A novel particle swarm optimization(PSO) algorithm, ideal free distribution(IFD) PSO, is proposed based on the
analysis of IFD model, in which, three non-overlapping personal best positions of the particles are selected, and their fitness values are regarded as food quality of resource patch. Particles are randomly assigned to each resource patch according to ideal free distribution model. Particles in each sub-population search the optima independently in accordance with standard PSO algorithm. In order to guarantee the diversity of the whole population, the best position of each sub-population is set to keep a distance, which linearly decreases with iterations. After a certain number of iterations, all sub-population particles are regrouped. The experimental results of benchmark functions show the effectiveness of IFDPSO algorithm.