A simple strategy to maintain diversity and reduce crowding in particle swarm optimization
Stephen Y. Chen, James Montgomery · 2011
Each particle of a swarm maintains its current location and its personal best location. It is useful to think of these personal best locations as a population of attractors. When this population of attractors converges, the explorative capacity of the swarm is reduced. The convergence of attractors can occur quickly since the personal best of a particle is broadcast to its neighbours. If a neighbouring particle comes close to this broadcasting attractor, it may update its own personal best to be near the broadcasting attractor. This convergence of attractors can be reduced by having particles update the broadcasting attractor rather than their own attractor/personal best. Through this simple change which incurs minimal computational costs, large performance improvements can be achieved in multi-modal search spaces.