Swarm intelligence

Any colony optimization (ACO) and swarm intelligence are forms of agent-based modeling inspired by colonies of social animals such as ants and bees [32]. ACO has become popular in engineering for optimal routing in water distribution systems [33, 34]. Particle swarm optimization has been successfully used to train ANNs, for instance, ANNs to predict river water levels [35], for parameter estimation, for example, in hydrology [36]. 

Swarm intelligence is a term that is applied to two rather different techniques — ant colony or pheromone trail optimization and particle swarm optimization. We deal here briefly with the latter. 

A couple of more recent texts provide gentle introductions to the subject. Evolutionary Computation by De Jong12 looks beyond the basic genetic algorithm, but does so at a studied pace, thus is suitable for those who do not yet have much experience in the field. Finally, Swarm Intelligence, by Kennedy and... 

Kennedy, J. and Eberhart, R.C., Swarm Intelligence, Morgan Kaufmann, San... 

Assembly of a TV Swarm intelligence City growth Protein folding Crystallization... 

Figure 5.19 Swarm intelligence in the case of migratory birds.

Self-organization systems under kinetic control (biological systems with genomic, enzymatic and/or evolutionary control), such as protein biosynthesis, virus assembly, formation of beehive and anthill, swarm intelligence. 

For a more detailed view of swarm intelligence, see http //www.swarm.org. 

The Seven Mysteries of Life by Guy Murchie Butterfly Economics A New General Theory of Social and Economic Behavior by Paul Omerod Paul Ormerod, Swarm Intelligence From Natural to Artificial Systems by Eric Bonabeau, Marco Dorigo Guy Theraulaz, Hidden Order How Adaptation Builds Complexity by John H. Holland Heather Mimnaugh, Turtles, Termites, and Traffic Jams by Mitchel Resnick The Evolution of Cooperation by Robert Axelrod. 

Millor, J., Halloy, J., Ame, J.-M. and Deneubourg, J.-L. (2006). Individual discrimination capability and collective choice in social insects. In Ant Colony Optimization and Swarm Intelligence, Lecture Notes in Computer Science. Berlin Springer, pp. 167-178. 

Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence from natural to artificial systems, Santa Fe Institute Studies on the Sciences of Complexity, Oxford University Press, New York... 

One body of exploratory computer science, now collectively known as Computational Swarm Intelligence, solves complex optimization problems by mimicking the flocking behavior of birds and the foraging behavior of ants (see Fundamentals of Computational Swarm Intelligence, by A. Engelbrecht, Hoboken, NJ John Wiley Sons, 2005). 

Korb O, StU tzle T, Exner TE. 2006. PLANTS application of ant colony optimization to structure-based drug design. In Proc. Ant Colony Optimization and Swarm Intelligence, 5th International Workshop, ANTS 2006 LNCS 4150. pp. 247-258. 

We apply swarm intelligence-based routing mechanisms that are well known to be useful in virtual environments as shown in [4,10,11]. Adapting them, crowd flow guidance in the real world through handheld devices such as smartphones is possible. 

Nowadays, smartphones are increasingly relevant in daily life among a very wide range of users. This remarkable growth results from some features such as portability, which traditional computers lack. This feature as well as relatively small size can be applied to solve old problems in new ways. In particular, there are two key features for the implementation of our framework (1) The smartphones wireless connectivity, which allows the deployment of a swarm intelligent system, and (2) built-in Global Positioning System (GPS) reception that provides location information. 

Ducatelle, F., Di Caro, G., GambardeUa, L. Principles and applications of swarm intelligence for adaptive routing in telecommunications networks. Swarm InteU. 4(3), 173-198 (2010)... 

Gajmel, S., Heiferhng, M. A distributed location service for MANET using swarm intelligence. In Proceedings on the IEEE Mobile WiMAX Symposium, pp. 220-225 (2009)... 

Ziane, S., Melouk, A. A swarm intelligent multi-path routing for multimedia traffic over mobile ad hoc networks. In Proceedings of the Q2SWinet, New York, pp. 55-62 (2005)... 

The meta-heuristic algorithm, namely FA, which idealizes some of the flashing characteristics of fireflies, has been recently developed by Xin-She Yang [30]. Although the FA has many similarities with other swarm intelligence based algorithms, it is indeed much simpler both in concept and implementation [32]. There are already several applications of FA to different optimization problems [2,5,15,20,22,31-33]. The authors reported that the FA is powerful and very efficient novel population-based method and can outperform other meta-heuristics, such as GA, in solving many optimization problems and particularly NP-hard problems. 

Training a neural network model essentially means selecting one model from the set of allowed models (or, in a Bayesian framework, determining a distribution over the set of allowed models) that minimizes the cost criterion. There are numerous algorithms available for training neural network models most of them can be viewed as a straightforward application of optimization theory and statistical estimation. Recent developments in this field use particle swarm optimization and other swarm intelligence techniques. 

Heinonen, J. Pettersson, F. (2007). Job-shop scheduling and visibility studies with a hybrid ACO algorithm. In , Swarm intelligence Focus on ant and particle swarm optimization, Chan, F. T. S. and Tiwari, M. K. (eds.), pp. 355 - 372, Itech Education and Publishing, Vienna, Austria. 

Baykasoglu, A. Ozbakir, L. Tapkan, P. (2007). Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem. In Swarm Intelligence Focus on Ant and Particle Swarm Optimization, F.T.S. Chan and M.K Tiwari (Eds.), Itech Education and Publishing, Vienna, Austria, pp. 113-144. 

Bonabeau, E. Dorigo, M. Theraulaz, G. (1999). Swarm Intelligence From Natural to Artificial Systems, Oxford University Press. 

Kelly, J.M. 1993. Earthquake-Resistance Design with Rubber. London Springer-Verlag. Kennedy, J. and R. C. Eberhart 2001. Swarm Intelligence. San Francisco Morgan Kaufmann. 

PSO is a swarm-intelligence-based, approximate, nondeterministic optimization technique. It is a robust stochastic technique based on the movement and intelligence of swarms. It applies the concept of social interaction to problem-solving (people.scs.carleton.ca). 

Kennedy, J., Eberhart, R. (2001). Swarm intelligence. San Francisco, CA Morgan Kaufmann Publishers. 

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