisation has changed a lot and the original is no longer used in practice (Clerc [2005] 2006:87).3.3 3.3: Genetic algorithms
4 4: Current research and unsolved problems
Holland ([1991] 1992:px) wrote in 1991 [18] that 'the future for studies of adaptive systems looks bright'. How accurate was his prediction?4.1 4.1: The state of swarm intelligence research in 2004
At the end of 2004 an international collaboration, termed XPS (eXtended Particle Swarms) led by University of Essex was started (Clerc [2005] 2006:13).4.2 4.2: The state of swarm intelligence research in 2005
As of 2005, research in adaptive particle swarm optimisation was still insufficient (Clerc [2005] 2006:13).5 5: Epilogue
6 6: Endnotes
Information not essential to the main text is presented in endnotes. These are intented for introducing concepts that may be unknown to a general audience, and for mior clarifications. Endnotes can be skipped in their entirety by readers familiar with all concepts examined in the main text.[1] Operations Research (OR) and management science are the fields commonly associated with using mathematics and optimisation to solve business problems. See Wikipedia (2008a) for an overview, and Sodhi (2007) for some history.
[2] An algorithm is a recipe taking some input, processing it, and leading to an output. Computer software is one way to realise algorithms. See Wikipedia (2008b) for an overview.
[3] Swarm intelligence is part of natural computation, evolutionary computation, and artificial intelligence. For an overview of swarm intelligence see Wikipedia (2008c).
[4] Evolutionary computation is a subfield of artificial and computational intelligence using iteration, selection, and other techniques inspired by natural (Darwinian) evolution. For an overview see Wikipedia (2008d).
[5] Natural computation is the general term for computing inspired by natural processes. For a brief overview, see Wikipedia (2008e).
[6] Several authors of recent business books discuss how decentralisation could help companies: Brafman and Beckstrom (2006) discuss how "the absence of structure, leadership, and formal organization, once considered a weakness, has become a major asset" (ibid 2006:7); Abrahamson and Freedman (2006) claim that disorder can have benefits in businesses; Hamel (2007) writes about how bureaucracy stifles innovation; Tapscott and Williams ([2006] 2007) point out that mass collaboration over the Internet can change the way businesses operate. Malone (2004 cited in Gloor 2006:3) suggested that managers need to depart from the traditional command and control approach towards a management style emphasising coordination and cultivation.
[7] The terms 'swarm intelligence', 'swarm theory', 'swarm behaviour', or related ones have been appeared on technology discussion websites such as Slashdot (2003a, 2003b, 2006, 2007a, 2007b, 2007c, 2007d, 2007e), in popular science magazines such as National Geographic (Miller 2007), and in newspapers such as the New York Times (Zimmer 2007).
[8] Mathematical characterisation means a set of mathematical constructs (such as functions) that can elegantly and concisely define and describe a notion, idea, system, or technique.
[9] According to Miller (2007), US agency Darpa (Defense Advanced Research Projects Agency) has funded collaborative robotics projects using helicopters, aerocrafts, underwater gliders, and ground vehicles. Fleischer (2003:1) notes that modern warfare involves many interacting systems. It is natural that swarm intelligence could be useful in managing a swarm of robots in a battle zone.
[10] Kennedy and Eberhart (2001:xix) cite Dorigo et al (1999:7) in their work. However, the citations made here are directly from Dorigo et al (1999).
[11] In the context of computer science, 'to program' or 'to code' means to implement an algorithm into computer software.
[12] These are applications of particle swarm optimisation (Eberhart and Kennedy 2001:xviii).
[13] For an introduction to logistics see Wikipedia (2008f). For an introduction to supply chain management (SCM), which is closely related with logistics, see Wikipedia (2008g). Plenert (2007:6) notes that the one-word definition that captures the essence of supply chain management is movement, and clarifies that supply chain management is about tracking the flow of resources such as materials (including parts), information, and money.
[14] In layman's terms, an NP-hard problem is a problem that can be modelled mathematically and that no one has found a quick solution to it, which means that it is very hard to solve or takes too much time to find a solution even with a very fast computer, with our current (as of 2008) knowledge of algorithmics. NP-hard means 'nondeterministic polynomial time hard'. For a simple explanation of the concept, see Wikipedia (2007a). Garey and Johnson ([1979] 2003:113) formally define an NP-hard problem as follows: 'A string relation R is NP-hard if there is some NP-complete language L (itself stated as a string relation [...]) such as that 
. A search problem \Pi (under encoding scheme e) is said to be NP-hard if the string relation R[Π,e] is NP-hard'. In practice, this means that 'if a string relation R (or a search problem Π) is NP-hard, then it cannot be solved in polynomial time unless P = NP' (ibid). Note that the question of whether P is NP is still an open problem, as of 2008. For a brief introduction to the P = NP problem, see Wikipedia (2008h).
