This paper presents a framework for integrating intrinsic motivation with particle swarm optimisation. Intrinsically motivated particle swarm optimisation can be used for adaptive task allocation when the nature of the target task is not well understood in advance, or can change over time. We first present a general framework in which a computational model of motivation generates a dynamic fitness function to focus the attention of the particle swarm. We then discuss two approaches to modelling motivation in this framework: a computational model of curiosity using an unsupervised neural network and a model of novelty based on background subtraction. We introduce metrics for evaluating intrinsically motivated particle swarm optimisation and test our algorithm as an approach to task allocation in a workplace hazard mitigation scenario. We found that both proposed motivation techniques work well for generating a fitness function that can locate hazards, without requiring a precise definition of a hazard. We found that particle swarm optimisation can converge on optima in our generated fitness landscape in some, but not all, of our simulations.