Discrete evaluation and the particle swarm algorithm

We propose that the optimal performance of the PSO algorithm should differ from that of the real life creatures on which PSO is modelled. If a bird finds a good food source, the likely behaviour for a flock is to congregate there, settle and feed. However, once PSO has found an optimum, while some particles should explore in the immediate vicinity for any better optimum present, the rest of the swarm should set out to explore new areas. The common PSO practice of only evaluating each particle’s performance at discrete intervals can, at small computational cost, be used to automatically adjust the PSO behaviour in situations where the swarm is ‘settling’ so as to encourage part of the swarm to explore further.