Partikkelinoptimointi

Partikkelinoptimointi, also known as particle swarm optimization (PSO), is a computational intelligence technique used to solve optimization problems. It is a metaheuristic inspired by the social behavior of bird flocking or fish schooling. In PSO, a population of candidate solutions, called particles, is maintained within a search space. Each particle moves around the search space, influenced by its own best-known position and the best-known position found by any particle in the swarm.

The core idea is that each particle adjusts its trajectory based on its past experience and the experience of its neighbors. The position of each particle represents a potential solution to the optimization problem, and its velocity determines how it moves through the search space. The algorithm iteratively updates the position and velocity of each particle. The velocity is influenced by three components: inertia, the particle's own best position (pbest), and the global best position found by the swarm (gbest). The inertia component allows the particle to continue in its current direction, while the pbest and gbest components pull the particle towards better solutions.

PSO is an unconstrained optimization technique, meaning it does not require the objective function to be differentiable. It is widely applied in various fields, including engineering design, machine learning, and data analysis, for tasks such as function optimization, parameter tuning, and feature selection. Its simplicity and effectiveness have made it a popular choice for solving complex optimization challenges.