The Particle Swarm Optimization (PSO) algorithm updates each individual’s velocity and position using its own prior best position and the best position found so far by any particle. Effective search for global optima can benefit if each particle may utilize additional historical information regarding the quality of previously visited positions in its proximity. We present an approach for doing so, using an archive containing some prior particle positions, analogous to the archive used in some multi-objective optimization algorithms.