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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:
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.
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]
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 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.
In the ABC algorithm, the first half of the swarm consists of employed bees, and the second half constitutes the onlooker bees. The number of employed bees or the onlooker bees is equal to the number of solutions in the swarm. The ABC generates a randomly distributed initial population of SN solutions (food sources), where SN denotes the swarm ...