Modified particle swarm optimization for odor source localization of multi-robot

Odor source localization is very important in real world applications. We studied the problem of odor source localization and presented a modified particle swarm optimization algorithm for odor source localization of multi robot. The algorithm dynamically adjusts two learning factors in the velocity update equation based on the effect of wind on self cognition and social cognition of a particle. In addition, an artificial potential field method is employed to improve the performance of our algorithm. We conducted various experiments in time-varying environments, and the experimental results confirm the superiority of our algorithm.