Definition
Particle Swarm Optimization (PSO) is a computational optimization method inspired by the coordinated movement of bird flocks.
It consists of particles acting as potential solutions, navigating through the problem space by considering their positions and the global best solution.
History of Particle Swarm Optimization
James Kennedy, a social psychologist, and Russell Eberhart, an electrical engineer, developed Particle Swarm Optimization (PSO) in 1995, inspired by the social behavior of birds and fish. While the technique aimed to simulate flock movement, it evolved into a powerful tool for solving complex optimization problems.
In PSO, each potential solution is modeled as a particle in a swarm, jointly moving toward the optimal solution. This technique was presented at the 1995 IEEE International Conference on Neural Networks, where it was initially evaluated alongside evolutionary methods like genetic algorithms.
Over the years, it has significantly evolved and adapted for various applications like network optimization and engineering design optimization.