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A particle swarm searching for the global minimum of a function. In computational science, particle swarm optimization (PSO) [1] is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.
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 ]
The remaining particles then move through the problem space following the lead of the optimum particles. At each time iteration, the particle swarm optimiser accelerates each particle toward its optimum locations according to simple mathematical rules. Particle swarm optimization has been applied in many areas.
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.
Crowd simulation is the process of simulating the movement (or dynamics) of a large number of entities or characters. [1] It is commonly used to create virtual scenes for visual media like films and video games, and is also used in crisis training, [2] architecture and urban planning, [3] and evacuation simulation.
Particle swarm optimization (PSO) is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity , as well as a communication channel between the particles.
2005 DARPA Grand Challenge winner Stanley performed SLAM as part of its autonomous driving system. A map generated by a SLAM Robot. Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.
Particle–Particle–Particle–Mesh (P 3 M) is a Fourier-based Ewald summation method [1] [2] to calculate potentials in N-body simulations. [3] [4] [5]The potential could be the electrostatic potential among N point charges i.e. molecular dynamics, the gravitational potential among N gas particles in e.g. smoothed particle hydrodynamics, or any other useful function.