In this paper we present a new definition of neighborhood for particle swarm optimization (PSO) methods called area of influence. Area of influence (AOI) derives from the observation that in nature the effective exchange of information between individuals of a society deteriorates as their physical distance increases. In PSO with AOI, the loss of information exchange ability with distance is simulated by making the exchange of information a function of the physical distance between particles in hyperspace. In this paper, we compare the AOI method to the standard PSO neighborhood methods, global best, local best, and von Neumann. We also introduce a local search method using reinitialization of velocity components based on the current search range. We show that AOI along with local search and a time-varying constriction coefficient provides strong benefits to several PSO algorithms. Results are presented using the standard benchmark functions from the PSO literature.