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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.
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 ...
However, the analyses were criticized by Pedersen [22] for being oversimplified as they assume the swarm has only one particle, that it does not use stochastic variables and that the points of attraction, that is, the particle's best known position p and the swarm's best known position g, remain constant throughout the optimization process.
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]
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 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:
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.
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.