Particle swarm optimization (PSO) is a population-based, heuristic minimization technique that is based on social behavior. The method has been shown to perform well on a variety of problems including those with nonconvex, nonsmooth objective functions with multiple local minima. However, the method can be computationally expensive since many function calls are required to advance the swarm at each optimization iteration. This is a significant drawback when function evaluations depend on output from an off-the-shelf simulation program, which is often the case in engineering applications. To this end, we propose a hybrid algorithm incorporating low fidelity surrogate functions that serve as a more efficient information sharing medium. Numerical results are given that show the hybrid approach can improve algorithmic efficiency in a number of test problems including a difficult hydraulic capture problem.