Particle Swarm Optimization
• Particle Swarm Optimization (PSO) was introduced by Kennedy and Eberhart in the mid-1990s.
•It is a population-based stochastic approach which has been grouped under swarm intelligence (Kennedy, 2006; Engelbrecht, 2007; Parsopoulos & Vrahatis, 2007) and evolutionary computation (Trelea, 2003).
•PSO can be used to solve continuous and discrete problems.
•PSO was derived from a concept of a flock of birds which fly everywhere to find food.
•Each bird is illustrated as a particle.
•Each particle moves stochastically in search space for a feasible solution.
•Each of the particles has its own velocity and position.
PSO is a robust stochastic optimization technique based on the movement and intelligence of swarms.
ReplyDelete- It uses a number of agents (particles) that constitute a swarm moving around in the search space looking for the best solution.
- Each particle is treated as a point in a N-dimensional space which adjusts its “flying” according to its own flying experience as well as the flying experience of other particles.