Partikkelswarm

Partikkelswarm, also known as particle swarm optimization (PSO), is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It is inspired by social behavior of bird flocking or fish schooling. In PSO, a population of candidate solutions, referred to as particles, is initialized with random positions and velocities. Each particle represents a potential solution to the optimization problem. The particles then move through the solution space, with their positions and velocities updated according to simple mathematical formulas over time. The movement of each particle is influenced by its local best known position, but is also guided toward the best known positions in the search space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions. PSO is widely used in various fields such as engineering, economics, and computer science for solving optimization problems. It is particularly useful for problems that are difficult to solve using traditional optimization methods, such as those with non-linear, non-differentiable, or multi-modal objective functions. The simplicity and efficiency of PSO make it a popular choice for many optimization tasks.