When investigating multi-optima problems, a particle swarm algorithm should not converge on single optima but ideally should explore many optima by continual searching. The common practice of only evaluating each particle's performance at discrete intervals can, at small computational cost, be used to adjust particle behaviour in situations where the swarm is 'settling' so as to encourage the swarm to explore further. An algorithm is proposed that, by making each wave of particles partially independent, is suitable for multi-optima problems.