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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 ...
This code would run quite acceptably on a Cray Y-MP (built in the early 1980s), which can sustain 0.8 multiply–adds per memory operation to main memory. A machine like a 2.8 GHz Pentium 4, built in 2003, has slightly less memory bandwidth and vastly better floating point, so that it can sustain 16.5 multiply–adds per memory operation.
Examples of swarm intelligence in natural systems include ant colonies, bee colonies, bird flocking, hawks hunting, animal herding, bacterial growth, fish schooling and microbial intelligence. The application of swarm principles to robots is called swarm robotics while swarm intelligence refers to the more general set of algorithms.
Multi-swarm optimization is a variant of particle swarm optimization (PSO) based on the use of multiple sub-swarms instead of one (standard) swarm. The general approach in multi-swarm optimization is that each sub-swarm focuses on a specific region while a specific diversification method decides where and when to launch the sub-swarms.
For a pair of types K, V, the type map[K]V is the type mapping type-K keys to type-V values, though Go Programming Language specification does not give any performance guarantees or implementation requirements for map types. Hash tables are built into the language, with special syntax and built-in functions.
The Boids model can be used for direct control and stabilization of teams of simple unmanned ground vehicles (UGV) [6] or micro aerial vehicles (MAV) [7] in swarm robotics. For stabilization of heterogeneous UAV-UGV teams, the model was adapted for using onboard relative localization by Saska et al. [ 8 ]
In computer science and operations research, the bees algorithm is a population-based search algorithm which was developed by Pham, Ghanbarzadeh et al. in 2005. [1] It mimics the food foraging behaviour of honey bee colonies.
In line with recent work in swarm intelligence research involving optimization algorithms inspired by the behavior of social insects (including bees, ants and termites), and vertebrates such as fish and birds, there has recently been research on using bee waggle dance behavior for efficient fault-tolerant routing. [34]