[15] Also known as the problem of the sizing and location of facilities (Cheung 2005:159). To put it simply, the problem is about how to keep costs at a minimum while deciding where to locate facilities and what capacity each facility should have or how to allocate customers to each facility. Devine and Lesso (1972 cited in ibid) first tried to solve an instance of this problem for oil platforms in offshore oil fields. As a p-median problem, it was solved by Hansen and Mladenovic (1997 cited in Cheung 2005:160) using Variable Neighbourhood Search [16]. A modern example of a Variable Search Neighbourhood algorithm (applied on the task allocation problem) can be seen in Lusa and Potts (2006).
[16] Euclidean distance (for a two-dimensional space) is the distance that can be measured with a ruler. See also Wikipedia (2007b).
[17] The complexity of a problem defined by a non-convex function can be significant because of the presence of many local optima. For an introduction to convex functions see Wikipedia (2007c).
[18] Holland wrote this in the 1991 preface to the 1992 edition of his 1975 book.
7 7: References
The references are ordered in alphabetic order. The last name of the author appears first.7.1 7.1: A
Abraham Ajith, Grosan Crina, and Ramos Vitorino (editors) (2006): Swarm Intelligence in Data Mining. Studies in computational intelligence, volume 34. Springer. ISBN 978-3-540-34955-6 [book].Abrahamson Eric and Freedman, David H. (2006): A perfect mess - the hidden benefits of disorder. Orion Publishing Group Ltd (London, England, UK). ISBN-13 978-0-29785204-9, ISBN 0-297-85204-3 [book].
7.2 7.2: B
Beni Gerardo and Wang Jing (1989): Swarm Intelligence. Proceedings of the Seventh Annual Meeting of the Robotics Society of Japan, pp 425-428. Tokyo, Japan.Bonabeau Eric, Dorigo Marco, Theraulaz Guy (1999): Swarm intelligence - from natural to artificial systems. Santa Fe Institute, studies in the sciences of complexity. Oxford University Press Inc (New York, NY, USA). ISBN 0-19-513158-4 [book].
Brafman Ori and Beckstrom, Rod A. (2006): The Starfish and the Spider - the unstoppable power of leaderless organizations. Portfolio, Penguin Group USA Inc (New York City, NY, USA). ISBN 1-59184-143-7 [book].
7.3 7.3: C
Chan Chi Kin and Lee H.W.J. (editors) (2005): Successful strategies in supply chain management. Idea Group Publishing (London, England, UK). ISBN 1-59140-304-9 [book].Cheung Bernard K.-S. (2005): Genetic algorithm and other meta-heuristics: essential tools for solving modern supply chain management problems, in Chan and Lee (2005), Chapter VII, pages 144-173.
Clerc Maurice ([2005] 2006): Particle Swarm Optimization. Iste Ltd (London, England, UK). ISBN-13: 978-1-905209-04-0. ISBN 1-905209-04-5 [book].
7.4 7.4: D
Devine M. D. and Lesso W. G. (1972): Models for minimum cost development of offshore oil fields . Management Science 18, B378-B387.Dorigo Marco, Gambardella Luca Maria, Birattari Mauro, Martinoli Alcherio, Poli Riccardo, Stutzle Thomas (editors) (2006): Ant colony optimization and swarm intelligence, 5th international workshop, Ants 2006, Brussels, Belgium, September 2006, Proceedings, Lecture notes in computer science, Springer (Berlin, Germany). ISSN 0302-9743. ISBN 3-540-38482-0. ISBN-13: 978-3-540-38482-3 [book].
Dorigo Marco and Stutzle Thomas (2004): Ant colony optimization. MIT Press (Cambridge, MA, USA). ISBN 0-262-04219-3 [book].
7.5 7.5: E
Eberhart Russell C. and Kennedy James (1995a): A new optimizer using particle swarm theory. 6th international symposium on micromachine and human science, Nagoya, Japan, pp 39-43.Eberhart Russell C. and Kennedy James (1995b): Particle swarm optimization. IEEE international conference on neural networks, Perth, Australia, pp 1942-1948.
Eberhart Russell C. and Kennedy James (2001): Swarm Intelligence. Morgan Kauffman, Academic Press, Elsevier (San Diego, CA, USA; San Francisco, CA, USA; London, England, UK). ISBN-13: 978-1-55860-595-4. ISBN 1-55860-595-9.
