Odor Source Localization by Concatenating Particle Swarm Optimization and Grey Wolf Optimizer

A concatenated approach which utilizes the strength of Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) is proposed for odor source localization by a team of mobile robots. Odor plume is modeled by using the Gaussian distribution. Robots continue random search within the workspace to locate the plume. When one of the robot enters in the vicinity of plume, robot's new positions are calculated by applying concatenation of PSO first then Grey Wolf Optimizer second and vice versa. In order to prevent getting stuck at local minima, concept of search counter is used. Proposed approach is compared with Refined Hybrid PSO and the simulation result shows the validity of the proposed approach over the other.