26. A method for object recognition incorporating swarming domain classifiers as set forth in claim 25 , wherein in the act of updating the agent positions and velocity according to a possibilistic update technique, the possibilistic update technique 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 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, and gbest is used to store a best location among all agents, and pbest is an observed best solution that the agent has identified.
26. A method for object recognition incorporating swarming domain classifiers as set forth in claim 25 , wherein in the act of updating the agent positions and velocity according to a possibilistic update technique, the possibilistic update technique 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 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, and gbest is used to store a best location among all agents, and pbest is an observed best solution that the agent has identified. 27. A method for object recognition incorporating swarming domain classifiers as set forth in claim 26 , wherein in the act of updating the agent position and velocity according to a velocity update technique, the velocity update technique is calculated according to the following; 
 v i ( t )= wv i ( t− 1)+ c 1 *r and( )*( p best− x i ( t− 1))+ c 2 *r and( )*( g best− x i ( t− 1)) x i ( t )= x i ( t− 1)+ v i ( t ), where x i (t)and V i (t)are the position and velocity vectors 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 velocity update technique, where w is a decay constant, and where the rand( ) function generates a random number.