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Particle swarm optimization (PSO) is a population-based stochastic method that helps with optimization problems. It is modeled after natural processes, such as the flocking of birds or the movement of schools of fish.
Particle swarm optimization works with a set of feasible solutions and constraints on an optimization problem. The optimization problem has to have a target condition – then the algorithm works to solve the problem and provide the best values.
Particle swarm optimization was developed in 1995 by Russell Eberhard and James Kennedy. These researchers started out looking at computer simulations of bird flocking, and then worked to perfect the algorithm based on this research. Now, particle swarm optimization can help engineers to solve all sorts of machine learning problems, based on the idea that monitoring the disparate “particles,” or, for example, parts of a peer to peer network, will deliver actionable insights.