Search results
Results From The WOW.Com Content Network
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 Intelligence : From Natural to Artificial Systems with Eric Bonabeau and Guy Theraulaz, Oxford University Press, 1999 (ISBN 0-19-513159-2). Robot Shaping with Marco Colombetti, MIT Press, 1998 (ISBN 0-262-04164-2). Ant algorithms for discrete optimization with Gianni Di Caro and Luca Maria Gambardella, Artificial Life, Vol. 5, N. 2, 1999.
A further key concept in the field of swarm intelligence is stigmergy. [21] [22] Stigmergy is a mechanism of indirect coordination between agents or actions. The principle is that the trace left in the environment by an action stimulates the performance of a next action, by the same or a different agent.
This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis, [ 6 ] [ 7 ] the first algorithm was aiming to search for an optimal path in a graph, based on the behavior of ants seeking a path ...
The design of swarm robotics systems is guided by swarm intelligence principles, which promote fault tolerance, scalability, and flexibility. [1] Unlike distributed robotic systems in general, swarm robotics emphasizes a large number of robots. While various formulations of swarm intelligence principles exist, one widely recognized set includes:
Gerardo Beni (born Florence, Italy 21 February 1946) is a professor of electrical engineering at University of California, Riverside who, with Jing Wang, is known as the originator of the term swarm intelligence [1] [2] in the context of cellular robotics and the concept of electrowetting, [3] with Susan Hackwood.
Dispersive flies optimisation (DFO) is a bare-bones swarm intelligence algorithm which is inspired by the swarming behaviour of flies hovering over food sources. [1] DFO is a simple optimiser which works by iteratively trying to improve a candidate solution with regard to a numerical measure that is calculated by a fitness function .
The Fireworks Algorithm (FWA) is a swarm intelligence algorithm that explores a very large solution space by choosing a set of random points confined by some distance metric in the hopes that one or more of them will yield promising results, allowing for a more concentrated search nearby.