Edelen Mark Russell (2003): Swarm intelligence and stigmergy: robotic implementation of foraging behaviour. Master of Science dissertation at University of Maryland. Accessed online 7 January 2008. Available at: https://drum.umd.edu/dspace/bitstream/1903/107/1/dissertation.pdf
Engelbrecht Andries P. ([2005] 2006): Fundamentals of computational swarm intellignece. Wiley. ISBN 0-470-09191-6 [book].
7.6 7.6: F
Fleischer Mark (2003): Foundations of swarm intelligence: from principles to practice. Conference on Swarming: Network Enabled C4ISR, January 13-14, 2003 McLean, Virginia, USA. arXiv submitted 2 February 2005, accessed 7 January 2008. Available at: http://arxiv.org/abs/nlin/0502003v17.7 7.7: G
Garey Michael R. and Johnson David S. ([1979] 2003): Computers and intractability - a guide to the theory of NP-completeness. W. H. Freeman and Company (New York, NY, USA). ISBN 0-7167-1045-5 [book].Gloor Peter A. (2006): Swarm creativity - competitive advantage through collaborative innovation networks. Oxford University Press Inc (New York, NY, USA). ISBN-13: 978-0-19-530412-1. ISBN 0-19-530412-8.
7.8 7.8: H
Hamel Gary (2007): The Future of Management. Harvard Business School Publishing (Boston, MA, USA). ISBN-13 978-1-4221-0250-3. ISBN 1-4221-0250-5 [book].Hansen P. and Mladenovic N. (1997): Variable neighbourhood search for the p-median. Location Sci, 5, 207-226.
Holland John H. ([1975] 1992): Adaptation in natural and artificial systems - an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press (Cambridge, MA, USA). ISBN 0-262-58111-6.
7.9 7.9: I
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Lusa Amaia and Potts Chris N. (2006): A variable neighbourhood search algorithm for the constrained task allocation problem [online]. Accessed 8 January 2008. Available at: https://upcommons.upc.edu/e-prints/handle/2117/312?mode=full&submit_simple=Mostrar+el+registre+complet+Dublin+Core+de+l%27%C3%ADtem7.13 7.13: M
Malone Thomas W. (2004): The future of work - how the new order of business will shape your organization, your management style, and your life. Harvard Business School Press (Boston, MA, USA). [book].Melo Adriano, Menezes Ronaldo, Furtado Vasco, and Coelho Andre L. V. (2006): Self-organized and social models of criminal activity in urban environments. Dorigo et al (2006) pp 518-519.
Miller Peter (2007): Swarm behaviour - Swarm theory [online]. National Geographic magazine, July 2007 (accessed 7 January 2008). Available at: http://ngm.nationalgeographic.com/ngm/0707/feature5/
MIT Press (2006): Artificial Life - collective complexity out of individual simplicity [online]. Accessed 7 Januiary 2008. Available at: http://www.mitpressjournals.org/doi/abs/10.1162/106454601753238663?journalCode=artl
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Oliveira Denise de and Bazzan Anna L. C. (2006): Traffic lights control with adaptive group formation based on swarm intelligence. Dorigo et al (2006) pp 520-521.7.16 7.16: P
Plenert Gerhart (2007): Reinventing lean - introducing lean management into the supply chain. Butterworth Heinemann, Elsevier (Burlington, MA, USA). ISBN-13: 978-0-12-370517-4. ISBN 0-12-370517-7 [book].7.17 7.17: Q
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Sabino Jodelson A., Stutzle Thomas, Birattari Mauro, and Leal Jose Eugenio (2006): ACO applied to switch engine scheduling in a railroad yard. Dorigo et al (2006) pp 502-503.Slashdot (2003a): Swarm Intelligence [online]. 25 February 2003 (accessed 7 January 2008). Available at: http://developers.slashdot.org/article.pl?sid=03/02/25/1758240&tid=156
Slashdot (2003b): Swarm theory applied to music [online]. 13 March 2003 (accessed 7 January 2008). Available at: http://science.slashdot.org/article.pl?sid=03/03/19/1958252&tid=141
Slashdot (2006): Robot swarm shifts heavy objects [online]. 18 October 2006 (accessed 7 January 2008). Available at: http://hardware.slashdot.org/article.pl?sid=06/10/18/1756237
Slashdot (2007a): Swarm theory makes National Geographic [online]. 5 July 2007 (accessed 7 January 2008). Available at: http://hardware.slashdot.org/article.pl?sid=07/07/05/1244224
Slashdot (2007b): Swarm OS demonstrated at Idea Festival [online]. 17 September 2007 (accessed 7 January 2008). Available at: http://hardware.slashdot.org/article.pl?sid=07/09/17/1724253
