Particle Swarm Optimization Algorithm

Among swarm intelligence algorithms, one particularly practical program code is known as Particle Swarm Optimization (PSO). This algorithm draws inspiration from bird flock foraging behavior, simulating information exchange and cooperation among individuals within a flock to find optimal solutions. In this process, each individual is considered a particle that adjusts its position and velocity based on its current state and surrounding information to locate the optimal solution. The algorithm typically implements two key components: personal best (pbest) tracking each particle's historical optimum and global best (gbest) representing the swarm's overall best solution. Due to its simplicity and effective optimization performance, PSO has been widely applied across various domains including engineering optimization, data mining, and machine learning. A basic PSO implementation involves iterative updates of particle velocity using the formula v = w*v + c1*rand()*(pbest - present) + c2*rand()*(gbest - present), followed by position updates. Therefore, when conducting research and applications in swarm intelligence algorithms, the significance of Particle Swarm Optimization cannot be overlooked.