Scalability of particle swarm algorithms
Sébastien Piccand, Michael O’Neill, Jacqueline Walker · 2007
When dealing with complex optimisations problems, evolutionary computation techniques have proven to be very helpful. Amongst optimisation algorithms driven by evolutionary computation techniques, particle swarm algorithms have proven to be a very good alternative to genetic algorithms because of their faster convergence. However they can still suffer from premature convergence to local optima. Premature convergence occurs when the particles of the swarm are too close to each other to enable further exploration of the space. To put it another way, the dispersion or distribution of the swarm throughout the search space has been localised to a small region with a consequent stagnation of the search process. Many strategies have been used to try to prevent convergence to a local optimum. However little work