Slashdot (2007c): The rules of swarm [online]. 13 November 2007 (accessed 7 January 2008). Available at: http://science.slashdot.org/science/07/11/13/2319204.shtml
Slashdot (2007d): Robots assimilate into cockroach society [online]. 16 November 2007 (accessed 7 January 2008). Available at: http://hardware.slashdot.org/article.pl?sid=07/11/16/227204
Slashdot (2007e): Honeybees might prompt faster Internet server technology [online]. 19 November 2007 (accessed 7 January 2008). Available at: http://science.slashdot.org/article.pl?sid=07/11/19/0544204
Sodhi ManMohan S. (2007): What about the "O" in O.R.? [online]. December 2007 (accessed 7 January 2008). Available at: http://www.lionhrtpub.com/orms/orms-12-07/frqed.html
7.20 7.20: T
Tapscott Don and Williams Anthony D. ([2006] 2007): Wikinomics - how mass collaboration changes everything. Atlantic Books, Grove Atlantic Ltd (London, England, UK). ISBN 978-1-84354-636-8 [book].7.21 7.21: U
Ugur Emre (circa 2006): Literature survey on swarm robotics [online]. Accessed 7 January 2008. Available at: http://www.kovan.ceng.metu.edu.tr/~emre/literature/Literature_On_Swarm_Robotics.html7.22 7.22: V
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Wikipedia contributors (2007a). NP-hard [online]. Wikipedia, The Free Encyclopedia. 5 December 5, 2007, 18:15 UTC (accessed 8 January 2008). Available at: http://en.wikipedia.org/w/index.php?title=NP-hard&oldid=175967605Wikipedia contributors (2007b). Euclidean distance [online]. Wikipedia, The Free Encyclopedia. 21 December, 2007, 04:58 UTC (accessed 8 January 2008). Available at: http://en.wikipedia.org/w/index.php?title=Euclidean_distance&oldid=179335558
Wikipedia contributors (2007c). Convex function [online]. Wikipedia, The Free Encyclopedia. 22 December, 2007, 04:04 UTC (accessed 8 January 2008). Available at: http://en.wikipedia.org/w/index.php?title=Convex_function&oldid=179527993
Wikipedia contributors (2008a): Operations research [online]. Wikipedia, The Free Encyclopedia. 5 January, 2008, 22:52 UTC (accessed 7 January 2008). Available at: http://en.wikipedia.org/w/index.php?title=Operations_research&oldid=182398812
Wikipedia contributors (2008b): Algorithm [online]. Wikipedia, The Free Encyclopedia. 6 January, 2008, 20:29 UTC (accessed 7 January 2008). Available at: http://en.wikipedia.org/w/index.php?title=Algorithm&oldid=182591216
Wikipedia contributors (2008c): Swarm intelligence [online]. Wikipedia, The Free Encyclopedia. 2 January, 2008, 19:28 UTC (accessed 7 January 2007). Available at: http://en.wikipedia.org/w/index.php?title=Swarm_intelligence&oldid=181672566
Wikipedia contributors (2008d): Evolutionary computation [online]. Wikipedia, The Free Encyclopedia. 11 December, 2007, 18:35 UTC (accessed 7 January 2008). Available at: http://en.wikipedia.org/w/index.php?title=Evolutionary_computation&oldid=177257685
Wikipedia contributors (2008e). Natural computation [online]. Wikipedia, The Free Encyclopedia. 6 November, 2007, 18:58 UTC (accessed 7 January 2008). Available at: http://en.wikipedia.org/w/index.php?title=Natural_computation&oldid=169653086
Wikipedia contributors (2008f). Logistics [online]. Wikipedia, The Free Encyclopedia. 7 January, 2008, 20:45 UTC (accessed 8 January 2008). Available at: http://en.wikipedia.org/w/index.php?title=Logistics&oldid=182804196
Wikipedia contributors (2008g). Supply chain management [online]. Wikipedia, The Free Encyclopedia. 4 January, 2008, 02:13 UTC (accessed 8 January 2008). Available at: http://en.wikipedia.org/w/index.php?title=Supply_chain_management&oldid=182019665
Wikipedia contributors (2008h). P = NP problem [online]. Wikipedia, The Free Encyclopedia. 4 January, 2008, 08:23 UTC (accessed 8 January 2008). Available at: http://en.wikipedia.org/w/index.php?title=P_%3D_NP_problem&oldid=182065692
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Zimmer Carl (2007): From ants to people, an instinct to swarm [online]. The New York Times, 13 November 2007 (accessed 7 January 2008). Available at: http://www.nytimes.com/2007/11/13/science/13traff.html?_r=2&ex=1352696400&en=693ae1e813eb2a6b&ei=5088&partner=rssnyt&emc=rss&oref=slogin&oref=slogin8 8: Bibliography
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