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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.
The book by Kennedy and Eberhart [5] describes many philosophical aspects of PSO and swarm intelligence. An extensive survey of PSO applications is made by Poli . [ 6 ] [ 7 ] In 2017, a comprehensive review on theoretical and experimental works on PSO has been published by Bonyadi and Michalewicz.
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:
DFO [2] was introduced with the intention of analysing a simplified swarm intelligence algorithm with the fewest tunable parameters and components. In the first work on DFO, this algorithm was compared against a few other existing swarm intelligence techniques using error, efficiency and diversity measures.
The bacterial foraging algorithm (BFA) is a biologically inspired swarm intelligence optimization approach that mimics bacteria's foraging activity to gather the most energy available throughout the search phase. Since its introduction in 2002, it has garnered widespread interest from scholars.
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.
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]