Particle Swarm Optimization
Particle Swarm Optimization (PSO) is a computational intelligence technique inspired by the social behavior of bird flocking or fish schooling, used for solving optimization problems. It works by simulating a population of candidate solutions, called particles, that move through the search space based on their own experience and the experience of neighboring particles. PSO is particularly effective for continuous, non-linear, and multi-modal optimization tasks where traditional gradient-based methods may struggle.
Developers should learn PSO when working on complex optimization problems in fields like machine learning, engineering design, or financial modeling, where finding global optima in high-dimensional spaces is critical. It is especially useful for parameter tuning in neural networks, feature selection, and scheduling problems, as it often converges faster than genetic algorithms and requires fewer parameters to configure. PSO is also valuable in real-time applications due to its simplicity and low computational overhead.