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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.
Particle swarm optimization (PSO) A swarm intelligence method. Intelligent water drops (IWD) A swarm-based optimization algorithm based on natural water drops flowing in rivers Gravitational search algorithm (GSA) A swarm intelligence method. Ant colony clustering method (ACCM) A method that make use of clustering approach, extending the ACO.
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 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.
Stochastic diffusion search (SDS) was first described in 1989 as a population-based, pattern-matching algorithm. [1] It belongs to a family of swarm intelligence and naturally inspired search and optimisation algorithms which includes ant colony optimization, particle swarm optimization and genetic algorithms; as such SDS was the first Swarm Intelligence metaheuristic.
Selection of the best continues until certain stop criteria are met. This essentially uses a frequency-tuning technique to control the dynamic behaviour of a swarm of bats, and the balance between exploration and exploitation can be controlled by tuning algorithm-dependent parameters in bat algorithm.
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]