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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.
In the first work on DFO, this algorithm was compared against a few other existing swarm intelligence techniques using error, efficiency and diversity measures. It is shown that despite the simplicity of the algorithm, which only uses agents’ position vectors at time t to generate the position vectors for time t + 1, it exhibits a competitive ...
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:
The ant colony optimization algorithm is a probabilistic technique for solving computational problems that can be reduced to finding good paths through graphs.Initially proposed by Marco Dorigo in 1992 in his PhD thesis, [1] [2] the first algorithm aimed to search for an optimal path in a graph based on the behavior of ants seeking a path between their colony and a source of food.
Natural computing, [1] [2] also called natural computation, is a terminology introduced to encompass three classes of methods: 1) those that take inspiration from nature for the development of novel problem-solving techniques; 2) those that are based on the use of computers to synthesize natural phenomena; and 3) those that employ natural materials (e.g., molecules) to compute.
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 ...
Learning classifier system; Memetic algorithms; Neuroevolution; Particle swarm optimization; Beetle antennae search; Self-organization such as self-organizing maps, competitive learning; Swarm intelligence; A thorough catalogue with many other recently proposed algorithms has been published in the Evolutionary Computation Bestiary. [11]
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.