1. An object recognition system incorporating swarming domain classifiers, comprising: a processor that includes a plurality of software agents configured to operate as a cooperative swarm to classify an object in a domain, where each agent is assigned an initial velocity vector to explore a solution space for object solutions, and where each agent is a complete classifier which determines a classification value representing a classification confidence that the solution space in the domain includes the object, and where the classification value for each agent is independent of other agents, and where each agent is configured to perform at least one iteration, the iteration being a search in the solution space for a potential solution optima where each agent keeps track of its coordinates in multi-dimensional space that are associated with an observed best solution (pbest) that the agent has identified, and a global best solution (gbest) where the gbest is used to store the best location among all agents, with each velocity vector thereafter changing towards pbest and gbest, allowing the cooperative swarm to concentrate on the vicinity of the object and classify the object when the classification value exceeds a preset threshold; wherein each velocity vector changes towards pbest and gbest in a probabilistic way; and wherein the plurality of agents constitutes a possibilistic particle swarm optimization module for finding multiple objects in the domain, the possibilistic particle swarm optimization module configured to update agent position and velocity using a possibilistic update technique when the number of iterations is greater than a preset value, agent velocity in the possibilistic particle swarm optimization module is calculated according to the following: v i ⁡ ( t ) = w ⁢ ⁢ v i ⁡ ( t - 1 ) + c 1 * rand ( ) * ( pbest - x i ⁡ ( t - 1 ) ) + c 2 * rand ( ) * ∑ j = 1 C ⁢ μ ij ⁡ ( gbest j - x i ⁡ ( t - 1 ) ) , where μ ij = η j η j + d ij 2 , and η j is a dynamic elastic constraint around the jth minima, and d ij 2 is the distance of an ith agent to a jth minima, and where initially η j is unbounded and it is slowly decreased to a small region around the local minima, and where v i (t) is the velocity vector at time t of the ith agent and c 1 and c 2 are parameters that weight the influence of their respective terms in the possibilistic velocity update technique, where w is a decay constant, and where the rand( ) function generates a random number.