SUMMARYThis paper reports the development of a novel optimization algorithm based on a colony of natural ants. The pheromone deposition of a group of foraging ants is perceived as a Gaussian profile and the ants are made to move by sampling the profile. The effectiveness of the algorithm is first verified on a set of benchmark functions and then extended to the design of a feedback controller for a buck converter. The proposed algorithm is illustrated through computed and measured results. Further, the new method is compared with Genetic Algorithm (GA) and is found to be on par with it.